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NOM DU FICHIER / FILE NAME:
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CA 03177985 2022-09-27
WO 2022/133330 PCT/US2021/064233
ROBOT FLEET MANAGEMENT AND ADDITIVE MANUFACTURING FOR
VALUE CHAIN NETWORKS
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional Patent App. No.
63/127,983,
filed December 18, 2020 and U.S. Provisional Patent App. No. 63/185,348, filed
May 6, 2021.
This application claims priority to India Patent App. No. IN202111029964,
filed July 3, 2021 and
India Patent App. No. IN202111036187, filed August 10, 2021. The entire
disclosures of the
above applications are incorporated by reference.
FIELD
.. [0002] The present disclosure relates to information technology methods and
systems for
management of value chain network entities, including supply chain and demand
management
entities. The present disclosure also relates to the field of enterprise
management platforms, more
particularly involving data management, artificial intelligence, network
connectivity and digital
twins, additive manufacturing, robotics-as-a-service, and energy management.
BACKGROUND
[0003] Historically, many of the various categories of goods purchased and
used by household
consumers, by businesses and by other customers were been supplied mainly
through a relatively
linear fashion, in which manufacturers and other suppliers of finished goods,
components, and
other items handed off items to shipping companies, freight forwarders and the
like, who
.. delivered them to warehouses for temporary storage, to retailers, where
customers purchased
them, or directly to customer locations. Manufacturers and retailers undertook
various sales and
marketing activities to encourage and meet demand by customers, including
designing products,
positioning them on shelves and in advertising, setting prices, and the like.
[0004] Orders for products were fulfilled by manufacturers through a supply
chain, such as
depicted in Fig. 1, where suppliers 122 in various supply environments 160,
operating production
facilities 134 or acting as resellers or distributors for others, made a
product 130 available at a
point of origin 102 in response to an order. The product 130 was passed
through the supply
chain, being conveyed and stored via various hauling facilities 138 and
distribution facilities 134,
such as warehouses 132, fulfillment centers 112 and delivery systems 114, such
as trucks and
other vehicles, trains, and the like. In many cases, maritime facilities and
infrastructure, such as
ships, barges, docks and ports provided transport over waterways between the
points of origin
102 and one or more destinations 104.
[0005] Organizations have access to an almost unlimited amount of data. With
the advent of
smart connected devices, wearable technologies, the Internet of Things (IoT),
and the like, the
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amount of data available to an organization that is planning, overseeing,
managing and operating
a value chain network has increased dramatically and will likely to continue
to do so. For
example, in a manufacturing facility, warehouse, campus, or other operating
environment, there
may be hundreds to thousands of IoT sensors that provide metrics such as
vibration data that
measure the vibration signatures of important machinery, temperatures
throughout the facility,
motion sensors that can track throughput, asset tracking sensors and beacons
to locate items,
cameras and optical sensors, chemical and biological sensors, and many others.
Additionally, as
wearable technologies become more prevalent, wearables may provide insight
into the
movement, health indicators, physiological states, activity states, movements,
and other
characteristics of workers. Furthermore, as organizations implement CRM
systems, ERP
systems, operations systems, information technology systems, advanced
analytics and other
systems that leverage information and information technology, organizations
have access to an
increasingly wide array of other large data sets, such as marketing data,
sales data, operational
data, information technology data, performance data, customer data, financial
data, market data,
pricing data, supply chain data, and the like, including data sets generated
by or for the
organization and third-party data sets.
[0006] The presence of more data and data of new types offers many
opportunities for
organizations to achieve competitive advantages; however, it also presents
problems, such as of
complexity and volume, such that users can be overwhelmed, missing
opportunities for insight. A
need exists for methods and systems that allow enterprises not only to obtain
data, but to convert
the data into insights and to translate the insights into well-informed
decisions and timely
execution of efficient operations.
ADDITIVE MANUFACTURING
[0007] Additive manufacturing, encompassing technologies like 3D printing,
vapor deposition,
polymer (or other material) coating, epitaxial and/or crystalline growth
approaches, and others,
alone or in combination with other technologies, such as subtractive or
assembly technologies,
enables manufacturing of a three-dimensional product from a design via a
process of forming
successive layers of the product, with optional interim or subsequent steps to
arrive at a finished
component or system. The design may be in the form of a data source like an
electronic 3D
model created with a computer aided design package or via 3D scanner. The 3D
printing or other
additive process then involves forming a first material-layer and then adding
successive material
layers wherein each new material-layer is added on a pre-formed material-
layer, until the entire
designed three-dimensional product is completed. References to 3D printing or
other particular
additive manufacturing technologies throughout this disclosure should be
understood to
encompass alternative embodiments involving other additive manufacturing
technologies, except
where context specifically indicates otherwise.
[0008] A large number of additive processes are currently available. They may
differ in the
manner successive layers are deposited to create the 3D product. They may also
differ in terms of
materials that are used to form the product. Metals (such term including
alloys except where
context specifically indicates otherwise and including specialized metals such
as shape memory
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materials) are increasingly popular 3D printing materials. Common ones include
Titanium,
Stainless steel, Aluminum, Tool Steel, Inconel and Cobalt Chrome. Some methods
melt or soften
metal to produce the layers. Examples of metal 3D printing methods include
selective laser
melting (SLM), selective laser sintering (SLS), direct metal laser sintering
(DMLS) and/or fused
deposition modeling (FDM). Other methods include: (a) metal extrusion where a
filament or rod
consisting of polymer and heavily loaded with metal powder is extruded through
a nozzle (like in
FDM) to form the "green" part that is post-processed (debinded and sintered)
to create a fully-
metal part; (b) metal binder jetting that uses print-heads to apply a liquid
binding agent onto
layers of powder and (c) nanoparticle jetting that uses jetting of metal
nanoparticles from inkjet
nozzles in super-thin layers.
[0009] Regardless of the design data sources or the methods employed for
additive
manufacturing, including metal 3D printing, the entire process from design and
manufacturing to
end customer delivery, remains prone to inefficiency, process variations,
product inconsistency,
and unreliability. This can result in a final 3D printed product that does not
meet customer
expectations and/or product specifications, and it can lead to low quality 3D
printed products or
components that result in failures, among other problems. These issues can
also increase
operating costs for 3D printing service providers through material waste,
reduced throughput due
to machine downtime and/or unproductive print hours, and associated supply
chain risks and
inefficiencies. For example, it is common for 3D printed products to get
deformed during or after
the manufacture due to printing procedures and non-optimized printing
parameters. Also,
common issues that can arise due to inefficient manufacturing supply chains
include fraud,
delayed deliveries, contractual liability, and product recalls.
[0010] To ensure that the final metal 3D printed product matches the customer
expectations and
producer specifications around quality, cost and turnaround time, a need
exists for smarter
product design, manufacturing, supply chain and demand management methods and
systems. A
further need exists for methods and systems that allow for improved
monitoring, management,
and optimization of additive manufacturing capabilities by and for various
interested parties.
[0011] Conventional machine vision systems are made of a combination of
optics, lighting,
sensors and software and aim to replicate the function of human eye. Such
systems create an
image of an object by capturing and analyzing the reflected light from the
object. An optical lens
captures the image and presents it to an image sensor such as a charge coupled
device (CCD) or
complementary metal oxide semiconductor (CMOS) device. Such devices contain
matrices or
linear arrays of small, accurately spaced photo sensitive elements fabricated
on silicon chips
using integrated circuit technology. The sensor device converts the light
falling on it, through the
camera lens, into analog electrical signal corresponding to light intensity.
The object image is
thus broken down into an array of individual picture elements or pixels. An
analog to digital
converter is used to convert analog voltage of element into digital value. If
voltage level for each
pixel is given either 0 or 1 value depending on some threshold value, it is
called binary system.
On the other hand, a gray scale system assigns up to 256 different values
depending on intensity
to each pixel. Thus, in addition to black and white, many different shades of
gray can be
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distinguished. A gray-scale image may be seen to have one channel, represented
by a 2-D matrix
of pixels having pixel values in the range of, for example, 0 to 255. A color
image on the other
hand represents the brightness and color of the pixels in an image by the
three primary color
values: R (red), G (green), and B (blue). Thus, color images have red, green,
and blue (RGB)
channels each representing RGB components of the image. This raw data captured
by the image
sensor is then sent to an image processing system for analysis. The image
processing system then
processes the raw data to extract useful information to analyze the image and
make decisions on
such analysis. The image processing system may include a pre-processing
function to enhance
the image quality. For example, such processing may involve image scaling,
noise reduction,
color adjustment, brightness adjustment, white balance adjustment, sharpness,
adjustment,
contrast adjustment and the like. Further the image may be analyzed using
machine learning or
other algorithms to identify one or more objects in the image and determine
the position and
orientation of such objects.
[0012] While vision technology has improved significantly in the past few
years, most of the
improvements relate to processing of the image data captured by vision sensors
and may be
attributed to the use of big data, sophisticated machine learning algorithms
like convolutional
neural networks (CNNs) and graphical processing units (GPUs) for processing of
the image data.
The conventional vision technology however, has significant limitations
specifically with respect
to capturing of the raw data about an object or a scene. For example, the
optical lenses in
conventional vision systems attempt to extract information in a linear and
obtuse way by relying
on simple focusing techniques. The attempt to get an object into focus on an
image ends up
losing a large amount of information and other optical properties. A need
exists for capturing
previously lost or inferred information so as to generate an image that is not
"perfect" to the eye,
but rich to algorithms. A further need exists for a richer object recognition
complex vision
application where conventional vision technologies have proved inadequate
including
recognizing objects in dynamic environments like when the object or vision
system are moving,
as in a mobile, robotic usage example; recognizing three dimensional (3D)
objects by capturing
depth data; predicting object attributes like depth, orientation, and motion;
recognizing tiny
objects; recognizing facial features; recognizing objects in a power
constrained or network
constrained environment, and other use cases for which traditional machine
vision systems and
methods are poorly suited.
[0013] Furthermore, automation is revolutionizing value chains for almost all
categories of
items, and robotics is at the heart of the revolution. While physical robots
have played an ever-
expanding role in manufacturing for years, typical implementations have
historically focused on
fixed location robots completing prescribed tasks in pre-defined arrangements,
such as painting,
welding, and so forth in an assembly line. These limited roles produced and
continue to produce
significant improvements in quality, cost, and productivity, but do not take
full advantage of
emerging technologies in engineering, materials science, software process
automation, artificial
intelligence, additive manufacturing, data-driven analytics, digital twins,
blockchains, smart
contracts, and the like. These technologies can be integrated with
developments in robotics
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(including hardware and software robotics) to produce an innovative array of
highly functional
autonomous robots with interactive capabilities. Emerging and future robot
classes and
capabilities provide opportunity for ever-expanding robot use cases and
management platforms
that can automatically configure, organize, deploy, and control robots and
robot fleets to securely
deliver reliable services, including contracted services that access robotic
fleet capabilities in
"robotics-as-a-service" platforms, among others.
SUMMARY
[0014] A robot fleet management platform includes a set of datastores that
store a governance
library that defines a set of governance standards that include at least one
set of security
standards, legal standards, ethical standards, regulatory standards, quality
standards, or
engineering standards that are applied to decisions made by one or more
respective intelligence
services. The robot fleet management platform includes a set of one or more
processors that
execute a set of computer-readable instructions. The set of one or more
processors collectively
execute a governance-enabling intelligence layer that receives and responds to
intelligence
requests received from respective intelligence service clients. The
intelligence layer includes a
set of artificial intelligence services that includes at least one of a
machine learning service, a
rules-based intelligence service, a digital twin service, a robot process
automation service, or a
machine vision service. The intelligence layer includes an intelligence layer
controller that
coordinates performance of respective intelligence services on behalf of the
respective
intelligence service clients and performance of a set of analyses
corresponding to the respective
intelligence services based in part on the set of governance standards. The
intelligence layer
returns decisions determined collectively by the artificial intelligence
service in response to the
intelligence requests, such that the decisions are determined based on a set
of intelligence service
data sources and the set of analyses.
[0015] In other features, the intelligence layer controller is configured to
receive an intelligence
request from an intelligence service client indicating a requested decision,
determine zero or
more governance standards that are implicated by a type of the requested
decision, determine
zero or more pre-defined analyses that are implicated by the type of the
requested decision or by
a governance standard implicated by the type of decision, and provide the zero
or more
governance standards and the zero or more pre-defined analyses to the
artificial intelligence
service. The zero or more governance standards are selected from the
governance standards
defined in the governance library.
[0016] In other features, the intelligence layer controller is further
configured to iteratively
determine and provide additional governance standards and pre-defined analyses
to the artificial
intelligence service in response to determinations made by the artificial
intelligence service until
the requested decision is provided by the artificial intelligence service. In
other features, the
intelligence layer is further configured to determine the set of intelligence
layer data sources
based on the type of the requested decision. In other features, a requesting
intelligence service
client provides the set of intelligence layer data sources with the request.
In other features, the
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decisions provided by the intelligence layer define respective actions to be
taken by the
respective intelligence service clients. In other features, the respective
actions include an action
to request human intervention.
[0017] In other features, the respective actions include non-adaptive pre-
defined actions. In
.. other features, the respective actions include domain-specific actions that
are responsive to the
respective requests. In other features, the intelligence service clients
include a security system
that requests classifications of potential security risks. In other features,
the intelligence service
clients include a resource provisioning system that requests recommendations
for resources to
support a robot fleet. In other features, the intelligence service clients
include a logistics system
that requests logistics-based recommendations with respect to one or more
robot fleets. In other
features, the intelligence service clients include a job configuration system
that requests proposed
job configurations given a job request. In other features, the intelligence
service clients include a
fleet configuration system that requests proposed fleet configurations given a
set of tasks to be
completed by a robot fleet. In other features, the intelligence service
clients include a robot
operating unit deployed by the robot fleet management platform.
[0018] A robot fleet management platform for configuring robot fleet resources
includes a set
of one or more processors that execute a set of computer-readable
instructions. The set of one or
more processors collectively execute a job parsing system that applies a set
of filters to job
content received in association with a job request to identify portions
thereof suitable for robot
automation. A task definition system establishes a set of robot tasks that
each define at least a
type of robot and a task objective, the set of robot tasks being based at
least in part on the
portions of the job request that are suitable for robot automation and meet a
first fleet objective of
a set of fleet objectives. A fleet configuration proxy service processes the
set of robot tasks and
additional job content relating to the job request to produce a fleet resource
configuration data
structure for the job request that defines a set of task associations and a
set of robot adaptation
instructions. Each task association associates at least one robot operating
unit to a respective
robot task of the set of robot tasks and the set of robot adaptation
instructions define a manner by
which one or more robot operating units of a robot fleet are to be adapted to
perform respective
tasks to which robots are assigned. A fleet intelligence layer activates a set
of intelligence
services to produce at least one recommended robot task and associated
contextual information
that facilitates robot selection and task ordering in a workflow of robot
tasks. A job workflow
system generates a workflow that defines an order of performance of the robot
tasks based on the
fleet resource configuration data structure and the set of robot tasks. A
workflow simulation
system is configured to simulate performance of the job based on the workflow
and a job
execution simulation environment. The workflow simulation system applies the
workflow in the
job execution simulation environment that includes digital models of the robot
operating units
assigned to the robot fleet and digital models of the task definitions to
produce a simulation
result, such that the simulation result is used to iteratively redefine one or
more of the set of
tasks, the fleet resource configuration data structure, or the workflow until
the simulation result
satisfies a second fleet objective of the set of fleet objectives
corresponding to the job request. A
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job execution plan generator, in response to the simulation result satisfying
the set of fleet
objectives, generates a job execution plan based on the set of tasks, the
fleet resource
configuration data structure, and the workflow.
[0019] In other features, the task definition system interacts with the
intelligence layer to
suggest alternate tasks that meet a second fleet objective. In other features,
the task definition
system interacts with the intelligence layer to optimize at least one of a
robot type and a task
objective based on the first fleet objective. In other features, the first
fleet objective includes fleet
resource utilization criteria. In other features, the task definition system
receives from the fleet
configuration proxy service a particular robot type for use when performing
the robot task. In
other features, the task definition system configures the set of robot tasks
based on the particular
robot type provided by the fleet configuration proxy service. In other
features, the task definition
system generates a data structure for each task in the set of tasks that
includes a reference to a
digital twin for at least one of the task and at least one robot operating
unit for performing the
tasks for use by the workflow simulation system. In other features, the task
definition system
generates a data structure for each task in the set of tasks that identifies
at least one a type of
robot and a robot operating unit for performing the task and a configuration
data structure for
configuring a robot for performing the task. In other features, the task
definition system generates
a data structure for each task in the set of tasks and stores the data
structure in a library of robot
tasks that is indexed by information indicative of the job request and an
identifier of at least one
of the robot type and the robot operating unit. In other features, the task
definition system
matches requirements for constraints identified in the job request with robot
capabilities when
identifying the type of robot for meeting the task objective. In other
features, the task definition
system generates a plurality of robot tasks for a plurality of different robot
types to achieve a task
objective.
[0020] In other features, the task definition system queries a library of
robot tasks for candidate
robot tasks that satisfy the task objective and interacts with the fleet
configuration proxy service
to select a robot tasks from the candidate robot tasks based on the at least
one fleet objective. In
other features, the at least one fleet objective is compatibility with
available robot operating units.
In other features, the task definition system queries a library of robot tasks
for candidate robot
tasks that satisfy the task objective and interacts with the fleet
intelligence layer to select a robot
task from the candidate robot tasks based on a suitability of the candidate
robot tasks for
achieving the task objective. In other features, the task definition system
references information
descriptive of sensor detection packages that indicate preferred sequences of
sensing tasks when
defining the set of tasks. In other features, the job workflow system
references information
descriptive of sensor detection packages that indicate preferred sequences of
sensing tasks when
defining the workflow of robot tasks. In other features, the job workflow
system generates the
workflow of robot tasks based on second task dependency on a first task for
meeting an objective
of the second task. In other features, the job workflow simulation system
operates digital twins of
tasks in the set of tasks for determining an optimized workflow order of
tasks.
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[0021] A robot fleet management platform for configuring robot fleet resources
includes a set
of one or more processors that execute a set of computer-readable
instructions. The set of one or
more processors collectively execute a job configuration system that receives
a job request and
determines a set of robot tasks to be performed by a robot fleet based on job
content associated
.. with the job request and at least one fleet objective in a set of fleet
objectives. A fleet
configuration proxy service applies fleet configuration services to the set of
robot tasks and the
job content to produce a fleet resource configuration data structure for the
job request. A fleet
intelligence layer activates a set of intelligence services to produce at
least one recommended
robot task and associated contextual information that facilitates robot
selection and task ordering
.. in a workflow of robot tasks. A job workflow system generates a workflow
that defines an order
of performance of the robot tasks based on the fleet resource configuration
data structure and the
set of robot tasks. A workflow simulation system is configured to simulate
performance of the
job based on the workflow and a job execution simulation environment to
produce a simulation
result that is used to recursively redefine one or more of the set of tasks,
the fleet resource
configuration data structure, or the workflow until the simulation result
satisfies a second fleet
objective of the set of fleet objectives corresponding to the job request. A
job execution plan
generator, in response to the simulation result satisfying the set of fleet
objectives, generates a
job execution plan based on the set of tasks, the fleet resource configuration
data structure, and
the workflow.
[0022] In other features, the job configuration system includes a job parsing
system that applies
content and structural filters to job content received in association with a
job request to identify
portions thereof suitable for robot automation. In other features, the job
configuration system
includes a task definition system that establishes a set of robot tasks that
each define at least a
type of robot and a task objective, the set of robot tasks are based at least
in part on the portions
of the job request that are suitable for robot automation and meet a first
fleet objective of the set
of fleet objectives. In other features, the fleet resource configuration data
structure defines a set
of task associations and a set of robot adaptation instructions. Each task
association associates at
least one robot operating unit to a respective robot task of the set of robot
tasks and the set of
robot adaptation instructions define a manner by which one or more robot
operating units of a
robot fleet are to be adapted to perform respective tasks to which the robots
are assigned. In other
features, the workflow simulation system applies the workflow in the job
execution simulation
environment that includes digital models of the robot operating units assigned
to the robot fleet
and digital models of the task definitions to produce the simulation result.
In other features, the
job configuration system interacts with the intelligence layer to suggest
alternate tasks that meet a
second fleet objective. In other features, the job configuration system
interacts with the
intelligence layer to optimize at least one of a robot type and a task
objective based on at least
one of the set of fleet objectives. In other features, the first fleet
objective includes fleet resource
utilization criteria.
[0023] In other features, the job configuration system receives from the fleet
configuration
proxy service a particular robot type for use when performing the robot task.
In other features,
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the job configuration system configures the set of robot tasks based on the
particular robot type
provided by the fleet configuration proxy service. In other features, the job
configuration system
generates a data structure for each task in the set of tasks that includes a
reference to a digital
twin for at least one of the task and at least one robot operating unit for
performing the tasks for
use by the workflow simulation system. In other features, the job
configuration system generates
a data structure for each task in the set of tasks that identifies at least
one a type of robot and a
robot operating unit for performing the task and a configuration data
structure for configuring a
robot for performing the task. In other features, the job configuration system
generates a data
structure for each task in the set of tasks and stores the data structure in a
library of robot tasks
that is indexed by information indicative of the job request and an identifier
of at least one of the
robot type and the robot operating unit. In other features, the job
configuration system matches
requirements for constraints identified in the job request with robot
capabilities when identifying
the type of robot for meeting the task objective. In other features, the job
configuration system
generates a plurality of robot tasks for a plurality of different robot types
to achieve a task
objective. In other features, the job configuration system queries a library
of robot tasks for
candidate robot tasks that satisfy the task objective and interacts with the
fleet configuration
proxy service to select a robot tasks from the candidate robot tasks based on
the at least one fleet
objective.
[0024] In other features, the at least one fleet objective is compatibility
with available robot
operating units. In other features, the job configuration system queries a
library of robot tasks for
candidate robot tasks that satisfy the task objective and interacts with the
fleet intelligence layer
to select a robot task from the candidate robot tasks based on a suitability
of the candidate robot
tasks for achieving the task objective. In other features, the job
configuration system references
information descriptive of sensor detection packages that indicate preferred
sequences of sensing
tasks when defining the set of tasks. In other features, the job workflow
system references
information descriptive of sensor detection packages that indicate preferred
sequences of sensing
tasks when defining the workflow of robot tasks. In other features, the job
workflow system
generates the workflow of robot tasks based on second task dependency on a
first task for
meeting an objective of the second task. In other features, the job workflow
simulation system
operates digital twins of tasks in the set of tasks for determining an
optimized workflow order of
tasks.
[0025] A robot fleet management platform includes a set of one or more
processors that execute
a set of computer-readable instructions. The set of one or more processors
collectively execute
receiving a job request includes information descriptive of job deliverable
and request-specific
constraints for delivering the job deliverable. Also executed is applying
content and structural
filters to content received in association with a job request to identify
portions thereof suitable for
robot automation. Also executed is establishing a set of robot tasks that each
define at least a type
of robot and a task objective, the set of robot tasks are based at least in
part on the portions of the
job request that are suitable for robot automation and meet a first fleet
objective. Also executed is
applying fleet configuration services to the job content and the set of robot
tasks to produce a
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fleet resource configuration data structure for the job request that
associates at least one robot
operating unit with each task in the set of tasks and, based on the at least
one robot operating
unit, robot adaptation instructions for performing the associated task. Also
executed is
recommending robot task and associated contextual information that facilitates
robot selection
and task ordering in a workflow of robot tasks with a fleet intelligence
layer. Also executed is
generating a workflow of the robot tasks based on the fleet resource
configuration data structure
and the set of robot tasks. Also executed is simulating digital models of the
robot operating units
performing digital models of the task definitions thereby validating the
generated workflow while
providing a result of the job execution simulation for recursively
establishing the set of robot
tasks. Also executed is generating at least a first portion of an execution
plan for robot fleet
resources configured in the fleet resource configuration data structure.
[0026] In other features, the robot fleet management platform includes
suggesting alternate
tasks that meet a second fleet objective with the fleet intelligence layer. In
other features, the
robot fleet management platform includes optimizing at least one of a robot
type and a task
objective with the intelligence layer based on the first fleet objective. In
other features, the first
fleet objective includes fleet resource utilization criteria. In other
features, the task definition
system receives from the fleet configuration proxy service a particular robot
type for use when
performing the robot task. In other features, establishing the set of robot
tasks is based on the
particular robot type provided by the fleet configuration proxy service. In
other features,
establishing the set of robot tasks includes generating a data structure for
each task in the set of
tasks that includes a reference to a digital twin for at least one of the task
and at least one robot
operating unit for performing the tasks for use by the workflow simulation
system. In other
features, establishing the set of robot tasks includes generating a data
structure for each task in
the set of tasks that identifies at least one a type of robot and a robot
operating unit for
performing the task and a configuration data structure for configuring a robot
for performing the
task. In other features, establishing the set of robot tasks includes
generating a data structure for
each task in the set of tasks and stores the data structure in a library of
robot tasks that is indexed
by information indicative of the job request and an identifier of at least one
of the robot type and
the robot operating unit.
[0027] In other features, establishing the set of robot tasks includes
matching requirements for
constraints identified in the job request with robot capabilities when
identifying the type of robot
for meeting the task objective. In other features, establishing the set of
robot tasks includes
generating a plurality of robot tasks for a plurality of different robot types
to achieve a task
objective. In other features, establishing the set of robot tasks includes
querying a library of robot
tasks for candidate robot tasks that satisfy the task objective and interacts
with the fleet
configuration proxy service to select a robot tasks from the candidate robot
tasks based on the at
least one fleet objective. In other features, the at least one fleet objective
is compatibility with
available robot operating units. In other features, establishing the set of
robot tasks includes
querying a library of robot tasks for candidate robot tasks that satisfy the
task objective and
interacts with the fleet intelligence layer to select a robot task from the
candidate robot tasks
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based on a suitability of the candidate robot tasks for achieving the task
objective. In other
features, establishing the set of robot tasks includes referencing information
descriptive of sensor
detection packages that indicate preferred sequences of sensing tasks when
defining the set of
tasks. In other features, generating the workflow of the robot tasks includes
referencing
information descriptive of sensor detection packages that indicate preferred
sequences of sensing
tasks when defining the workflow of robot tasks. In other features, generating
the workflow of
the robot tasks is based on second task dependency on a first task for meeting
an objective of the
second task. In other features, simulating digital models of the robot
operating units includes
operating digital twins of tasks in the set of tasks for determining an
optimized workflow order of
tasks.
[0028] A robot fleet platform is for preparing a job request for facilitating
configuration of a
robot fleet operated by the robot fleet platform. The system includes a set of
one or more
processors that execute a set of computer-readable instructions. The set of
one or more
processors collectively execute a job request ingestion system configured to
receive job content
relating to at least one of picking, packing, moving, storing, warehousing,
transporting or
delivering of a set of items in a supply chain, the job content including an
electronic job request
and related data. A job content parsing system is configured to apply filters
to the received job
content to identify candidate portions thereof for robot automation. A fleet
intelligence layer
activates a set of intelligence services to process terms in the candidate
portions of the job
content and receive therefrom at least one recommended robot task and
associated contextual
information that facilitates robot selection and task ordering in a workflow
of robot tasks. A
demand intelligence layer provides real time information relating to a
parameter of demand for
the set of items in the supply chain. A job requirements system produces a set
of job request
instance-specific job requirements based on the portions of the job content
that indicate robot
automation, the real time information from the demand intelligence layer and
the at least one
recommended robot task and associated contextual information. The set of job
requirements is
stored in a non-transitory computer readable memory that is accessible by at
least one processor
of the set of processors.
[0029] In other features, the job content parsing system retrieves a set of
content and structural
filters from a job configuration library that facilitates mapping indicia of
the job content with
target terms that indicate robot automation. In other features, the job
content parsing system
augments a set of default content and structural filters with filter criteria
from a job configuration
library that facilitates mapping indicia of the job content with target terms
that indicate robot
automation. In other features, the content filter indicates terms in the job
content that distinguish
robot automation content from other content in the job content. In other
features, the terms are
retrieved from a job configuration library that facilitates mapping indicia of
the job content with
terms that indicate robot automation. In other features, the fleet
intelligence layer facilitates
sending portions of the job content identified as suitable for robot
automation to a machine
learning service of the set of intelligence services for improving job content
parsing. In other
features, the machine learning service is trained with training data sets
includes human-generated
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feedback on job content parsing results for a plurality of job requests, robot
automation
knowledge bases, desired job-specific knowledge bases, technical dictionaries,
and content
received from job experts. In other features, the job parsing system is
configured to detect
physical location information in the job content that facilitates
automatically determining at least
one of transportation options, operational constraints, permitting
requirements, transport
restrictions, fleet assets that are local to a physical location of the job
request, and logistics
constraints.
[0030] In other features, the physical location information includes one or
more of an address, a
region, GPS data, aerial photography, a marked location on a map image, map
coordinates,
.. latitude, longitude, altitude, a route, a depth relative to sea level. In
other features, the job parsing
system is configured to detect electrical power information for at least one
location in the job
content including a plurality of voltages, frequencies, currents, schedules of
availability,
schedules of grid-provided electricity costs, cost per kwh, a power demand
profile, a maximum
thermal density, and proximity to the at least one location. In other
features, the job parsing
system is configured to detect digital data representative of a layout of a
portion of a job site that
is present or referenced in the job content to facilitate generating at least
one job request instance-
specific requirement associated with job site layout. In other features, the
job parsing system is
configured to detect at least one of information descriptive of an operating
environment,
deliverables, interfaces through which information about the job request is
communicated with a
job requester, wireless communication network accessibility, budget
constraints for performing
tasks, and scheduling of resources in regards to access and operation at a job
site. In other
features, the job request ingestion system is configured to scan received job
content for external
links to related data. In other features, the job request ingestion system is
configured to retrieve
related data for use by the robot fleet platform based on the external links.
In other features, the
job request ingestion system is configured to determine and forward to a job
content parsing
system portions of job content received that include references to activities
suitable for being
performed by a robotic fleet resource.
[0031] In other features, the job request ingestion system is configured to
process content
received with a job configuration indicia filter that automatically routes job
configuration indicia
in the job content to a job configuration library look up service for
classifying the job
configuration indicia as one of a current job configuration, a prior job
configuration, or an
unknown job configuration. In other features, the job content parsing system
is configured to
identify structural and content elements in the received content that
facilitate identification of
candidate robot tasks. In other features, the job content parsing system is
configured to identify
structural elements in the received content that indicate at least one of
tasks, sub tasks, task
ordering, task dependencies, and task requirements for facilitating selection
of fleet robot
operating units. In other features, the job content parsing system is
configured to identify content
terms indicative of at least one robot minimum capacity. In other features,
the job content parsing
system is configured with a robot type filter that when applied to the job
request content
identifies terms indicative of a type of robot for performing a task. In other
features, the job
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request ingestion system includes a job request ingestion interface for
receiving the electronic job
request. In other features, applying the content and structural filters
includes scanning received
content for data indicative of robot activities. In other features, applying
the content and
structural filters with the job content parsing system includes processing
received content with a
robot type filter that when applied to the job request content identifies
terms indicative of a type
of robot for performing a task.
[0032] In other features, the job parsing system utilizes the content filters
to detect qualified job
data. In other features, the robot fleet platform includes a qualified data
query generation system
configured to generate a query regarding at least one element of qualified
data in the job content
for clarification thereof. In other features, the query regarding the at least
one element of
qualified data is presented in the user interface. In other features, the
query regarding at least one
element of qualified data is provided to the fleet intelligence layer for
processing with at least one
intelligence service of the set of intelligence services to provide at least
one clarification item of
data for the at least one element of qualified data through the fleet
intelligence layer. In other
features, the robot fleet platform includes a qualified data resolution system
configured to
evaluate at least one qualified data element in the job content for similarity
to clarified data from
a plurality of job requests, and based on an outcome of the evaluation to
adjust the at least one
qualified data element based on a similar clarified data element. In other
features, adjusting the at
least one qualified data element includes replacing a qualified data value in
the qualified data
element with a corresponding data value from the clarified data element. In
other features, the
content filters are configured to identify qualified data, including at least
one of missing data,
unclear data and qualitative references. In other features, the fleet
intelligence layer facilitates
processing qualified data with a machine learning service of the set of
intelligence services for
improving parsing of qualified data. In other features, the content filters
are configured to
identify qualified data and related context for facilitating resolution of at
least one of missing
data, unclear data and qualitative references in the qualified data.
[0033] A value chain network automation system includes a supply chain robotic
fleet data set
including attributes of a set of states and capabilities of a set of robotic
systems in a supply chain
for a set of goods. The system includes a demand intelligence robotic process
automation data set
including attributes of a set of states of a set of robotic process automation
systems that undertake
automation of a set of demand forecasting tasks for the set of goods. The
system includes a
coordination system that provides a set of robotic task instructions for the
supply chain robotic
fleet based on processing the supply chain robotic fleet data set and the
demand intelligence
robotic process automation data set to coordinate supply and demand for the
set of goods.
FURTHER SUMMARY
[0034] According to some embodiments of the present disclosure, methods and
systems are
provided herein for an information technology system that may include a cloud-
based
management platform with a micro-services architecture; a set of interfaces,
network
connectivity facilities, adaptive intelligence facilities, data storage
facilities, and monitoring
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facilities; and a set of applications for enabling an enterprise to manage a
set of value chain
network entities from a point of origin to a point of customer use.
[0035] Among other things, provided herein are methods, systems, components,
processes,
modules, blocks, circuits, sub-systems, articles, services, software,
hardware, and other elements
(collectively referred to in some cases as the "platform" or the "system,"
which terms should be
understood to encompass any of the above except where context indicates
otherwise) that
individually or collectively improve the utilization of additive manufacturing
capabilities in a
network of value chain entities in a value chain network (such terms
encompassing the many
examples and embodiments disclosed herein and in the documents incorporated by
reference
herein).
[0036] In embodiments, such methods and systems allow for feedback and
monitoring by the
customer and various other interested parties throughout the modelling,
printing and supply chain
processes resulting in optimizing 3D printing parameters, achieving greater
fidelity and accuracy
in printing and enhancing efficiency and traceability of design processes,
manufacturing, supply
chains demand management systems, products, and product use cases among
others.
[0037] An aspect provided herein includes an information technology system
having an
artificial intelligence system for learning on a training set of outcomes,
parameters, and data
collected from a set of distributed manufacturing network entities in a
distributed manufacturing
network and/or value chain network to optimize digital production processes
and workflows.
[0038] An aspect provided herein includes an information technology system for
a distributed
manufacturing network, comprising an additive manufacturing management
platform configured
for managing process and production workflows for a set of distributed
manufacturing network
entities through design, modelling, printing, supply chain, delivery, point-of-
sale and point of
usage stages; an artificial intelligence system configured for learning on a
training set of
outcomes, parameters, and data collected from the distributed manufacturing
network entities of
the distributed manufacturing network to optimize digital production processes
and workflows;
and a distributed ledger system integrated with a digital thread configured
for providing unified
views of workflow and transaction information to entities in the distributed
manufacturing
network.
[0039] In embodiments, the information technology system comprises a control
system
configured to adjust the data and one or more parameters collected from the
distributed
manufacturing network entities in real time.
[0040] In embodiments, the information technology system comprises a digital
twin system
configured to build a digital twin of one or more distributed manufacturing
network entities, the
digital twin providing a substantially real-time representation of the
distributed manufacturing
network entity through data from one or more sensors positioned in, on or near
the distributed
manufacturing network entity. In embodiments, the digital twin may represent
various
parameters and attributes of the manufacturing entity (whether an additive,
subtractive,
biological, chemical, or other entity), such as the types of materials it can
handle, current levels
of available source materials, processing/output speed, operating
capabilities, biological
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manufacturing capability, vacuum processing capability, energy production and
consumption
information (e.g., for heating, laser processing, and the like), pricing
parameters, and the like. In
embodiments, the platform, such as using an artificial intelligence system,
may execute
simulations on the digital twin or projected outputs thereof for predicting a
possible future state
of the distributed manufacturing network entity and/or one or more outputs
thereof.
[0041] In embodiments, the distributed manufacturing network entities include
a set of printed
parts, products, processes, additive manufacturing units like 3D printers,
other types of
manufacturing units, parties (e.g., suppliers, manufacturers, financiers,
users, customers and
others), packagers, infrastructure, vehicles, and manufacturing nodes.
[0042] An aspect provided herein includes a distributed manufacturing network
comprising: an
additive manufacturing management platform with an artificial intelligence
system configured to
learn on a training set of outcomes, parameters, and data collected from a set
of distributed
manufacturing network entities for optimizing manufacturing, supply chain,
demand
management, service, maintenance and other processes and workflows; and a
distributed ledger
integrated with digital threads of the distributed manufacturing network
entities.
[0043] In embodiments, the distributed network entity is a part being
manufactured using
additive manufacturing and the digital thread constitutes information related
to the complete
lifecycle of the part from design, modeling, production, validation, use and
maintenance through
disposal. In embodiments, a digital thread may include a set of instructions
for manufacturing an
item that includes additive manufacturing instructions, such as design
specifications and/or
operating parameters by which one or more additive manufacturing units may be
configured and
operated to produce the item. In embodiments, a digital thread may include
multiple alternative
such instruction sets, such as ones that are configured to facilitate
manufacturing of the item by
alternative forms of additive manufacturing and/or hybrid or combinations
thereof with other
additive manufacturing types and/or with other manufacturing types. In
embodiments the
instruction sets are embodied in a set of digital twins.
[0044] An aspect provided herein includes an autonomous additive manufacturing
platform
comprising: a plurality of sensors positioned in, on, and/or near a product or
a part and
configured to collect sensor data related to the product or the part, the
sensor data being
substantially real-time sensor data; an adaptive intelligence system connected
to the plurality of
sensors and configured to receive the sensor data from the plurality of
sensors, the adaptive
intelligence system including: a machine learning system configured to input
the sensor data into
one or more machine learning models, the sensor data being used as training
data for the machine
learning models, the machine learning models being configured to transform the
sensor data into
simulation data; and a digital twin system configured to create a product twin
or a part twin based
on the simulation data, the product twin or the part twin providing for
substantially real-time
representation of the product or the part and providing for simulation of a
possible future state of
the product or the part via the simulation data; and an artificial
intelligence system configured to
execute simulations on the digital twin system; wherein the one or more models
are utilized by
the artificial intelligence system to make classifications, predictions,
recommendations, and/or to
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generate or facilitate decisions or instructions relating to the product and
the part, such as
decisions or instructions governing design, configuration, material selection,
shape selection,
manufacturing type, job scheduling and many others.
[0045] In embodiments, the models trained by the machine learning system are
utilized by the
artificial intelligence system to execute simulations on the part twin for
predicting part expansion
or shrinkage, such as based on physical models of expansion or contraction for
the materials
simulated by the simulation.
[0046] In embodiments, the models trained by the machine learning system are
utilized by the
artificial intelligence system to execute simulations on the part twin for
predicting part warpage.
[0047] In embodiments, the models trained by the machine learning system are
utilized by the
artificial intelligence system to execute simulations on the part twin for
calculating necessary
changes to the additive manufacturing process to compensate for part shrinkage
and warpage,
such as material selection, shape selection, interface selection, heat
management element
selection or configuration, or the like.
[0048] In embodiments the models trained by the machine learning system and/or
other Al
system may undertake simulations and generate or facilitate decisions or
instructions based at
least in part on anticipated conditions of use, such as based on geolocation
of a customer,
specification for indoor or outdoor use, a set of weather and/or climate
models, or the like. For
example, additive manufacturing of a part that has the same intended use may
be configured to
use different materials, structural elements, or other elements based on
whether the part is
intended for use outdoors in a very cold climate, versus being used indoors or
in a very hot
environment. Thus, methods and systems are provided for point-of-usage aware,
environment-of-
usage aware, and customer type-of-usage aware automated configuration of
manufacturing
instructions for a part or product that involves automated manufacturing
entities, such as additive
manufacturing entities.
[0049] In embodiments, the models trained by the machine learning system are
utilized by the
artificial intelligence system to execute simulations on the part twin for
testing the compatibility
of 3D printed parts with other parts, with a system in which the parts will be
used, with
infrastructure elements of an environment of use, with ambient conditions of
an environment,
with available tools, and/or with a 3D printer or other additive or other
manufacturing system that
may be available to produce the part.
[0050] In embodiments, the models trained by the machine learning system are
utilized by the
artificial intelligence system to execute simulations on the part twin for
predicting deformations
or failure in a 3D printed part. In embodiments, the models may also determine
a set or sequence
of process control parameter adjustments that will implement a corrective
action, e.g., to adjust a
layer dimension or thickness, so as to correct a defect. In embodiments, the
system may send a
warning or error signal to an operator or a user, or automatically abort the
printing process.
[0051] In embodiments, the artificial intelligence system includes or
integrates with a machine
vision system that uses a variable-focus, liquid lens-based camera for image
capture and defect
detection. In embodiments, the artificial intelligence system operates on
images captured at
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variable focal lengths, with variable lighting settings, or the like, to
facilitate improved AI-based
object recognition, boundary detection, item classification, material
recognition, or other factors
that are relevant to the design, manufacturing, or utilization of a part or
other component. In
embodiments, outputs from an integrated Al and variable focus lens system are
integrated with or
into a digital twin that represents a set of items, such as parts, that are
captured by a system using
the variable focus lens.
[0052] In embodiments, the models trained by the machine learning system are
utilized by the
artificial intelligence system to execute simulations on the part twin for
optimizing the build
process to minimize the occurrence of deformations.
[0053] In embodiments, the models trained by the machine learning system are
utilized by the
artificial intelligence system to execute simulations on the product twin for
predicting the cost
and/or the price of the product or component thereof. Cost prediction may
utilize inputs from
marketplaces, outputs from search engines, cost models (such as enterprise
procurement system
models), costs presented in smart contracts, costs presented on web sites, and
other inputs, such
as ones that relate to costs of additive manufacturing input materials, costs
of additive
manufacturing processing time, or the like. Cost prediction may use inputs on
process costs,
including energy costs, labor costs, and the like. Price predictions may be
based on similar
inputs, such as public information from various sources that indicate current
or historical market
prices for a product. Cost or price predictions may take inputs from smart
contracts, such as
smart contract parameters that indicate current cost and price information
offered in third party
contracts for materials, parts, and the like.
[0054] An aspect provided herein includes an information technology system for
a distributed
manufacturing network comprising: an additive manufacturing management
platform with an
artificial intelligence system configured to learn on a training set of
outcomes, parameters, and
data collected from a set of distributed manufacturing network entities and
execute simulations
on digital twins of the distributed manufacturing network entities to make
classifications,
predictions, and optimization related decisions for the distributed
manufacturing network entities;
and a distributed ledger system integrated with a digital thread configured to
provide unified
views of workflow and transaction information to the entities in the
distributed manufacturing
network.
[0055] In embodiments, the digital manufacturing network entities include a
set of printed
parts, products, processes, additive manufacturing units like 3D printers,
other types of
manufacturing units, parties (e.g., suppliers, manufacturers, financiers,
users, customers and
others), packagers, infrastructure, vehicles, and manufacturing nodes.
[0056] In embodiments, the artificial intelligence system executes simulations
on one or more
of the part twins, the product twins and the printer twins for generating 3D
printing quotes. In
embodiments a set of additive manufacturing quotes may be embodied in a smart
contract,
optionally linked to a blockchain, such that additive manufacturing operations
may be contracted
for via the smart contract.
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[0057] In embodiments, the artificial intelligence system executes simulations
on one or more
of the part twins, the product twins, the printer twins or other twins for
generating a set of
recommendations related to printing or other additive manufacturing to a user
of the platform.
Recommendations may include recommendations for type of material, type of
printer or other
additive manufacturing facility, type of technique, service provider or source
of manufacturing,
location of manufacturing, timing of scheduling of an additive manufacturing
job, or step thereof,
parameters for design (e.g., among a set of possible designs), and the like.
In embodiments, the
recommendations relate to a choice of a material for printing. In embodiments,
the
recommendations relate to a choice of a 3D printing technique.
[0058] In embodiments, the artificial intelligence system executes simulations
on one or more
of the part twins, the product twins and the printer twins for generating
recommendations related
to printing to a user of the platform.
[0059] In embodiments, the artificial intelligence system executes simulations
on one or more
of the part twins, the product twins and the printer twins for predicting
delivery times for 3D
printing jobs.
[0060] In embodiments, the artificial intelligence system executes simulations
on one or more
of the part twins, the product twins, the printer twins and the manufacturing
node twins for
predicting cost over-runs in the manufacturing process.
[0061] In embodiments, the artificial intelligence system executes simulations
on one or more
of the part twins, the product twins, the printer twins and the manufacturing
node twins for
optimizing the production sequencing of parts and products based on quoted
price, delivery, sale
margin, order size, or similar characteristics.
[0062] In embodiments, the artificial intelligence system executes simulations
on one or more
of the part twins, the product twins, the printer twins and the manufacturing
node twins for
optimizing the cycle time for manufacturing.
[0063] In embodiments, the artificial intelligence system executes simulations
on one or more
of the part twins, the product twins, the printer twins, the customer twins
and the manufacturing
node twins to predict and manage product demand from one or more customers.
[0064] In embodiments, the artificial intelligence system executes simulations
on one or more
of the twins to predict and manage supply of a set of items from the digital
manufacturing
network.
[0065] In embodiments, wherein the artificial intelligence system executes
simulations on one
or more of the twins to optimize production capacity for a distributed
manufacturing network.
[0066] In embodiments, the distributed manufacturing entities include: link
to, use, take input
from, or integrate with a set of other systems, such as an Enterprise Resource
Planning (ERP)
system, a Manufacturing Execution system (MES), a Product Lifecycle Management
(PLM)
system, a maintenance management system (MMS), a Quality Management system
(QMS), a
certification system, a compliance system, a Robot/Cobot system, and an SCCG
system.
[0067] An aspect provided herein includes a computer-implemented method for
facilitating the
manufacture and delivery of a 3D printed product to a customer using one or
more manufacturing
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nodes of a distributed manufacturing network, comprising receiving one or more
product
requirements from the customer; tokenizing and storing the product
requirements in a distributed
ledger system; determining one or more manufacturing nodes, printers,
processes and materials
based on the product requirements; generating a quote including pricing and
delivery timelines;
.. and upon acceptance of the quote by the customer, manufacturing and
delivering the 3D printed
product to the customer. In embodiments the quote is automatically generated
and configured
into a smart contract for additive manufacturing.
[0068] In embodiments, determining includes matching a customer order with a
manufacturing
node or a 3D printer based on factors like printer capabilities, locations of
the customer and the
manufacturing nodes, available capacity at each node, pricing and timelines
requirements and the
customer satisfaction score.
[0069] In various embodiments, such as involving entity matching, design
selection, type of
manufacturing selection, material selection, recommendation, scheduling, and
the like, location-
based determinations may include geofencing and other distance-based
information, route-based
information (such as factoring in traffic congestion and other factors that
may impact delivery
times), and other location-related information related to a point-of-
distribution, transportation
facility, point-of-sale and/or point-of-use, such as infrastructure
information, resource availability
information, weather information, climate information, and many others.
Location-based
determination may, for example, factor ambient temperature or other conditions
for a location (or
a combination of location and intended use) into selection of a material for
manufacturing, a
structure (such as factoring in likely expansion or contraction in hot or cold
extreme conditions)
and the like.
[0070] In embodiments, the method further comprises rating one or more
manufacturing nodes
based on a customer satisfaction score for meeting customer requirements.
[0071] In embodiments, the method may help in managing the production
workflows within
and across one or more manufacturing nodes, thereby facilitating collaboration
across the
manufacturing nodes through the sharing of resources, capabilities and
intelligence. In
embodiments, the manufacturing nodes may collaborate for forecasting and
prediction of
material supply and product demand. In embodiments, the manufacturing nodes
may collaborate
for design and product development. In embodiments, the manufacturing nodes
may collaborate
for manufacturing and assembling one or more parts of a product. In
embodiments, the
manufacturing nodes may collaborate for distribution and delivery of
manufactured products.
[0072] In embodiments, the method may provide "manufacturing as a service" by
leveraging
unutilized capacity of one or more manufacturing nodes or 3D printers by
exposing the capacity
to one or more users seeking to fabricate 3D printed parts. In embodiments,
manufacturing as a
service may be offered via a smart contract, optionally using a blockchain
and/or distributed
ledger. In embodiments, manufacturing-as-a-service may be governed and managed
by an
artificial intelligence system, such as for configuring offerings, scheduling
jobs, setting prices,
setting other contract terms and conditions, and the like for a set of
additive manufacturing
entities.
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[0073] An aspect provided herein includes a distributed manufacturing network
comprising: a
distributed ledger system integrated with digital threads of a set of
distributed manufacturing
network entities for storing information on event, activities and transactions
related to the
distributed manufacturing network entities; and an artificial intelligence
system configured to
learn on a training set of outcomes, parameters, and data collected from the
distributed
manufacturing network entities to optimize manufacturing and value chain
workflows.
[0074] In embodiments, the distributed ledger system includes a decentralized
application
downloadable by entities in the distributed manufacturing network.
[0075] In embodiments, the distributed ledger system includes a user interface
configured to
provide a set of unified views of the workflows to the set of entities of a
distributed
manufacturing network.
[0076] In embodiments, the distributed ledger system includes a user interface
configured to
provide tracking and reporting on state and movement of a product from order
through
manufacture and assembly to final delivery to the customer.
[0077] In embodiments, the distributed ledger system includes a user interface
configured to
provide unified data collection from a metrology system.
[0078] In embodiments, the distributed ledger system includes a system for
digital rights
management of entities in the distributed manufacturing network. In
embodiments, the
distributed ledger system stores digital fingerprinting information of
documents/files and other
information including creation, modification.
[0079] In embodiments, the distributed ledger system uses a token, such as a
cryptocurrency
token, such as to incentivize value creation and transfer value between
entities in the distributed
manufacturing network. For example, a unit of a token may represent a defined
amount of
manufacturing capacity of a given type, a defined amount of material of a
given type, a defined
time of utilization, or other measurable quantity of distributed manufacturing
capacity. In
embodiments, tokens may comprise a mechanism for exchange of value governed by
a set of
smart contracts.
[0080] In embodiments, the distributed ledger system includes a system for
attesting the
experience of a manufacturing node.
[0081] In embodiments, the distributed ledger system includes a system for
capturing the end-
to-end traceability of a part.
[0082] In embodiments, the distributed ledger system includes a system for
tracking all
transactions, modifications, quality checks and certifications on the
distributed ledger.
[0083] In embodiments, the distributed ledger system includes a system for
validating
capabilities of a manufacturing node.
[0084] In embodiments, the distributed ledger system includes or supports
smart contracts for
automating and managing the workflows in the distributed manufacturing
network.
[0085] In embodiments, the distributed ledger system includes or supports a
smart contract for
executing a purchase order covering the scope of work, quotation, timelines,
and payment terms.
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[0086] In embodiments, the distributed ledger system includes or supports a
smart contract for
processing of payment by a customer upon delivery of product.
[0087] In embodiments, the distributed ledger system includes or supports a
smart contract for
processing insurance claims for a defective product.
[0088] In embodiments, the distributed ledger system includes or supports a
smart contract for
processing warranty claims.
[0089] In embodiments, the distributed ledger system includes or supports a
smart contract for
automated execution and payment for maintenance.
[0090] An aspect provided herein includes a distributed manufacturing network
information
technology system, comprising: a cloud-based additive manufacturing management
platform
with a user interface, connectivity facilities, data storage facilities, and
monitoring facilities; a set
of applications for enabling the additive manufacturing management platform to
manage a set of
distributed manufacturing network entities; and an artificial intelligence
system configured to
learn on a training set of outcomes, parameters, and data collected from the
distributed
manufacturing network entities to optimize manufacturing and value chain
workflows.
[0091] In embodiments, the connectivity facilities include network
connections, interfaces,
ports, application programming interfaces (APIs), brokers, services,
connectors, wired or
wireless communication links, human-accessible interfaces, software
interfaces, micro-services,
SaaS interfaces, PaaS interfaces, IaaS interfaces, cloud capabilities, or the
like.
[0092] In embodiments, the artificial intelligence system provides
optimization and process
control across the entire lifecycle of manufacturing from product conception
and design through
manufacturing and distribution to sales, usage, service and maintenance.
[0093] In embodiments, the artificial intelligence system provides for
generative design and
topology optimization to determine at least one product design suitable for
fabrication, suitable to
meet customer needs, suitable to meet producer specifications, or the like.
[0094] In embodiments, the artificial intelligence system provides for
optimization of a build
preparation process.
[0095] In embodiments, the artificial intelligence system optimizes a part
orientation process
for superior production results.
[0096] In embodiments, the artificial intelligence system provides for
optimizing toolpath
generation.
[0097] In embodiments, the artificial intelligence system provides for
optimized dynamic 2D,
2.5D and 3D nesting to maximize the number of printed parts while minimizing
the raw material
waste.
[0098] In embodiments, the user interface includes a dashboard providing
tracking and tracing
of production history of one or more 3D printed parts.
[0099] In embodiments, the user interface includes a dashboard providing batch
traceability to
identify parts from the same batch.
[0100] In embodiments, the user interface includes a digital twin interface to
resolve queries
from a user of the network related to a part or a product.
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[0101] In embodiments, the user interface includes a virtual reality (VR)
interface configured to
enable a user to build 3D models in YR.
[0102] In embodiments, the applications are selected from a group consisting
of production
management applications, production reporting applications, production
analysis applications and
value chain management applications.
[0103] In embodiments, the application is an order tracking application
configured to track the
product order through its movement in the distributed manufacturing network.
[0104] In embodiments, the application is a workflow management application
configured to
manage the complete 3D printing production workflow.
[0105] In embodiments, the application is an alerts and notifications
application configured to
generate alerts, notifications and reports about one or more events in the
distributed
manufacturing network to a user or customer of the network. In embodiments,
the alerts and
notifications application is configured to transmit alerts related to print
errors or failures to a
computing device of a user.
[0106] In embodiments, the application is a payment gateway application
configured to manage
the entire billing, payment and invoicing process for a customer ordering a
product using the
distributed manufacturing network.
[0107] In embodiments, the artificial intelligence system is configured to
automatically classify
and cluster parts, such as ones that may be additively manufactured, such as
based on similarity
of attributes, including physical attributes, shapes, functional attributes,
material attributes,
performance attributes, economic attributes, and others.
[0108] In embodiments, the artificial intelligence system is configured to
analyze usage
patterns associated with one or more users and learn user preferences with
respect to materials,
orientations, and/or print strategies.
[0109] In embodiments, the artificial intelligence system is configured to
minimize material
waste production during the additive manufacturing process.
[0110] In embodiments, the artificial intelligence system is configured to
optimize material
utilization during the additive manufacturing process including by providing
instruction sets that
factor in waste production and material recapture or recycling.
[0111] In embodiments, the artificial intelligence system is configured to
optimize a
combination of material utilization, energy utilization and other resource
utilization during the
additive manufacturing process, such as by factoring in energy and labor costs
to optimization of
an instruction set.
[0112] In embodiments, the artificial intelligence system configured to manage
the real time
dynamics affecting inventory levels for smart inventory and materials
management in the
distributed manufacturing network.
[0113] In embodiments, the artificial intelligence system is configured to
build, maintain, and
provide a library of parts with preconfigured parameters, and is searchable by
materials,
properties, functions, equipment compatibility, shape compatibility, interface
compatibility, part
type, part class, industry, and compliance.
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[0114] In embodiments, the artificial intelligence system makes use of an
algorithm comprising
an artificial neural network, a decision tree, a logistic regression model, a
stochastic gradient
descent model, a fuzzy classifier, a support vector machine, a Bayesian
network, a hierarchical
clustering algorithm, a k-means algorithm, a genetic algorithm, a deep
learning system, a
supervised learning system, a semi-supervised learning system, a deep
convolutional neural
network, deep recurrent neural network or any combination thereof. In
embodiments the artificial
intelligence system (in any embodiments described herein) may use any of the
artificial
intelligence types described herein or in the documents incorporated herein by
reference. In
embodiments the artificial intelligence system (in any embodiments described
herein) may make
use of a training data set that may include, among other things, one or more
of: a set of expert
actions or operations upon information; process and/or workflow data; a set of
models of various
types; a set of outcomes (such as from additive manufacturing processes, from
utilization of
additive manufacturing outputs, from workflows and operations, and/or from
related economic
activities, including sales and service activities); a sensor data set;
information from public
information sources (such as search engine results, news feeds, website
information, social media
information, traffic data, weather data, climate data, demographic data,
geospatial data, and many
others); information from enterprise and other databases and information
technology systems;
information from crowdsourcing; Internet of Things information; and/or other
data sources and
inputs.
[0115] In embodiments, the distributed manufacturing network information
technology system
is configured to provide 3D printed products that conform to a body part or
anatomy of a user
wherein the 3D printed product is a wearable selected from a group consisting
of eyewear,
footwear, earwear and headgear.
[0116] An aspect provided herein includes an information technology system for
supporting
additive manufacturing and value chain workflows, comprising a cloud-based
metal additive
manufacturing management platform including an artificial intelligence system
configured to
learn on a training set of outcomes, parameters, and data collected from one
or more additive
manufacturing nodes to optimize additive manufacturing and value chain
processes and
workflows; and a distributed ledger system configured to store data related to
the manufacturing
nodes.
[0117] In embodiments, the artificial intelligence system learns on a training
set of outcomes,
parameters, and data collected from one or more additive manufacturing nodes
to optimize
process and material selection for additive manufacturing.
[0118] In embodiments, the artificial intelligence system learns on a training
set of outcomes,
parameters, and data collected from one or more additive manufacturing nodes
to optimize
formulation of feedstock for additive manufacturing.
[0119] In embodiments, the artificial intelligence system learns on a training
set of outcomes,
parameters, and data collected from one or more additive manufacturing nodes
to optimize part
design for additive manufacturing.
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[0120] In embodiments, the artificial intelligence system learns on a training
set of outcomes,
parameters, and data collected from one or more additive manufacturing nodes
to predict and
manage risk related to the manufacture or delivery of a part or product by the
one or more
manufacturing nodes to a customer.
[0121] In embodiments, the artificial intelligence system learns on a training
set of outcomes,
parameters, and data collected from one or more additive manufacturing nodes
to provide
personalized marketing and customer service with respect to a part or product
manufactured and
delivered by the one or more manufacturing nodes to a customer.
[0122] LIQUID LENS SUMMARY
[0123] Provided herein are methods, systems, components, processes, modules,
blocks, circuits,
sub-systems, articles, services, software, hardware, and other elements
(collectively referred to in
some cases as the "platform" or the "system," which terms should be understood
to encompass
any of the above except where context indicates otherwise) that individually
or collectively
improve the vision capabilities in a network of value chain entities in a
value chain network or
VCN (such terms encompassing the many examples and embodiments disclosed
herein and in
the documents incorporated by reference herein) for improving the vision
capabilities of the
VCN network.
[0124] An aspect provided herein includes a dynamic vision system having an
artificial
intelligence system for learning on a training set of outcomes, parameters,
and data collected
from a variable focus liquid lens optical assembly to recognize an object.
[0125] An aspect provided herein includes a dynamic vision system comprising:
a variable
focus liquid lens optical assembly; a control system configured to adjust one
or more optical
parameters and data collected from the optical assembly in real time; and a
processing system
that dynamically learns on a training set of outcomes, parameters and data
collected from the
optical assembly to train a machine learning model to recognize an object
and/or environment.
[0126] In embodiments, the variable focus liquid lens may be continuously
adjusted by the
control system based on environment factors and on feedback from the
processing system to
generate an object concept. In embodiments, the object concept includes
contextual intelligence
about the object and its environment and provides superior object recognition
by the dynamic
vision system.
[0127] In embodiments, the processing system may receive real-time, or near
real-time
adjustable data streams from the variable focus liquid lens optical assembly
to generate
situational awareness or create out-of-focus images of the object so as to
capture rich metadata
and contextual intelligence about the object and its environment.
[0128] In embodiments, the control system and the processing system may be
integrated with
the variable focus liquid lens optical assembly.
[0129] In embodiments, the optical parameters adjusted by the control system
include focal
length, liquid materials, specularity, color, environment, lens shape, or some
other type of
parameter which in turn impacts spherical aberration, field curvature, coma,
chromatic
aberration, distortion, vignetting, ghosting, flaring, diffraction, and/or
some other characteristic.
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[0130] In embodiments, the processing system may train on a set of outcomes,
parameters, and
data from the liquid lens optical assembly to derive the configuration of the
liquid lens optical
assembly wherein the configuration may include liquid lens materials,
geometry, shape, optical
properties, performance and design.
[0131] An aspect provided herein includes a robotic vision system comprising:
an optical
assembly including one or more sensors, a variable focus liquid lens and a
photon capture board;
and a processing system configured to dynamically learn on a training set of
outcomes,
parameters and data collected from the optical assembly to train an artificial
intelligence model to
recognize an object. In embodiments, the robotic vision system further
includes a control system
configured to adjust one or more optical parameters and data collected from
the optical assembly
in real time.
[0132] In embodiments, the artificial intelligence model is trained for
classification, predictions
or optimization related decisions about the object.
[0133] In embodiments, the artificial intelligence model may determine the
position, orientation
and motion of the object.
[0134] In embodiments, the artificial intelligence model may be a neural
network.
[0135] In embodiments, the artificial intelligence model may build a three-
dimensional
representation of the object in a single step, or plurality of steps, without
the intermediate step of
processing into two-dimensional images.
[0136] In embodiments, the one or more sensors may include cameras, LIDARs,
RADARs,
SONARs, thermal imaging sensor, hyperspectral imaging sensor, illuminance
sensors, force
sensors, torque sensors, velocity sensors, acceleration sensors, position
sensors, proximity
sensors, gyro sensors, sound sensors, motion sensors, location sensors, load
sensors, temperature
sensors, touch sensors, depth sensors, ultrasonic range sensors, infrared
sensors, chemical
sensors, magnetic sensors, inertial sensors, gas sensors, humidity sensors,
pressure sensors,
viscosity sensors, flow sensors, object sensors, tactile sensors, or some
other type of sensor.
[0137] In embodiments, the processing system may temporally combine an output
from two or
more sensors using conditional probabilities to create a combined view of the
object that is richer
and includes information about the position, orientation and motion of the
object.
[0138] An aspect provided herein includes vision system for dynamically
learning an object
concept about an object of interest: a variable focus liquid lens assembly; a
control system
configured to adjust one or more optical parameters of the variable focus
liquid lens assembly in
real time; one or more vision sensors configured to capture real-time pixel
array based on the
data received from the variable focus liquid lens assembly in response to
adjustments by the
control system, the pixel array representing the object concept; an adaptive
intelligence system
configured to process the object concept to build a three-dimensional
representation of the object,
the adaptive intelligence system including: a machine learning system
configured to input the
object concept into one or more machine learning models, the object concept
being used as
training data for the machine learning models; and an artificial intelligence
system configured to
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make classifications, predictions, and other decisions relating to the object
including determining
the position, orientation and motion of the object.
[0139] An aspect provided herein includes a method for recognizing an object,
comprising
receiving at a sensor, real time adjustable data streams representing visual
and contextual
information about an object of interest; generating by an image processing
system, an object
concept including contextual intelligence about the object and its
environment; adjusting by a
control system, optical parameters of a conformable liquid lens; revising by a
machine learning
system the object concept in response to the adjustment of optical parameters
of the conformable
liquid lens; determining by an artificial intelligence system, the object
attributes including object
classification, depth, location, orientation and motion where the object
concept is constantly
revised in response to the adjustment of optical parameters of the conformable
liquid lens and
used as an input to train a machine learning model, which dynamically learns
on a training set of
outcomes, parameters and data collected from the conformable liquid lens.
[0140] ROBO SUMMARY STARTS HERE
[0141] The present disclosure relates to a fleet management platform that can
organize, deploy,
and control special-purpose, multi-purpose, and other classes of robots. Such
a platform that can
securely deliver reliable contracted services is one key to unlocking the
value creation potential
of autonomous robotics. This value proposition may be amplified when highly
configurable
robots are designed with the latest functionality and enabled with a high
level of artificial
intelligence; when the platform is equipped with intelligence and computing
capabilities that
integrate data from a wide range of sources, including deployed robots, value
chain network
(VCN) entities involved in a wide range of supply chain activities (such as
picking, packing,
moving, storing, warehousing, transporting and/or delivering among others) and
demand-related
activities (such as marketing, selling, advertising, forecasting, pricing,
positioning, placing,
designing, and others), ERP systems, smart contracts, and the like; and when
the platform learns
from and manages performance based on operational outcomes.
[0142] A more complete understanding of the disclosure will be appreciated
from the
description and accompanying drawings and the claims, which follow. All
documents referenced
herein are hereby incorporated by reference.
BRIEF DESCRIPTION OF THE DRAWINGS
[0143] The accompanying drawings, which are included to provide a better
understanding of
the disclosure, illustrate embodiments of the disclosure and together with the
description serve to
explain the many aspects of the disclosure. In the drawings:
[0144] Fig. 1 is a block diagram showing prior art relationships of various
entities and facilities
in a supply chain.
[0145] Fig. 2 is a block diagram showing components and interrelationships of
systems and
processes of a value chain network in accordance with the present disclosure.
[0146] Fig. 3 is another block diagram showing components and
interrelationships of systems
and processes of a value chain network in accordance with the present
disclosure.
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[0147] Fig. 4 is a block diagram showing components and interrelationships of
systems and
processes of a digital products network of Figs. 2 and 3 in accordance with
the present disclosure.
[0148] Fig. 5 is a block diagram showing components and interrelationships of
systems and
processes of a value chain network technology stack in accordance with the
present disclosure.
[0149] Fig. 6 is a block diagram showing a platform and relationships for
orchestrating controls
of various entities in a value chain network in accordance with the present
disclosure.
[0150] Fig. 7 is a block diagram showing components and relationships in
embodiments of a
value chain network management platform in accordance with the present
disclosure.
[0151] Fig. 8 is a block diagram showing components and relationships of value
chain entities
managed by embodiments of a value chain network management platform in
accordance with the
present disclosure.
[0152] Fig. 9 is a block diagram showing network relationships of entities in
a value chain
network in accordance with the present disclosure.
[0153] Fig. 10 is a block diagram showing a set of applications supported by
unified data
handling layers in a value chain network management platform in accordance
with the present
disclosure.
[0154] Fig. 11 is a block diagram showing components and relationships in
embodiments of a
value chain network management platform in accordance with the present
disclosure.
[0155] Fig. 12 is a block diagram showing components and relationships of a
data storage layer
in embodiments of a value chain network management platform in accordance with
the present
disclosure.
[0156] Fig. 13 is a block diagram showing components and relationships of an
adaptive
intelligent systems layer in embodiments of a value chain network management
platform in
accordance with the present disclosure.
[0157] Fig. 14 is a block diagram that depicts providing adaptive intelligence
systems for
coordinated intelligence for sets of demand and supply applications for a
category of goods in
accordance with the present disclosure.
[0158] Fig. 15 is a block diagram that depicts providing hybrid adaptive
intelligence systems
for coordinated intelligence for sets of demand and supply applications or a
category of goods in
accordance with the present disclosure.
[0159] Fig. 16 is a block diagram that depicts providing adaptive intelligence
systems for
predictive intelligence for sets of demand and supply applications for a
category of goods in
accordance with the present disclosure.
[0160] Fig. 17 is a block diagram that depicts providing adaptive intelligence
systems for
classification intelligence for sets of demand and supply applications for a
category of goods in
accordance with the present disclosure.
[0161] Fig. 18 is a block diagram that depicts providing adaptive intelligence
systems to
produce automated control signals for sets of demand and supply applications
for a category of
goods in accordance with the present disclosure.
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[0162] Fig. 19 is a block diagram that depicts training artificial
intelligence/machine learning
systems to produce information routing recommendations for a selected value
chain network in
accordance with the present disclosure.
[0163] Fig. 20 is a block diagram that depicts a semi-sentient problem
recognition system for
recognition of pain points/problem states in a value chain network in
accordance with the present
disclosure.
[0164] Fig. 21 is a block diagram that depicts a set of artificial
intelligence systems operating
on value chain information to enable automated coordination of value chain
activities for an
enterprise in accordance with the present disclosure.
[0165] Fig. 22 is a block diagram showing components and relationships
involved in
integrating a set of digital twins in an embodiment of a value chain network
management
platform in accordance with the present disclosure.
[0166] Fig. 23 is a block diagram showing a set of digital twins involved in
embodiments of a
value chain network management platform in accordance with the present
disclosure.
[0167] Fig. 24 is a block diagram showing components and relationships of
entity discovery
and management systems in embodiments of a value chain network management
platform in
accordance with the present disclosure.
[0168] Fig. 25 is a block diagram showing components and relationships of a
robotic process
automation system in embodiments of a value chain network management platform
in
accordance with the present disclosure.
[0169] Fig. 26 is a block diagram showing components and relationships of a
set of opportunity
miners in an embodiment of a value chain network management platform in
accordance with the
present disclosure.
[0170] Fig. 27 is a block diagram showing components and relationships of a
set of edge
intelligence systems in embodiments of a value chain network management
platform in
accordance with the present disclosure.
[0171] Fig. 28 is a block diagram showing components and relationships in an
embodiment of a
value chain network management platform in accordance with the present
disclosure.
[0172] Fig. 29 is a block diagram showing additional details of components and
relationships in
embodiments of a value chain network management platform in accordance with
the present
disclosure.
[0173] Fig. 30 is a block diagram showing components and relationships in an
embodiment of a
value chain network management platform that enables centralized orchestration
of value chain
network entities in accordance with the present disclosure.
[0174] Fig. 31 is a block diagram showing components and relationships of a
unified database
in an embodiment of a value chain network management platform in accordance
with the present
disclosure.
[0175] Fig. 32 is a block diagram showing components and relationships of a
set of unified data
collection systems in embodiments of a value chain network management platform
in accordance
with the present disclosure.
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[0176] Fig. 33 is a block diagram showing components and relationships of a
set of Internet of
Things monitoring systems in embodiments of a value chain network management
platform in
accordance with the present disclosure.
[0177] Fig. 34 is a block diagram showing components and relationships of a
machine vision
system and a digital twin in embodiments of a value chain network management
platform in
accordance with the present disclosure.
[0178] Fig. 35 is a block diagram showing components and relationships of a
set of adaptive
edge intelligence systems in embodiments of a value chain network management
platform in
accordance with the present disclosure.
[0179] Fig. 36 is a block diagram showing additional details of components and
relationships of
a set of adaptive edge intelligence systems in embodiments of a value chain
network
management platform in accordance with the present disclosure.
[0180] Fig. 37 is a block diagram showing components and relationships of a
set of unified
adaptive intelligence systems in embodiments of a value chain network
management platform in
accordance with the present disclosure.
[0181] Fig. 38 is a schematic of a system configured to train an artificial
system that is
leveraged by a value chain system using real world outcome data and a digital
twin system
according to some embodiments of the present disclosure.
[0182] Fig. 39 is a schematic of a system configured to train an artificial
system that is
leveraged by a container fleet management system using real world outcome data
and a digital
twin system according to some embodiments of the present disclosure.
[0183] Fig. 40 is a schematic of a system configured to train an artificial
system that is
leveraged by a logistics design system using real world outcome data and a
digital twin system
according to some embodiments of the present disclosure.
[0184] Fig. 41 is a schematic of a system configured to train an artificial
system that is
leveraged by a packaging design system using real world outcome data and a
digital twin system
according to some embodiments of the present disclosure.
[0185] Fig. 42 is a schematic of a system configured to train an artificial
system that is
leveraged by a waste mitigation system using real world outcome data and a
digital twin system
according to some embodiments of the present disclosure.
[0186] Fig. 43 is a schematic illustrating an example of a portion of an
information technology
system for value chain artificial intelligence leveraging digital twins
according to some
embodiments of the present disclosure.
[0187] Fig. 44 is a block diagram showing components and relationships of a
set of intelligent
project management facilities in embodiments of a value chain network
management platform in
accordance with the present disclosure.
[0188] Fig. 45 is a block diagram showing components and relationships of an
intelligent task
recommendation system in embodiments of a value chain network management
platform in
accordance with the present disclosure.
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[0189] Fig. 46 is a block diagram showing components and relationships of a
routing system
among nodes of a value chain network in embodiments of a value chain network
management
platform in accordance with the present disclosure.
[0190] Fig. 47 is a block diagram showing components and relationships of a
dashboard for
managing a set of digital twins in embodiments of a value chain network
management platform.
[0191] Fig. 48 is a block diagram showing components and relationships in
embodiments of a
value chain network management platform that uses a microservices
architecture.
[0192] Fig. 49 is a block diagram showing components and relationships of an
Internet of
Things data collection architecture and sensor recommendation system in
embodiments of a
value chain network management platform.
[0193] Fig. 50 is a block diagram showing components and relationships of a
social data
collection architecture in embodiments of a value chain network management
platform.
[0194] Fig. 51 is a block diagram showing components and relationships of a
crowdsourcing
data collection architecture in embodiments of a value chain network
management platform.
[0195] Fig. 52 is a diagrammatic view that depicts embodiments of a set of
value chain network
digital twins representing virtual models of a set of value chain network
entities in accordance
with the present disclosure.
[0196] Fig. 53 is a diagrammatic view that depicts embodiments of a warehouse
digital twin kit
system in accordance with the present disclosure.
[0197] Fig. 54 is a diagrammatic view that depicts embodiments of a stress
test performed on a
value chain network in accordance with the present disclosure.
[0198] Fig. 55 is a diagrammatic view that depicts embodiments of methods used
by a machine
for detecting faults and predicting any future failures of the machine in
accordance with the
present disclosure.
[0199] Fig. 56 is a diagrammatic view that depicts embodiments of deployment
of machine
twins to perform predictive maintenance on a set of machines in accordance
with the present
disclosure.
[0200] Fig. 57 is a schematic illustrating an example of a portion of a system
for value chain
customer digital twins and customer profile digital twins according to some
embodiments of the
present disclosure.
[0201] Fig. 58 is a schematic illustrating an example of an advertising
application that
interfaces with the adaptive intelligent systems layer in accordance with the
present disclosure.
[0202] Fig. 59 is a schematic illustrating an example of an e-commerce
application integrated
with the adaptive intelligent systems layer in accordance with the present
disclosure.
[0203] Fig. 60 is a schematic illustrating an example of a demand management
application
integrated with the adaptive intelligent systems layer in accordance with the
present disclosure.
[0204] Fig. 61 is a schematic illustrating an example of a portion of a system
for value chain
smart supply component digital twins according to some embodiments of the
present disclosure.
[0205] Fig. 62 is a schematic illustrating an example of a risk management
application that
interfaces with the adaptive intelligent systems layer in accordance with the
present disclosure.
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[0206] Fig. 63 is a diagrammatic view of maritime assets associated with a
value chain network
management platform including components of a port infrastructure in
accordance with the
present disclosure.
[0207] Figs. 64 and 65 are diagrammatic views of maritime assets associated
with a value chain
.. network management platform including components of a ship in accordance
with the present
disclosure.
[0208] Fig. 66 is a diagrammatic view of maritime assets associated with a
value chain network
management platform including components of a barge in accordance with the
present
disclosure.
.. [0209] Fig. 67 is a diagrammatic view of maritime assets associated with a
value chain network
management platform including those involved in maritime events, legal
proceedings and making
use of geofenced parameters in accordance with the present disclosure.
[0210] Fig. 68 is a schematic illustrating an example environment of the
enterprise and
executive control tower and management platform, including data sources in
communication
therewith, according to some embodiments of the present disclosure.
[0211] Fig. 69 is a schematic illustrating an example set of components of the
enterprise control
tower and management platform according to some embodiments of the present
disclosure.
[0212] Fig. 70 is a schematic illustrating and example of an enterprise data
model according to
some embodiments of the disclosure.
[0213] Fig. 71 is a schematic illustrating examples of different types of
enterprise digital twins,
including executive digital twins, in relation to the data layer, processing
layer, and application
layer of the enterprise digital twin framework according to some embodiments
of the present
disclosure.
[0214] Fig. 72 is a schematic illustrating an example implementation of the
enterprise and
executive control tower and management platform according to some embodiments
of the present
disclosure.
[0215] Fig. 73 is a flow chart illustrating an example set of operations for
configuring and
serving an enterprise digital twin.
[0216] Fig. 74 illustrates an example set of operations of a method for
configuring an
organizational digital twin.
[0217] Fig. 75 illustrates an example set of operations of a method for
generating an executive
digital twin.
[0218] Fig. 76 through Fig. 103 are schematic diagrams of embodiments of
neural net systems
that may connect to, be integrated in, and be accessible by the platform for
enabling intelligent
transactions including ones involving expert systems, self-organization,
machine learning,
artificial intelligence and including neural net systems trained for pattern
recognition, for
classification of one or more parameters, characteristics, or phenomena, for
support of
autonomous control, and other purposes in accordance with embodiments of the
present
disclosure.
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[0219] Fig. 104 is a schematic illustrating an example intelligence services
system according to
some embodiments of the present disclosure.
[0220] Fig. 105 is a schematic illustrating an example neural network with
multiple layers
according to some embodiments of the present disclosure.
[0221] Fig. 106 is a schematic illustrating an example convolutional neural
network (CNN)
according to some embodiments of the present disclosure.
[0222] Fig. 107 is a schematic illustrating an example neural network for
implementing natural
language processing according to some embodiments of the present disclosure.
[0223] Fig. 108 is a schematic illustrating an example reinforcement learning-
based approach
for executing one or more tasks by a mobile system according to some
embodiments of the
present disclosure.
[0224] Fig. 109 is a schematic illustrating an example physical orientation
determination chip
according to some embodiments of the present disclosure.
[0225] Fig. 110 is a schematic illustrating an example network enhancement
chip according to
some embodiments of the present disclosure.
[0226] Fig. 111 is a schematic illustrating an example diagnostic chip
according to some
embodiments of the present disclosure.
[0227] Fig. 112 is a schematic illustrating an example governance chip
according to some
embodiments of the present disclosure.
.. [0228] Fig. 113 is a schematic illustrating an example prediction,
classification, and
recommendation chip according to some embodiments of the present disclosure.
[0229] Fig. 114 is a diagrammatic view illustrating an example environment of
an autonomous
additive manufacturing platform according to some embodiments of the present
disclosure.
[0230] Fig. 115 is a schematic illustrating an example implementation of an
autonomous
.. additive manufacturing platform for automating and optimizing the digital
production workflow
for metal additive manufacturing according to some embodiments of the present
disclosure.
[0231] Fig. 116 is a flow diagram illustrating the optimization of different
parameters of an
additive manufacture process according to some embodiments of the present
disclosure.
[0232] Fig. 117 is a schematic view illustrating a system for learning on data
from an
autonomous additive manufacturing platform to train an artificial learning
system to use digital
twins for classification, predictions and decision making according to some
embodiments of the
present disclosure.
[0233] Fig. 118 is a schematic illustrating an example implementation of an
autonomous
additive manufacturing platform including various components along with other
entities of a
.. distributed manufacturing network according to some embodiments of the
present disclosure.
[0234] Fig. 119 is a schematic illustrating an example implementation of an
autonomous
additive manufacturing platform for automating and managing manufacturing
functions and sub-
processes including process and material selection, hybrid part workflows,
feedstock
formulation, part design optimization, risk prediction and management,
marketing and customer
service according to some embodiments of the present disclosure.
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[0235] Fig. 120 is a diagrammatic view of a distributed manufacturing network
enabled by an
autonomous additive manufacturing platform and built on a distributed ledger
system according
to some embodiments of the present disclosure.
[0236] Fig. 121 is a schematic illustrating an example implementation of a
distributed
manufacturing network where the digital thread data is tokenized and stored in
a distributed
ledger so as to ensure traceability of parts printed at one or more
manufacturing nodes in the
distributed manufacturing network according to some embodiments of the present
disclosure.
[0237] Fig. 122 is a diagrammatic view illustrating an example implementation
of a
conventional computer vision system for creating an image of an object of
interest.
[0238] Fig. 123 is a schematic illustrating an example implementation of a
dynamic vision
system for dynamically learning an object concept about an object of interest
according to some
embodiments of the present disclosure.
[0239] Fig. 124 is a schematic illustrating an example architecture of a
dynamic vision system
according to some embodiments of the present disclosure.
[0240] Fig. 125 is a flow diagram illustrating a method for object recognition
by a dynamic
vision system according to some embodiments of the present disclosure.
[0241] Fig. 126 is a schematic illustrating an example implementation of a
dynamic vision
system for modelling, simulating and optimizing various optical, mechanical,
design and lighting
parameters of the dynamic vision system according to some embodiments of the
present
disclosure.
[0242] Fig. 127 is a schematic view illustrating an example implementation of
a dynamic vision
system depicting detailed view of various components along with integration of
the dynamic
vision system with one or more third party systems according to some
embodiments of the
present disclosure.
[0243] Fig. 128 is a schematic illustrating an example environment of a fleet
management
platform according to some embodiments of the present disclosure.
[0244] Fig. 129 is a schematic illustrating example configurations of a multi-
purpose robot and
a special purpose robot according to some embodiments of the present
disclosure.
[0245] Fig. 130 is a schematic illustrating an example platform-level
intelligence layer of a
fleet management platform according to some embodiments of the present
disclosure.
[0246] Fig. 131 is a schematic illustrating an example configuration of an
intelligence layer
according to some embodiments of the present disclosure.
[0247] Fig. 132 is a schematic illustrating an example security framework
according to some
embodiments of the present disclosure.
[0248] Fig. 133 is a schematic illustrating an example environment of a fleet
management
platform according to some embodiments of the present disclosure.
[0249] Fig. 134 is a schematic illustrating an example data flow of a job
configuration system
according to some embodiments of the present disclosure.
[0250] Fig. 135 is a schematic illustrating an example data flow of a fleet
operations system
according to some embodiments of the present disclosure.
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[0251] Fig. 136 is a schematic illustrating an example job parsing system and
task definition
system and an example data flow thereof according to some embodiments of the
present
disclosure.
[0252] Fig. 137 is a schematic illustrating an example fleet configuration
system and an
example data flow thereof according to some embodiments of the present
disclosure.
[0253] Fig. 138 is a schematic illustrating an example workflow definition
system and an
example data flow thereof according to some embodiments of the present
disclosure.
[0254] Fig. 139 is a schematic illustrating example configurations of a multi-
purpose robot and
components thereof according to some embodiments of the present disclosure.
[0255] Fig. 140 is a schematic illustrating an example architecture of the
robot control system
according to some embodiments of the present disclosure
[0256] Fig. 141 is a schematic illustrating an example architecture of the
robot control system
12150 that utilizes data from multiple sensors in the vision and sensing
system according to some
embodiments of the present disclosure.
[0257] Fig. 142 is a schematic illustrating an example vision and sensing
system of a robot
according to some embodiments of the present disclosure.
DETAILED DESCRIPTION
[0258] Over time, companies have increasingly used technology solutions to
improve outcomes
related to a traditional supply chain like the one depicted in Fig. 1, such as
software systems for
predicting and managing customer demand, RFID and asset tracking systems for
tracking goods
as they move through the supply chain, navigation and routing systems to
improve the efficiency
of route selection, and the like. However, some large trends have placed
manufacturers, retailers
and other businesses under increasing pressure to improve supply chain
performance. First,
online and ecommerce operators, in particular AmazonTM have become the largest
retail channels
for many categories of goods and have introduced distribution and fulfillment
centers 112
throughout some geographies like the United States that house hundreds of
thousands, and
sometimes more, product categories (SKUs), so that customers can receive items
the day after
they are ordered, and in some cases on the same day (and in some cases
delivered to the door by
a drone, robot, and/or autonomous vehicle. For retailers that do not have
extensive geographic
distribution of fulfillment centers or warehouses, customer expectations for
speed of delivery
place increased pressure on supply chain efficiency and optimization.
Accordingly, a need still
exists for improved supply chain methods and systems.
[0259] Second, agile manufacturing capabilities (such as using 3D printing and
robotic
assembly techniques, among others), customer profiling technologies, and
online ratings and
reviews have led to increased customer expectations for customization and
personalization of
products. Accordingly, in order to compete, manufacturers and retailers need
improved methods
and systems for understanding, predicting, and satisfying customer demand.
[0260] Historically, supply chain management and demand planning and
management have
been largely separate activities, unified primarily when demand is converted
to an order, which is
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passed to the supply side for fulfillment in a supply chain. As expectations
for speed and
personalization increase, a need exists for methods and systems that can
provide unified
orchestration of supply and demand.
[0261] In parallel with these other large trends has been the emergence of the
Internet of
Things, in which some categories of products, particularly smart home products
like thermostats,
lighting systems, and speakers, are increasingly enabled with onboard network
connectivity and
processing capability, often including a voice controlled intelligent agent
like AlexaTM or SiriTM
that allows device control and triggering of certain application features,
such as playing music, or
even ordering a product. In some cases, smart products 650 even initiate
orders, such as printers
that order refill cartridges. Intelligent products 650 are in some cases
involved in a coordinated
system, such as where an AmazonTM EchoTM product controls a television, or
where a sensor-
enabled thermostat or security camera connects to a mobile device, but most
intelligent products
are still involved in sets of largely isolated, application-specific
interactions. As artificial
intelligence capabilities increase, and as more and more computing and
networking power is
moved to network-enabled edge devices and systems that reside in supply
environments 670, in
demand environments 672, and in all of the locations, systems, and facilities
that populate the
path of a product 650 from the loading dock of a manufacturer to the point of
destination 612 of a
customer 662 or retailers 664, a need and opportunity exists for dramatically
improved
intelligence, control, and automation of all of the factors involved in demand
and supply.
VALUE CHAIN NETWORKS
[0262] Referring to Fig. 2, a block diagram is presented at 200 showing
components and
interrelationships of systems and processes of a value chain network. In
example embodiments,
"value chain network," as used herein, refers to elements and interconnections
of historically
segregated demand management systems and processes and supply chain management
systems
and processes, enabled by the development and convergence of numerous diverse
technologies.
In example embodiments a value chain control tower 260 (e.g., referred to
herein in some cases
as a "value chain network management platform", a "VCNP", or simply as "the
system", or "the
platform") may be connected to, in communication with, or otherwise
operatively coupled with
data processing facilities including, but not limited to, big data centers
(e.g., big data processing
230) and related processing functionalities that receive data flow, data
pools, data streams and/or
other data configurations and transmission modalities received from, for
example, digital product
networks 252, directly from customers (e.g., direct connected customer 250),
or some other third
party 220. Communications related to market orchestration activities and
communications 210,
analytics 232, or some other type of input may also be utilized by the value
chain control tower
for demand enhancement 262, synchronized planning 234, intelligent procurement
238, dynamic
fulfillment 240 or some other smart operation informed by coordinated and
adaptive intelligence,
as described herein.
[0263] Referring to Fig. 3, another block diagram is presented showing
components and
interrelationships of systems and processes of a value chain network and
related uses cases, data
handling, and associated entities. In example embodiments, the value chain
control tower 360
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may coordinate market orchestration activities 310 including, but not limited
to, demand curve
management 352, synchronization of an ecosystem 348, intelligent procurement
344, dynamic
fulfillment 350, value chain analytics 340, and/or smart supply chain
operations 342. In example
embodiments, the value chain control tower 360 may be connected to, in
communication with, or
otherwise operatively coupled with adaptive data pipelines 302 and processing
facilities that may
be further connected to, in communication with, or otherwise operationally
coupled with external
data sources 320 and a data handling stack 330 (e.g., value chain network
technology) that may
include intelligent, user-adaptive interfaces, adaptive intelligence and
control 332, and/or
adaptive data monitoring and storage 334, as described herein. The value chain
control tower 302
may also be further connected to, in communication with, or otherwise
operatively coupled with
additional value chain entities including, but not limited to, digital product
networks 360,
customers (e.g., directed connected customers 362), and/or other connected
operations 364 and
entities of a value chain network.
DIGITAL PRODUCT NETWORKS ("DPN")
[0264] Referring to Fig. 4, a block diagram is presented showing components
and
interrelationships of systems and processes of the digital products networks
at 400. In example
embodiments, products (including goods and services) may create and transmit
data, such as
product level data, to a communication layer within the value chain network
technology stack
and/or to an edge data processing facility. This data may produce enhanced
product level data
and may be combined with third party data for further processing, modeling or
other adaptive or
coordinated intelligence activity, as described herein. This may include, but
is not limited to,
producing and/or simulating product and value chain use cases, the data for
which may be
utilized by products, product development processes, product design, and the
like.
STACK VIEW EXAMPLES
[0265] Referring to Fig. 5, a block diagram is presented at 500 showing
components and
interrelationships of systems and processes of a value chain network
technology stack, which
may include, but is not limited to a presentation layer, an intelligence
layer, and serverless
functionalities such as platforms (e.g., development and hosting platforms),
data facilities (e.g.,
relating to data with IoT and Big Data), and data aggregation facilities. In
example embodiments,
the presentation layer may include, but is not limited to, a user interface,
and modules for
investigation and discovery and tracking users' experience and engagements. In
example
embodiments, the intelligence layer may include, but is not limited to, a
statistical and
computation methods, semantic models, an analytics library, a development
environment for
analytics, algorithms, logic and rules, and machine learning. In example
embodiments, the
platforms or the value chain network technology stack may include a
development environment,
APIs for connectivity, cloud and/or hosting applications, and device
discovery. In example
embodiments, the data aggregation facilities or layer may include, but is not
limited to, modules
for data normalization for common transmission and heterogeneous data
collection from
disparate devices. In example embodiments, the data facilities or layer may
include, but is not
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limited to, IoT and big data access, control, and collection and alternatives.
In example
embodiments, the value chain network technology stack may be further
associated with
additional data sources and/or technology enablers.
VALUE CHAIN ORCHESTRATION FROM A COMMAND PLATFORM
[0266] Fig. 6 illustrates a connected value chain network 668 in which a value
chain network
management platform 604 (referred to herein in some cases as a "value chain
control tower," the
"VCNP," or simply as "the system," or "the platform") orchestrates a variety
of factors involved
in planning, monitoring, controlling, and optimizing various entities and
activities involved in the
value chain network 668, such as supply and production factors, demand
factors, logistics and
distribution factors, and the like. By virtue of a unified platform 604 for
monitoring and
managing supply factors and demand factors as well as status information
(e.g., quality and
status, plan, order and confirm, and/or track and trace) can be shared about
and between various
entities (e.g., including customers/consumers, suppliers, distribution such as
distributors,
suppliers, and production such as producers or production facilities) as
demand factors are
understood and accounted for, as orders are generated and fulfilled, and as
products are created
and moved through a supply chain. The value chain network 668 may include not
only an
intelligent product 650, but all of the equipment, infrastructure, personnel
and other entities
involved in planning and satisfying demand for it.
VALUE CHAIN NETWORK AND VALUE CHAIN NETWORK MANAGEMENT PLATFORM
[0267] Referring to Fig. 7, the value chain network 668 managed by a value
chain management
platform 604 may include a set of value chain network entities 652, such as,
without limitation: a
product 650, which may be an intelligent product 650; a set of production
facilities 674 involved
in producing finished goods, components, systems, sub-systems, materials used
in goods, or the
like; various entities, activities and other supply factors 648 involved in
supply environments
670, such as suppliers 642, points of origin 610, and the like; various
entities, activities and other
demand factors 644 involved in demand environments 672, such as customers 662
(including
consumers, businesses, and intermediate customers such as value added
resellers and
distributors), retailers 664 (including online retailers, mobile retailers,
conventional bricks and
mortar retailers, pop-up shops and the like) and the like located and/or
operating at various
destinations 612; various distribution environments 678 and distribution
facilities 658, such as
warehousing facilities 654, fulfillment facilities 628, and delivery systems
632, and the like, as
well as maritime facilities 622, such as port infrastructure facilities 660,
floating assets 620, and
shipyards 638, among others. In embodiments, the value chain network
management platform
604 monitors, controls, and otherwise enables management (and in some cases
autonomous or
semi-autonomous behavior) of a wide range of value chain network 668
processes, workflows,
activities, events and applications 630 (collectively referred to in some
cases simply as
"applications 630").
[0268] Referring still to Fig. 7, a high-level schematic of the value chain
network management
platform 604 is illustrated. The value chain network management platform 604
may include a set
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of systems, applications, processes, modules, services, layers, devices,
components, machines,
products, sub-systems, interfaces, connections, and other elements working in
coordination to
enable intelligent management of a set of value chain entities 652 that may
occur, operate,
transact or the like within, or own, operate, support or enable, one or more
value chain network
processes, workflows, activities, events and/or applications 630 or that may
otherwise be part of,
integrated with, linked to, or operated on by the VCNP 604 in connection with
a product 650
(which may be any category of product, such as a finished good, software
product, hardware
product, component product, material, item of equipment, item of consumer
packaged goods,
consumer product, food product, beverage product, home product, business
supply product,
consumable product, pharmaceutical product, medical device product, technology
product,
entertainment product, or any other type of product and/or set of related
services, and which may,
in embodiments, encompass an intelligent product 650 that is enabled with a
set of capabilities
such as, without limitation data processing, networking, sensing, autonomous
operation,
intelligent agent, natural language processing, speech recognition, voice
recognition, touch
interfaces, remote control, self-organization, self-healing, process
automation, computation,
artificial intelligence, analog or digital sensors, cameras, sound processing
systems, data storage,
data integration, and/or various Internet of Things capabilities, among
others.
[0269] In embodiments, the management platform 604 may include a set of data
handling
layers 624 each of which is configured to provide a set of capabilities that
facilitate development
and deployment of intelligence, such as for facilitating automation, machine
learning,
applications of artificial intelligence, intelligent transactions, state
management, event
management, process management, and many others, for a wide variety of value
chain network
applications and end uses. In embodiments, the data handling layers 624 are
configured in a
topology that facilitates shared data collection and distribution across
multiple applications and
uses within the platform 604 by a value chain monitoring systems layer 614.
The value chain
monitoring systems layer 614 may include, integrate with, and/or cooperate
with various data
collection and management systems 640, referred to for convenience in some
cases as data
collection systems 640, for collecting and organizing data collected from or
about value chain
entities 652, as well as data collected from or about the various data layers
624 or services or
components thereof. In embodiments, the data handling layers 624 are
configured in a topology
that facilitates shared or common data storage across multiple applications
and uses of the
platform 604 by a value chain network-oriented data storage systems layer 624,
referred to herein
for convenience in some cases simply as a data storage layer 624 or storage
layer 624. As shown
in Fig. 7, the data handling layers 624 may also include an adaptive
intelligent systems layer 614.
The adaptive intelligence systems layer 614 may include a set of data
processing, artificial
intelligence and computational systems 634 that are described in more detail
elsewhere
throughout this disclosure. The data processing, artificial intelligence and
computational systems
634 may relate to artificial intelligence (e.g., expert systems, artificial
intelligence, neural,
supervised, machine learning, deep learning, model-based systems, and the
like). Specifically,
the data processing, artificial intelligence and computational systems 634 may
relate to various
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examples, in some embodiments, such as use of a recurrent network as adaptive
intelligence
system operating on a blockchain of transactions in a supply chain to
determine a pattern, use
with biological systems, opportunity mining (e.g., where artificial
intelligence system may be
used to monitor for new data sources as opportunities for automatically
deploying intelligence),
robotic process automation (e.g., automation of intelligent agents for various
workflows), edge
and network intelligence (e.g., implicated on monitoring systems such as
adaptively using
available RF spectrum, adaptively using available fixed network spectrum,
adaptively storing
data based on available storage conditions, adaptively sensing based on a kind
of contextual
sensing), and the like.
[0270] In embodiments, the data handling layers 624 may be depicted in
vertical stacks or
ribbons in the figures and may represent many functionalities available to the
platform 604
including storage, monitoring, and processing applications and resources and
combinations
thereof. In embodiments, the set of capabilities of the data handling layers
624 may include a
shared microservices architecture. By way of these examples, the set of
capabilities may be
deployed to provide multiple distinct services or applications, which can be
configured as one or
more services, workflows, or combinations thereof. In some examples, the set
of capabilities may
be deployed within or be resident to certain applications or processes. In
some examples, the set
of capabilities can include one or more activities marshaled for the benefit
of the platform. In
some examples, the set of capabilities may include one or more events
organized for the benefit
.. of the platform. In embodiments, one of the sets of capabilities of the
platform may be deployed
within at least a portion of a common architecture such as common architecture
that supports a
common data schema. In embodiments, one of the sets of capabilities of the
platform may be
deployed within at least a portion of a common architecture that can support a
common storage.
In embodiments, one of the sets of capabilities of the platform may be
deployed within at least a
portion of a common architecture that can support common monitoring systems.
In embodiments,
one or more sets of capabilities of the platform may be deployed within at
least a portion of a
common architecture that can support one or more common processing frameworks.
In
embodiments, the set of capabilities of the data handling layers 624 can
include examples where
the storage functionality supports scalable processing capabilities, scalable
monitoring systems,
digital twin systems, payments interface systems, and the like. By way of
these examples, one or
more software development kits can be provided by the platform along with
deployment
interfaces to facilitate connections and use of the capabilities of the data
handling layers 624. In
further examples, adaptive intelligence systems may analyze, learn, configure,
and reconfigure
one or more of the capabilities of the data handling layers 624. In
embodiments, the platform 604
.. may, for example, include a common data storage schema serving a shipyard
entity related
service and a warehousing entity service. There are many other applicable
examples and
combinations applicable to the foregoing example including the many value
chain entities
disclosed herein. By way of these examples, the platform 604 may be shown to
create
connectivity (e.g., supply of capabilities and information) across many value
chain entities. In
many examples, there are pairings (doubles, triples, quadruplets, etc.) of
similar kinds of value
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chain entities using one or more smaller sets of capabilities of the data
handling layers 624 to
deploy (interact with, rely on, etc.) a common data schema, a common
architecture, a common
interface, and the like. While services and capabilities can be provided to
single value chain
entities, the platform can be shown to provide myriad benefits to value chains
and consumers by
supporting connectivity across value chain entities and applications used by
the entities.
VALUE CHAIN NETWORK ENTITIES MANAGED BY THE PLATFORM
[0271] Referring to Fig. 8, the value chain network management platform 604 is
illustrated in
connection with a set of value chain entities 652 that may be subject to
management by the
platform 604, may integrate with or into the platform 604, and/or may supply
inputs to and/or
take outputs from the platform 604, such as ones involved in or for a wide
range of value chain
activities (such as supply chain activities, logistics activities, demand
management and planning
activities, delivery activities, shipping activities, warehousing activities,
distribution and
fulfillment activities, inventory aggregation, storage and management
activities, marketing
activities, and many others, as involved in various value chain network
processes, workflows,
activities, events and applications 630 (collectively "applications 630" or
simply "activities")).
Connections with the value chain entities 652 may be facilitated by a set of
connectivity facilities
642 and interfaces 702, including a wide range of components and systems
described throughout
this disclosure and in greater detail below. This may include connectivity and
interface
capabilities for individual services of the platform, for the data handling
layers, for the platform
as a whole, and/or among value chain entities 652, among others.
[0272] These value chain entities 652 may include any of the wide variety of
assets, systems,
devices, machines, components, equipment, facilities, individuals or other
entities mentioned
throughout this disclosure or in the documents incorporated herein by
reference, such as, without
limitation: machines 724 and their components (e.g., delivery vehicles,
forklifts, conveyors,
loading machines, cranes, lifts, haulers, trucks, loading machines, unloading
machines, packing
machines, picking machines, and many others, including robotic systems, e.g.,
physical robots,
collaborative robots (e.g., "cobots"), drones, autonomous vehicles, software
bots and many
others); products 650 (which may be any category of products, such as a
finished goods, software
products, hardware products, component products, material, items of equipment,
items of
consumer packaged goods, consumer products, food products, beverage products,
home
products, business supply products, consumable products, pharmaceutical
products, medical
device products, technology products, entertainment products, or any other
type of products
and/or set of related services); value chain processes 722 (such as shipping
processes, hauling
processes, maritime processes, inspection processes, hauling processes,
loading/unloading
processes, packing/unpacking processes, configuration processes, assembly
processes,
installation processes, quality control processes, environmental control
processes (e.g.,
temperature control, humidity control, pressure control, vibration control,
and others), border
control processes, port-related processes, software processes (including
applications, programs,
services, and others), packing and loading processes, financial processes
(e.g., insurance
processes, reporting processes, transactional processes, and many others),
testing and diagnostic
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processes, security processes, safety processes, reporting processes, asset
tracking processes, and
many others); wearable and portable devices 720 (such as mobile phones,
tablets, dedicated
portable devices for value chain applications and processes, data collectors
(including mobile
data collectors), sensor-based devices, watches, glasses, hearables, head-worn
devices, clothing-
integrated devices, arm bands, bracelets, neck-worn devices, AR/VR devices,
headphones, and
many others); workers 718 (such as delivery workers, shipping workers, barge
workers, port
workers, dock workers, train workers, ship workers, distribution of
fulfillment center workers,
warehouse workers, vehicle drivers, business managers, engineers, floor
managers, demand
managers, marketing managers, inventory managers, supply chain managers, cargo
handling
workers, inspectors, delivery personnel, environmental control managers,
financial asset
managers, process supervisors and workers (for any of the processes mentioned
herein), security
personnel, safety personnel and many others); suppliers 642 (such as suppliers
of goods and
related services of all types, component suppliers, ingredient suppliers,
materials suppliers,
manufacturers, and many others); customers 662 (including consumers,
licensees, businesses,
enterprises, value added and other resellers, retailers, end users,
distributors, and others who may
purchase, license, or otherwise use a category of goods and/or related
services); a wide range of
operating facilities 712 (such as loading and unloading docks, storage and
warehousing facilities
654, vaults, distribution facilities 658 and fulfillment centers 628, air
travel facilities 740
(including aircraft, airports, hangars, runways, refueling depots, and the
like), maritime facilities
622 (such as port infrastructure facilities 622 (such as docks, yards, cranes,
roll-on/roll-off
facilities, ramps, containers, container handling systems, waterways 732,
locks, and many
others), shipyard facilities 638, floating assets 620 (such as ships, barges,
boats and others),
facilities and other items at points of origin 610 and/or points of
destination 628, hauling
facilities 710 (such as container ships, barges, and other floating assets
620, as well as land-based
vehicles and other delivery systems 632 used for conveying goods, such as
trucks, trains, and the
like); items or elements factoring in demand (i.e., demand factors 644)
(including market factors,
events, and many others); items or elements factoring in supply (i.e., supply
factors
648)(including market factors, weather, availability of components and
materials, and many
others); logistics factors 750 (such as availability of travel routes,
weather, fuel prices, regulatory
factors, availability of space (such as on a vehicle, in a container, in a
package, in a warehouse, in
a fulfillment center, on a shelf, or the like), and many others); retailers
664 (including online
retailers 730 and others such as in the form of eCommerce sites 730); pathways
for conveyance
(such as waterways 732, roadways 734, air travel routes, railways 738 and the
like); robotic
systems 744 (including mobile robots, cobots, robotic systems for assisting
human workers,
robotic delivery systems, and others); drones 748 (including for package
delivery, site mapping,
monitoring or inspection, and the like); autonomous vehicles 742 (such as for
package delivery);
software platforms 752 (such as enterprise resource planning platforms,
customer relationship
management platforms, sales and marketing platforms, asset management
platforms, Internet of
Things platforms, supply chain management platforms, platform as a service
platforms,
infrastructure as a service platforms, software-based data storage platforms,
analytic platforms,
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artificial intelligence platforms, and others); and many others. In some
example embodiments,
the product 650 may be encompassed as an intelligent product 650 or the VCNP
604 may include
the intelligent product 650. The intelligent product 650 may be enabled with a
set of capabilities
such as, without limitation data processing, networking, sensing, autonomous
operation,
intelligent agent, natural language processing, speech recognition, voice
recognition, touch
interfaces, remote control, self-organization, self-healing, process
automation, computation,
artificial intelligence, analog or digital sensors, cameras, sound processing
systems, data storage,
data integration, and/or various Internet of Things capabilities, among
others. The intelligent
product 650 may include a form of information technology. The intelligent
product 650 may
have a processor, computer random access memory, and a communication module.
The
intelligent product 650 may be a passive intelligent product that is similar
to a RFID type of data
structure where the intelligent product may be pinged or read. The product 650
may be
considered a value chain network entity (e.g., under control of platform) and
may be rendered
intelligent by surrounding infrastructure and adding an RFID such that data
may be read from the
intelligent product 650. The intelligent product 650 may fit in a value chain
network in a
connected way such that connectivity was built around the intelligent product
650 through a
sensor, an IoT device, a tag, or another component.
[0273] In embodiments, the monitoring systems layer 614 may monitor any or all
of the value
chain entities 652 in a value chain network 668, may exchange data with the
value chain entities
652, may provide control instructions to or take instructions from any of the
value chain entities
652, or the like, such as through the various capabilities of the data
handling layers 624 described
throughout this disclosure.
NETWORK CHARACTERISTICS OF THE VALUE CHAIN NETWORK ENTITIES
[0274] Referring to Fig. 9, orchestration of a set of deeply interconnected
value chain network
entities 652 in a value chain network 668 by the value chain network
management platform 604
is illustrated. Each of the value chain network entities 652 may have a
connection to the VCNP
604, to a set of other value chain network entities 652 (which may be a local
network connection,
a peer-to-peer connection, a mobile network connection, a connection via a
cloud, or other
connection), and/or through the VCNP 604 to other value chain network entities
652. The value
chain network management platform 604 may manage the connections, configure or
provision
resources to enable connectivity, and/or manage applications 630 that take
advantage of the
connections, such as by using information from one set of entities 652 to
inform applications 630
involving another set of entities 652, by coordinating activities of a set of
entities 652, by
providing input to an artificial intelligence system of the VCNP 604 or of or
about a set of
entities 652, by interacting with edge computation systems deployed on or in
entities 652 and
their environments, and the like.
[0275] The entities 652 may be external such that the VCNP 604 may interact
with these
entities 652. When the VCNP 604 functions as the control tower to establish
monitoring (e.g.,
establish monitoring such as common monitoring across several entities 652).
In one unified
platform, there may be an interface where a user may view various items such
as user's
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destinations, ports, air and rail assets, as well as orders, etc. Then, the
next step may be to
establish a common data schema that enables services that work on or in any
one of these
applications. This may involve taking any of the data that is flowing through
or about any of
these entities 652 and pull the data into a framework where other applications
across supply and
demand may interact with the entities 652. This may be a shared data pipeline
coming from an
IoT system and other external data sources, feeding into the monitoring layer,
being stored in a
common data schema in the storage layer, and then various intelligence may be
trained to
identify implications across these entities 652. In an example embodiment, a
supplier may be
bankrupt, or a determination is made that the supplier is bankrupt, and then
the VCNP 604 may
automatically trigger a substitute smart contract to be sent to a secondary
supplier with altered
terms. There may be management of different aspects of the supply chain. For
example, changing
pricing instantly and automatically on the demand side in response to one more
supplier's being
identified as bankrupt (e.g., from bankruptcy announcement). Other similar
examples may be
used based on what occurs in that automation layer which may be enabled by the
VCNP 604.
Then, at the interface layer of this VCNP 604, a digital twin may be used by
user to view all
these entities 652 that are not typically shown together and monitor what is
going on with each of
these entities 652 including identification of problem states. For example,
after viewing three
quarters of bad financial reports on a supplier, a report may be flagged to
watch it closely for
potential future bankruptcy, etc.
[0276] For example, an IoT system deployed in a fulfillment center 628 may
coordinate with an
intelligent product 650 that takes customer feedback about the product 650,
and an application
630 for the fulfillment center 628 may, upon receiving customer feedback via a
connection path
to the intelligent product 650 about a problem with the product 650, initiate
a workflow to
perform corrective actions on similar products 650 before the products 650 are
sent out from the
fulfillment center 628. Similarly, a port infrastructure facility 660, such as
a yard for holding
shipping containers, may inform a fleet of floating assets 620 via connections
to the floating
assets 620 (such as ships, barges, or the like) that the port is near
capacity, thereby kicking off a
negotiation process (which may include an automated negotiation based on a set
of rules and
governed by a smart contract) for the remaining capacity and enabling some
assets 620 to be
redirected to alternative ports or holding facilities. These and many other
connections among
value chain network entities 652, whether one-to-one connections, one-to-many
connections,
many-to-many connections, or connections among defined groups of entities 652
(such as ones
controlled by the same owner or operator), are encompassed herein as
applications 630 managed
by the VCNP 604.
VALUE CHAIN NETWORK ACTIVITIES AND APPLICATIONS MANAGED BY THE PLATFORM
[0277] Referring to Fig. 10, the set of applications 630 provided on the VCNP
604, integrated
with the VCNP 604 and/or managed by or for the VCNP 604 and/or involving a set
of value
chain network entities 652 may include, without limitation, one or more of any
of a wide range of
types of applications, such as: a supply chain management application 812
(such as, without
limitation, for management of timing, quantities, logistics, shipping,
delivery, and other details of
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orders for goods, components, and other items); an asset management
application 814 (such as,
without limitation, for managing value chain assets, such as floating assets
(such as ships, boats,
barges, and floating platforms), real property (such as used for location of
warehouses, ports,
shipyards, distribution centers and other buildings), equipment, machines and
fixtures (such as
used for handling containers, cargo, packages, goods, and other items),
vehicles (such as
forklifts, delivery trucks, autonomous vehicles, and other systems used to
move items), human
resources (such as workers), software, information technology resources, data
processing
resources, data storage resources, power generation and/or storage resources,
computational
resources and other assets); a finance application 822 (such as, without
limitation, for handling
finance matters relating to value chain entities and assets, such as involving
payments, security,
collateral, bonds, customs, duties, imposts, taxes and others); a risk
management application 818
(such as, without limitation, for managing risk or liability with respect to a
shipment, goods, a
product, an asset, a person, a floating asset, a vehicle, an item of
equipment, a component, an
information technology system, a security system, a security event, a
cybersecurity system, an
item of property, a health condition, mortality, fire, flood, weather,
disability, negligence,
business interruption, injury, damage to property, damage to a business,
breach of a contract, and
others); a demand management application 824 (such as, without limitation, an
application for
analyzing, planning, or promoting interest by customers of a category of goods
that can be
supplied by or with facilities of a value chain product or service, such as a
demand planning
application, a demand prediction application, a sales application, a future
demand aggregation
application, a marketing application, an advertising application, an e-
commerce application, a
marketing analytics application, a customer relationship management
application, a search engine
optimization application, a sales management application, an advertising
network application, a
behavioral tracking application, a marketing analytics application, a location-
based product or
service-targeting application, a collaborative filtering application, a
recommendation engine for a
product or service, and others, including ones that use or are enabled by one
or more features of
an intelligent product 650 or that are executed using intelligence
capabilities on an intelligent
product 650); a trading application 858 (such as, without limitation, a buying
application, a
selling application, a bidding application, an auction application, a reverse
auction application, a
bid/ask matching application, an analytic application for analyzing value
chain performance,
yield, return on investment, or other metrics, or others); a tax application
850 (such as, without
limitation, for managing, calculating, reporting, optimizing, or otherwise
handling data, events,
workflows, or other factors relating to a tax, a tariff, an impost, a levy, a
tariff, a duty, a credit, a
fee or other government-imposed charge, such as, without limitation, customs
duties, value added
tax, sales tax, income tax, property tax, municipal fees, pollution tax,
renewal energy credit,
pollution abatement credit, import duties, export duties, and others); an
identity management
application 830 (such as for managing one or more identities of entities 652
involved in a value
chain, such as, without limitation, one or more of an identity verification
application, a biometric
identify validation application, a pattern-based identity verification
application, a location-based
identity verification application, a user behavior-based application, a fraud
detection application,
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a network address-based fraud detection application, a black list application,
a white list
application, a content inspection-based fraud detection application, or other
fraud detection
application; an inventory management application 820 (such as, without
limitation, for managing
inventory in a fulfillment center, distribution center, warehouse, storage
facility, store, port, ship
or other floating asset, or other location); a security application, solution
or service 834 (referred
to herein as a security application, such as, without limitation, any of the
identity management
applications 830 noted above, as well as a physical security system (such as
for an access control
system (such as using biometric access controls, fingerprinting, retinal
scanning, passwords, and
other access controls), a safe, a vault, a cage, a safe room, a secure storage
facility, or the like), a
monitoring system (such as using cameras, motion sensors, infrared sensors and
other sensors), a
perimeter security system, a floating security system for a floating asset, a
cyber security system
(such as for virus detection and remediation, intrusion detection and
remediation, spam detection
and remediation, phishing detection and remediation, social engineering
detection and
remediation, cyber-attack detection and remediation, packet inspection,
traffic inspection, DNS
attack remediation and detection, and others) or other security application);
a safety application
840 (such as, without limitation, for improving safety of workers, for
reducing the likelihood of
damage to property, for reducing accident risk, for reducing the likelihood of
damage to goods
(such as cargo), for risk management with respected to insured items,
collateral for loans, or the
like, including any application for detecting, characterizing or predicting
the likelihood and/or
scope of an accident or other damaging event, including safety management
based on any of the
data sources, events or entities noted throughout this disclosure or the
documents incorporated
herein by reference); a blockchain application 844 (such as, without
limitation, a distributed
ledger capturing a series of transactions, such as debits or credits,
purchases or sales, exchanges
of in kind consideration, smart contract events, or the like, or other
blockchain-based
application); a facility management application 850 (such as, without
limitation, for managing
infrastructure, buildings, systems, real property, personal property, and
other property involved in
supporting a value chain, such as a shipyard, a port, a distribution center, a
warehouse, a dock, a
store, a fulfillment center, a storage facility, or others, as well as for
design, management or
control of systems and facilities in or around a property, such as an
information technology
system, a robotic/autonomous vehicle system, a packaging system, a packing
system, a picking
system, an inventory tracking system, an inspection system, a routing system
for mobile robots, a
workflow system for human assets, or the like); a regulatory application 852
(such as, without
limitation, an application for regulating any of the applications, services,
transactions, activities,
workflows, events, entities, or other items noted herein and in the documents
incorporated by
reference herein, such as regulation of permitted routes, permitted cargo and
goods, permitted
parties to transactions, required disclosures, privacy, pricing, marketing,
offering of goods and
services, use of data (including data privacy regulations, regulations
relating to storage of data
and others), banking, marketing, sales, financial planning, and many others);
a commerce
application, solution or service 854 (such as, without limitation an e-
commerce site marketplace,
an online site, an auction site or marketplace, a physical goods marketplace,
an advertising
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marketplace, a reverse-auction marketplace, an advertising network, or other
marketplace); a
vendor management application 832 (such as, without limitation, an application
for managing a
set of vendors or prospective vendors and/or for managing procurement of a set
of goods,
components or materials that may be supplied in a value chain, such as
involving features such as
vendor qualification, vendor rating, requests for proposal, requests for
information, bonds or
other assurances of performance, contract management, and others); an
analytics application 838
(such as, without limitation, an analytic application with respect to any of
the data types,
applications, events, workflows, or entities mentioned throughout this
disclosure or the
documents incorporated by reference herein, such as a big data application, a
user behavior
application, a prediction application, a classification application, a
dashboard, a pattern
recognition application, an econometric application, a financial yield
application, a return on
investment application, a scenario planning application, a decision support
application, a demand
prediction application, a demand planning application, a route planning
application, a weather
prediction application, and many others); a pricing application 842 (such as,
without limitation,
for pricing of goods, services (including any mentioned throughout this
disclosure and the
documents incorporated by reference herein; and a smart contract application,
solution, or service
(referred to collectively herein as a smart contract application 848, such as,
without limitation,
any of the smart contract types referred to in this disclosure or in the
documents incorporated
herein by reference, such as a smart contract for sale of goods, a smart
contract for an order for
goods, a smart contract for a shipping resource, a smart contract for a
worker, a smart contract for
delivery of goods, a smart contract for installation of goods, a smart
contract using a token or
cryptocurrency for consideration, a smart contract that vests a right, an
option, a future, or an
interest based on a future condition, a smart contract for a security,
commodity, future, option,
derivative, or the like, a smart contract for current or future resources, a
smart contract that is
configured to account for or accommodate a tax, regulatory or compliance
parameter, a smart
contract that is configured to execute an arbitrage transaction, or many
others). Thus, the value
chain management platform 604 may host an enable interaction among a wide
range of disparate
applications 630 (such term including the above-referenced and other value
chain applications,
services, solutions, and the like), such that by virtue of shared
microservices, shared data
infrastructure, and shared intelligence, any pair or larger combination or
permutation of such
services may be improved relative to an isolated application of the same type.
[0278] Referring still to Fig. 10, the set of applications 630 provided on the
VCNP 604,
integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or
involving a set of
value chain network entities 652 may further include, without limitation: a
payments application
860 (such as for calculating payments (including based on situational factors
such as applicable
taxes, duties and the like for the geography of an entity 652), transferring
funds, resolving
payments to parties, and the like, for any of the applications 630 noted
herein); a process
management application 862 (such as for managing any of the processes or
workflows described
throughout this disclosure, including supply processes, demand processes,
logistics processes,
delivery processes, fulfillment processes, distribution processes, ordering
processes, navigation
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processes, and many others); a compatibility testing application 864, such as
for assessing
compatibility among value chain network entities 652 or activities involved in
any of the
processes, workflows, activities, or other applications 630 described herein
(such as for
determining compatibility of a container or package with a product 650, the
compatibility of a
product 650 with a set of customer requirements, the compatibility of a
product 650 with another
product 650 (such as where one is a refill, resupply, replacement part, or the
like for the other),
the compatibility of a infrastructure and equipment entities 652 (such as
between a container ship
or barge and a port or waterway, between a container and a storage facility,
between a truck and a
roadway, between a drone or robot and a package, between a drone, AV or robot
and a delivery
destination, and many others); an infrastructure testing application 802 (such
as for testing the
capabilities of infrastructure elements to support a product 650 or an
application 630 (such as,
without limitation, storage capabilities, lifting capabilities, moving
capabilities, storage capacity,
network capabilities, environmental control capabilities, software
capabilities, security
capabilities, and many others)); and/or an incident management application 910
(such as for
managing events, accidents, and other incidents that may occur in one or more
environments
involving value chain network entities 652, such as, without limitation,
vehicle accidents, worker
injuries, shutdown incidents, property damage incidents, product damage
incidents, product
liability incidents, regulatory non-compliance incidents, health and/or safety
incidents, traffic
congestion and/or delay incidents (including network traffic, data traffic,
vehicle traffic, maritime
traffic, human worker traffic, and others, as well as combinations among
them), product failure
incidents, system failure incidents, system performance incidents, fraud
incidents, misuse
incidents, unauthorized use incidents, and many others).
[0279] Referring still to Fig. 10, the set of applications 630 provided on the
VCNP 604,
integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or
involving a set of
.. value chain network entities 652 may further include, without limitation: a
predictive
maintenance application 910 (such as for anticipating, predicting, and
undertaking actions to
manage faults, failures, shutdowns, damage, required maintenance, required
repairs, required
service, required support, or the like for a set of value chain network
entities 652, such as
products 650, equipment, infrastructure, buildings, vehicles, and others); a
logistics application
912 (such as for managing logistics for pickups, deliveries, transfer of goods
onto hauling
facilities, loading, unloading, packing, picking, shipping, driving, and other
activities involving
in the scheduling and management of the movement of products 650 and other
items between
points of origin and points of destination through various intermediate
locations; a reverse
logistic application 914 (such as for handling logistics for returned products
650, waste products,
damaged goods, or other items that can be transferred on a return logistics
path); a waste
reduction application 920 (such as for reducing packaging waste, solid waste,
waste of energy,
liquid waste, pollution, contaminants, waste of computing resources, waste of
human resources,
or other waste involving a value chain network entity 652 or activity); an
augmented reality,
mixed reality and/or virtual reality application 930 (such as for visualizing
one or more value
chain network entities 652 or activities involved in one or more of the
applications 630, such as,
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without limitation, movement of a product 650, the interior of a facility, the
status or condition of
an item of goods, one or more environmental conditions, a weather condition, a
packing
configuration for a container or a set of containers, or many others); a
demand prediction
application 940 (such as for predicting demand for a product 650, a category
of products, a
potential product, and/or a factor involved in demand, such as a market
factor, a wealth factor, a
demographic factor, a weather factor, an economic factor, or the like); a
demand aggregation
application 942 (such as for aggregating information, orders and/or
commitments (optionally
embodied in one or more contracts, which may be smart contracts) for one or
more products 650,
categories, or the like, including current demand for existing products and
future demand for
products that are not yet available); a customer profiling application 944
(such as for profiling
one or more demographic, psychographic, behavioral, economic, geographic, or
other attributes
of a set of customers, including based on historical purchasing data, loyalty
program data,
behavioral tracking data (including data captured in interactions by a
customer with a smart
product 650), online clickstream data, interactions with intelligent agents,
and other data
sources); and/or a component supply application 948 (such as for managing a
supply chain of
components for a set of products 650).
[0280] Referring still to Fig. 10, the set of applications 630 provided on the
VCNP 604,
integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or
involving a set of
value chain network entities 652 may further include, without limitation: a
policy management
application 868 (such as for deploying one or more policies, rules, or the
like for governance of
one or more value chain network entities 652 or applications 630, such as to
govern execution of
one or more workflows (which may involve configuring polices in the platform
604 on a per-
workflow basis), to govern compliance with regulations (including maritime,
food & drug,
medical, environmental, health, safety, tax, financial reporting, commercial,
and other regulations
as described throughout this disclosure or as would be understood in the art),
to govern
provisioning of resources (such as connectivity, computing, human, energy, and
other resources),
to govern compliance with corporate policies, to govern compliance with
contracts (including
smart contracts, wherein the platform 604 may automatically deploy governance
features to
relevant entities 652 and applications 630, such as via connectivity
facilities 642), to govern
interactions with other entities (such as involving policies for sharing of
information and access
to resources), to govern data access (including privacy data, operational
data, status data, and
many other data types), to govern security access to infrastructure, products,
equipment,
locations, or the like, and many others; a product configuration application
870 (such as for
allowing a product manager and/or automated product configuration process
(optionally using
robotic process automation) to determine a configuration for a product 650,
including
configuration on-the-fly, such as during agile manufacturing, or involving
configuration or
customization in route (such as by 3D printing one or more features or
elements), or involving
configuration or customization remotely, such as by downloading firmware,
configuring field
programmable gate arrays, installing software, or the like; a warehousing and
fulfillment
application 872 (such as for managing a warehouse, distribution center,
fulfillment center, or the
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like, such as involving selection of products, configuring storage locations
for products,
determining routes by which personnel, mobile robots, and the like move
products around a
facility, determining picking and packing schedules, routes and workflows,
managing operations
of robots, drones, conveyors, and other facilities, determining schedules for
moving products out
__ to loading docks or the like, and many other functions); a kit
configuration and deployment
application 874 (such as for enabling a user of the VCNP to configure a kit,
box, or otherwise
pre-integrated, pre-provisioned, and/or pre-configured system to allow a
customer or worker to
rapidly deploy a subset of capabilities of the VCNP 604 for a specific value
chain network entity
652 and/or application 630); and/or a product testing application 878 for
testing a product 650
(including testing for performance, activation of capabilities and features,
safety, compliance
with policy or regulations, quality, quality of service, likelihood of
failure, and many other
factors).
[0281] Referring still to Fig. 10, the set of applications 630 provided on the
VCNP 604,
integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or
involving a set of
value chain network entities 652 may further include, without limitation a
maritime fleet
management application 880 (for managing a set of maritime assets, such as
container ships,
barges, boats, and the like, as well as related infrastructure facilities such
as docks, cranes, ports,
and others, such as to determine optimal routes for fleet assets based on
weather, market, traffic,
and other conditions, to ensure compliance with policies and regulations, to
ensure safety, to
improve environmental factors, to improve financial metrics, and many others);
a shipping
management application 882 (such as for managing a set of shipping assets,
such as trucks,
trains, airplanes, and the like, such as to optimize financial yield, to
improve safety, to reduce
energy consumption, to reduce delays, to mitigate environmental impact, and
for many other
purposes); an opportunity matching application 884 (such as for matching one
or more demand
factors with one or more supply factors, for matching needs and capabilities
of value chain
network entities 652, for identifying reverse logistics opportunities, for
identifying opportunities
for inputs to enrich analytics, artificial intelligence and/or automation, for
identifying cost-saving
opportunities, for identifying profit and/or arbitrage opportunities, and many
others); a workforce
management application 888 (such as for managing workers in various work
forces, including
work forces in, on or for fulfillment centers, ships, ports, warehouses,
distribution centers,
enterprise management locations, retail stores, online/ecommerce site
management facilities,
ports, ships, boats, barges, trains, depots, and other facilities mentioned
throughout this
disclosure); a distribution and delivery application 890 (such as for
planning, scheduling, routing,
and otherwise managing distribution and delivery of products 650 and other
items); and/or an
enterprise resource planning (ERP) application 892 (such as for planning
utilization of enterprise
resources, including workforce resources, financial resources, energy
resources, physical assets,
digital assets, and other resources).
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CORE CAPABILITIES AND INTERACTIONS OF THE DATA HANDLING LAYERS (ADAPTIVE
INTELLIGENCE, MONITORING, DATA STORAGE AND APPLICATIONS)
[0282] Referring to Fig. 11, a high-level schematic of an embodiment of the
value chain
network management platform 604 is illustrated, including a set of systems,
applications,
processes, modules, services, layers, devices, components, machines, products,
sub-systems,
interfaces, connections, and other elements working in coordination to enable
intelligent
management of sets of the value chain entities 652 that may occur, operate,
transact or the like
within, or own, operate, support or enable, one or more value chain network
processes,
workflows, activities, events and/or applications 630 or that may otherwise be
part of, integrated
with, linked to, or operated on by the platform 604 in connection with a
product 650 (which may
be a finished good, software product, hardware product, component product,
material, item of
equipment, consumer packaged good, consumer product, food product, beverage
product, home
product, business supply product, consumable product, pharmaceutical product,
medical device
product, technology product, entertainment product, or any other type of
product or related
service, which may, in embodiments, encompass an intelligent product that is
enabled with
processing, networking, sensing, computation, and/or other Internet of Things
capabilities).
Value chain entities 652, such as involved in or for a wide range of value
chain activities (such as
supply chain activities, logistics activities, demand management and planning
activities, delivery
activities, shipping activities, warehousing activities, distribution and
fulfillment activities,
inventory aggregation, storage and management activities, marketing
activities, and many others,
as involved in various value chain network processes, workflows, activities,
events and
applications 630 may include any of the wide variety of assets, systems,
devices, machines,
components, equipment, facilities, individuals or other entities mentioned
throughout this
disclosure or in the documents incorporated herein by reference.
[0283] In embodiments, the value chain network management platform 604 may
include the set
of data handling layers 624, each of which is configured to provide a set of
capabilities that
facilitate development and deployment of intelligence, such as for
facilitating automation,
machine learning, applications of artificial intelligence, intelligent
transactions, intelligent
operations, remote control, analytics, monitoring, reporting, state
management, event
management, process management, and many others, for a wide variety of value
chain network
applications and end uses. In embodiments, the data handling layers 624 may
include a value
chain network monitoring systems layer 614, a value chain network entity-
oriented data storage
systems layer 624 (referred to in some cases herein for convenience simply as
a data storage
layer 624), an adaptive intelligent systems layer 614 and a value chain
network management
platform layer 604. The value chain network management platform 604 may
include the data
handling layers 624 such that the value chain network management platform
layer 604 may
provide management of the value chain network management platform 604 and/or
management
of the other layers such as the value chain network monitoring systems layer
614, the value chain
network entity-oriented data storage systems layer 624 (e.g., data storage
layer 624), and the
adaptive intelligent systems layer 614. Each of the data handling layers 624
may include a variety
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of services, programs, applications, workflows, systems, components and
modules, as further
described herein and in the documents incorporated herein by reference. In
embodiments, each of
the data handling layers 624 (and optionally the platform 604 as a whole) is
configured such that
one or more of its elements can be accessed as a service by other layers 624
or by other systems
(e.g., being configured as a platform-as-a-service deployed on a set of cloud
infrastructure
components in a microservices architecture). For example, the platform 604 may
have (or may
configure and/or provision), and a data handling layer 608 may use, a set of
connectivity
facilities 642, such as network connections (including various configurations,
types and
protocols), interfaces, ports, application programming interfaces (APIs),
brokers, services,
connectors, wired or wireless communication links, human-accessible
interfaces, software
interfaces, micro-services, SaaS interfaces, PaaS interfaces, IaaS interfaces,
cloud capabilities, or
the like by which data or information may be exchanged between a data handling
layer 608 and
other layers, systems or sub-systems of the platform 604, as well as with
other systems, such as
value chain entities 652 or external systems, such as cloud-based or on-
premises enterprise
systems (e.g., accounting systems, resource management systems, CRM systems,
supply chain
management systems and many others). Each of the data handling layers 624 may
include a set
of services (e.g., microservices), for data handling, including facilities for
data extraction,
transformation and loading; data cleansing and deduplication facilities; data
normalization
facilities; data synchronization facilities; data security facilities;
computational facilities (e.g., for
performing pre-defined calculation operations on data streams and providing an
output stream);
compression and de-compression facilities; analytic facilities (such as
providing automated
production of data visualizations) and others.
[0284] In embodiments, each data handling layer 608 has a set of application
programming
connectivity facilities 642 for automating data exchange with each of the
other data handling
layers 624. These may include data integration capabilities, such as for
extracting, transforming,
loading, normalizing, compression, decompressing, encoding, decoding, and
otherwise
processing data packets, signals, and other information as it exchanged among
the layers and/or
the applications 630, such as transforming data from one format or protocol to
another as needed
in order for one layer to consume output from another. In embodiments, the
data handling layers
624 are configured in a topology that facilitates shared data collection and
distribution across
multiple applications and uses within the platform 604 by the value chain
monitoring systems
layer 614. The value chain monitoring systems layer 614 may include, integrate
with, and/or
cooperate with various data collection and management systems 640, referred to
for convenience
in some cases as data collection systems 640, for collecting and organizing
data collected from or
about value chain entities 652, as well as data collected from or about the
various data layers 624
or services or components thereof. For example, a stream of physiological data
from a wearable
device worn by a worker undertaking a task or a consumer engaged in an
activity can be
distributed via the monitoring systems layer 614 to multiple distinct
applications in the value
chain management platform layer 604, such as one that facilitates monitoring
the physiological,
psychological, performance level, attention, or other state of a worker and
another that facilitates
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operational efficiency and/or effectiveness. In embodiments, the monitoring
systems layer 614
facilitates alignment, such as time-synchronization, normalization, or the
like of data that is
collected with respect to one or more value chain network entities 652. For
example, one or more
video streams or other sensor data collected of or with respect to a worker
718 or other entity in a
value chain network facility or environment, such as from a set of camera-
enabled IoT devices,
may be aligned with a common clock, so that the relative timing of a set of
videos or other data
can be understood by systems that may process the videos, such as machine
learning systems that
operate on images in the videos, on changes between images in different frames
of the video, or
the like. In such an example, the monitoring systems layer 614 may further
align a set of videos,
camera images, sensor data, or the like, with other data, such as a stream of
data from wearable
devices, a stream of data produced by value chain network systems (such as
ships, lifts, vehicles,
containers, cargo handling systems, packing systems, delivery systems,
drones/robots, and the
like), a stream of data collected by mobile data collectors, and the like.
Configuration of the
monitoring systems layer 614 as a common platform, or set of microservices,
that are accessed
across many applications, may dramatically reduce the number of
interconnections required by
an owner or other operator within a value chain network in order to have a
growing set of
applications monitoring a growing set of IoT devices and other systems and
devices that are
under its control.
[0285] In embodiments, the data handling layers 624 are configured in a
topology that
facilitates shared or common data storage across multiple applications and
uses of the platform
604 by the value chain network-oriented data storage systems layer 624,
referred to herein for
convenience in some cases simply as the data storage layer 624 or storage
layer 624. For
example, various data collected about the value chain entities 652, as well as
data produced by
the other data handling layers 624, may be stored in the data storage layer
624, such that any of
the services, applications, programs, or the like of the various data handling
layers 624 can access
a common data source (which may comprise a single logical data source that is
distributed across
disparate physical and/or virtual storage locations). This may facilitate a
dramatic reduction in
the amount of data storage required to handle the enormous amount of data
produced by or about
value chain network entities 652 as applications 630 and uses of value chain
networks grow and
proliferate. For example, a supply chain or inventory management application
in the value chain
management platform layer 604, such as one for ordering replacement parts for
a machine or
item of equipment, may access the same data set about what parts have been
replaced for a set of
machines as a predictive maintenance application that is used to predict
whether a component of
a ship, or facility of a port is likely to require replacement parts.
Similarly, prediction may be
used with respect to the resupply of items.
[0286] In embodiments, value chain network data objects 1004 may be provided
according to
an object-oriented data model that defines classes, objects, attributes,
parameters and other
features of the set of data objects (such as associated with value chain
network entities 652 and
applications 630) that are handled by the platform 604.
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[0287] In embodiments, the data storage systems layer 624 may provide an
extremely rich
environment for collection of data that can be used for extraction of features
or inputs for
intelligence systems, such as expert systems, analytic systems, artificial
intelligence systems,
robotic process automation systems, machine learning systems, deep learning
systems,
supervised learning systems, or other intelligent systems as disclosed
throughout this disclosure
and the documents incorporated herein by reference. As a result, each
application 630 in the
platform 604 and each adaptive intelligent system in the adaptive intelligent
systems layer 614
can benefit from the data collected or produced by or for each of the others.
In embodiments, the
data storage systems layer 624 may facilitate collection of data that can be
used for extraction of
features or inputs for intelligence systems such as a development framework
from artificial
intelligence. In examples, the collections of data may pull in and/or house
event logs (naturally
stored or ad-hoc, as needed), perform periodic checks on onboard diagnostic
data, or the like. In
examples, pre calculation of features may be deployed using AWS Lambda, for
example, or
various other cloud-based on-demand compute capabilities, such as pre-
calculations,
multiplexing signals. In many examples, there are pairings (doubles, triples,
quadruplets, etc.) of
similar kinds of value chain entities that may use one or more sets of
capabilities of the data
handling layers 624 to deploy connectivity and services across value chain
entities and across
applications used by the entities even when amassing hundreds and hundreds of
data types from
relatively disparate entities. In these examples, various pairings of similar
types of value chain
entities using, at least in part, the connectivity and services across value
chain entities and
applications, may direct the information from the pairings of connected data
to artificial
intelligence services including the various neural networks disclosed herein
and hybrid
combinations thereof. In these examples, genetic programming techniques may be
deployed to
prune some of the input features in the information from the pairings of
connected data. In these
examples, genetic programming techniques may also be deployed to add to and
augment the
input features in the information from the pairings. These genetic programming
techniques may
be shown to increase the efficacy of the determinations established by the
artificial intelligence
services. In these examples, the information from the pairings of connected
data may be migrated
to other layers on the platform including to support or deploy robotic process
automation,
prediction, forecasting, and other resources such that the shared data schema
may facilitate as
capabilities and resources for the platform 604.
[0288] A wide range of data types may be stored in the storage layer 624 using
various storage
media and data storage types, data architectures 1002, and formats, including,
without limitation:
asset and facility data 1030, state data 1140 (such as indicating a state,
condition status, or other
indicator with respect to any of the value chain network entities 652, any of
the applications 630
or components or workflows thereof, or any of the components or elements of
the platform 604,
among others), worker data 1032 (including identity data, role data, task
data, workflow data,
health data, attention data, mood data, stress data, physiological data,
performance data, quality
data and many other types); event data 1034 ((such as with respect to any of a
wide range of
events, including operational data, transactional data, workflow data,
maintenance data, and
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many other types of data that includes or relates to events that occur within
a value chain network
668 or with respect to one or more applications 630, including process events,
financial events,
transaction events, output events, input events, state-change events,
operating events, workflow
events, repair events, maintenance events, service events, damage events,
injury events,
replacement events, refueling events, recharging events, shipping events,
warehousing events,
transfers of goods, crossing of borders, moving of cargo, inspection events,
supply events, and
many others); claims data 664 (such as relating to insurance claims, such as
for business
interruption insurance, product liability insurance, insurance on goods,
facilities, or equipment,
flood insurance, insurance for contract-related risks, and many others, as
well as claims data
relating to product liability, general liability, workers compensation, injury
and other liability
claims and claims data relating to contracts, such as supply contract
performance claims, product
delivery requirements, warranty claims, indemnification claims, delivery
requirements, timing
requirements, milestones, key performance indicators and others); accounting
data 730 (such as
data relating to completion of contract requirements, satisfaction of bonds,
payment of duties and
tariffs, and others); and risk management data 732 (such as relating to items
supplied, amounts,
pricing, delivery, sources, routes, customs information and many others),
among many other data
types associated with value chain network entities 652 and applications 630.
[0289] In embodiments, the data handling layers 624 are configured in a
topology that
facilitates shared adaptation capabilities, which may be provided, managed,
mediated and the like
by one or more of a set of services, components, programs, systems, or
capabilities of the
adaptive intelligent systems layer 614, referred to in some cases herein for
convenience as the
adaptive intelligence layer 614. The adaptive intelligence systems layer 614
may include a set of
data processing, artificial intelligence and computational systems 634 that
are described in more
detail elsewhere throughout this disclosure. Thus, use of various resources,
such as computing
resources (such as available processing cores, available servers, available
edge computing
resources, available on-device resources (for single devices or peered
networks), and available
cloud infrastructure, among others), data storage resources (including local
storage on devices,
storage resources in or on value chain entities or environments (including on-
device storage,
storage on asset tags, local area network storage and the like), network
storage resources, cloud-
based storage resources, database resources and others), networking resources
(including cellular
network spectrum, wireless network resources, fixed network resources and
others), energy
resources (such as available battery power, available renewable energy, fuel,
grid-based power,
and many others) and others may be optimized in a coordinated or shared way on
behalf of an
operator, enterprise, or the like, such as for the benefit of multiple
applications, programs,
workflows, or the like. For example, the adaptive intelligence layer 614 may
manage and
provision available network resources for both a supply chain management
application and for a
demand planning application (among many other possibilities), such that low
latency resources
are used for supply chain management application (where rapid decisions may be
important) and
longer latency resources are used for the demand planning application. As
described in more
detail throughout this disclosure and the documents incorporated herein by
reference, a wide
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variety of adaptations may be provided on behalf of the various services and
capabilities across
the various layers 624, including ones based on application requirements,
quality of service, on-
time delivery, service objectives, budgets, costs, pricing, risk factors,
operational objectives,
efficiency objectives, optimization parameters, returns on investment,
profitability,
uptime/downtime, worker utilization, and many others.
[0290] The value chain management platform layer 604, referred to in some
cases herein for
convenience as the platform layer 604, may include, integrate with, and enable
the various value
chain network processes, workflows, activities, events and applications 630
described throughout
this disclosure that enable an operator to manage more than one aspect of a
value chain network
environment or entity 652 in a common application environment (e.g., shared,
pooled, similarly
licenses whether shared data for one person, multiple people, or anonymized),
such as one that
takes advantage of common data storage in the data storage layer 624, common
data collection or
monitoring in the monitoring systems layer 614 and/or common adaptive
intelligence of the
adaptive intelligence layer 614. Outputs from the applications 630 in the
platform layer 604 may
be provided to the other data handing layers 624. These may include, without
limitation, state and
status information for various objects, entities, processes, flows and the
like; object information,
such as identity, attribute and parameter information for various classes of
objects of various data
types; event and change information, such as for workflows, dynamic systems,
processes,
procedures, protocols, algorithms, and other flows, including timing
information; outcome
information, such as indications of success and failure, indications of
process or milestone
completion, indications of correct or incorrect predictions, indications of
correct or incorrect
labeling or classification, and success metrics (including relating to yield,
engagement, return on
investment, profitability, efficiency, timeliness, quality of service, quality
of product, customer
satisfaction, and others) among others. Outputs from each application 630 can
be stored in the
data storage layer 624, distributed for processing by the data collection
layer 614, and used by the
adaptive intelligence layer 614. The cross-application nature of the platform
layer 604 thus
facilitates convenient organization of all of the necessary infrastructure
elements for adding
intelligence to any given application, such as by supplying machine learning
on outcomes across
applications, providing enrichment of automation of a given application via
machine learning
based on outcomes from other applications or other elements of the platform
604, and allowing
application developers to focus on application-native processes while
benefiting from other
capabilities of the platform 604. In examples, there may be systems,
components, services and
other capabilities that optimize control, automation, or one or more
performance characteristics
of one or more value chain network entities 652; or ones that may generally
improve any of
process and application outputs and outcomes 1040 pursued by use of the
platform 604. In some
examples, outputs and outcomes 1040 from various applications 630 may be used
to facilitate
automated learning and improvement of classification, prediction, or the like
that is involved in a
step of a process that is intended to be automated.
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SOME DATA STORAGE LAYER DETAILS ¨ ALTERNATIVE DATA ARCHITECTURES
[0291] Referring to Fig. 12, additional details, components, sub-systems, and
other elements of
an optional embodiment of the data storage layer 624 of the platform 604 are
illustrated. Various
data architectures may be used, including conventional relational and object-
oriented data
architectures, blockchain architectures 1180, asset tag data storage
architectures 1178, local
storage architectures 1190, network storage architectures 1174, multi-tenant
architectures 1132,
distributed data architectures 1002, value chain network (VCN) data object
architectures 1004,
cluster-based architectures 1128, event data-based architectures 1034, state
data-based
architectures 1140, graph database architectures 1124, self-organizing
architectures 1134, and
other data architectures 1002.
[0292] The adaptive intelligent systems layer 614 of the platform 604 may
include one or more
protocol adaptors 1110 for facilitating data storage, retrieval access, query
management, loading,
extraction, normalization, and/or transformation to enable use of the various
other data storage
architectures 1002, such as allowing extraction from one form of database and
loading to a data
system that uses a different protocol or data structure.
[0293] In embodiments, the value chain network-oriented data storage systems
layer 624 may
include, without limitation, physical storage systems, virtual storage
systems, local storage
systems (e.g., part of the local storage architectures 1190), distributed
storage systems, databases,
memory, network-based storage, network-attached storage systems (e.g., part of
the network
storage architectures 1174such as using NVME, storage attached networks, and
other network
storage systems), and many others.
[0294] In embodiments, the storage layer 624 may store data in one or more
knowledge graphs
(such as a directed acyclic graph, a data map, a data hierarchy, a data
cluster including links and
nodes, a self-organizing map, or the like) in the graph database architectures
1124. In example
embodiments, the knowledge graph may be a prevalent example of when a graph
database and
graph database architecture may be used. In some examples, the knowledge graph
may be used to
graph a workflow. For a linear workflow, a directed acyclic graph may be used.
For a contingent
workflow, a cyclic graph may be used. The graph database (e.g., graph database
architectures
vpc608) may include the knowledge graph or the knowledge graph may be an
example of the
graph database. In example embodiments, the knowledge graph may include
ontology and
connections (e.g., relationships) between the ontology of the knowledge graph.
In an example,
the knowledge graph may be used to capture an articulation of knowledge
domains of a human
expert such that there may be an identification of opportunities to design and
build robotic
process automation or other intelligence that may replicate this knowledge
set. The platform may
be used to recognize that a type of expert is using this factual knowledge
base (from the
knowledge graph) coupled with competencies that may be replicable by
artificial intelligence that
may be different depending on type of expertise involved. For example,
artificial intelligence
such as a convolutional neural network may be used with spatiotemporal aspects
that may be
used to diagnose issues or packing up a box in a warehouse. Whereas the
platform may use a
different type of knowledge graph for a self-organizing map of an expert whose
main job is to
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segment customers into customer segmentation groups. In some examples, the
knowledge graph
may be built from various data such as job credentials, job listings, parsing
output deliverables.
In embodiments, the data storage layer 624 may store data in a digital thread,
ledger, or the like,
such as for maintaining a serial or other records of an entities 652 over
time, including any of the
entities described herein. In embodiments, the data storage layer 624 may use
and enable an asset
tag 1178, which may include a data structure that is associated with an asset
and accessible and
managed, such as by use of access controls, so that storage and retrieval of
data is optionally
linked to local processes, but also optionally open to remote retrieval and
storage options. In
embodiments, the storage layer 624 may include one or more blockchains 1180,
such as ones that
store identity data, transaction data, historical interaction data, and the
like, such as with access
control that may be role-based or may be based on credentials associated with
a value chain
entity 652, a service, or one or more applications 630. Data stored by the
data storage systems
624 may include accounting and other financial data 730, access data 734,
asset and facility data
1030 (such as for any of the value chain assets and facilities described
herein), asset tag data
1178, worker data 1032, event data 1034, risk management data 732, pricing
data 738, safety
data 664 and many other types of data that may be associated with, produced
by, or produced
about any of the value chain entities and activities described herein and in
the documents
incorporated by reference.
ADAPTIVE INTELLIGENT SYSTEMS AND MONITORING LAYERS
[0295] Referring to Fig. 13, additional details, components, sub-systems, and
other elements of
an optional embodiment of the platform 604 are illustrated. The management
platform 604 may,
in various optional embodiments, include the set of applications 630, by which
an operator or
owner of a value chain network entity, or other users, may manage, monitor,
control, analyze, or
otherwise interact with one or more elements of a value chain network entity
652, such as any of
the elements noted in connection above and throughout this disclosure.
[0296] In embodiments, the adaptive intelligent systems layer 614 may include
a set of
systems, components, services and other capabilities that collectively
facilitate the coordinated
development and deployment of intelligent systems, such as ones that can
enhance one or more
of the applications 630 at the application platform layer 604; ones that can
improve the
performance of one or more of the components, or the overall performance
(e.g., speed/latency,
reliability, quality of service, cost reduction, or other factors) of the
connectivity facilities 642;
ones that can improve other capabilities within the adaptive intelligent
systems layer 614; ones
that improve the performance (e.g., speed/latency, energy utilization, storage
capacity, storage
efficiency, reliability, security, or the like) of one or more of the
components, or the overall
performance, of the value chain network-oriented data storage systems 624;
ones that optimize
control, automation, or one or more performance characteristics of one or more
value chain
network entities 652; or ones that generally improve any of the process and
application outputs
and outcomes 1040 pursued by use of the platform 604.
[0297] These adaptive intelligent systems 614 may include a robotic process
automation system
1442, a set of protocol adaptors 1110, a packet acceleration system 1410, an
edge intelligence
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system 1420 (which may be a self-adaptive system), an adaptive networking
system 1430, a set
of state and event managers 1450, a set of opportunity miners 1460, a set of
artificial intelligence
systems 1160, a set of digital twin systems 1700, a set of entity interaction
management systems
1900 (such as for setting up, provisioning, configuring and otherwise managing
sets of
interactions between and among sets of value chain network entities 652 in the
value chain
network 668), and other systems.
[0298] In embodiments, the value chain monitoring systems layer 614 and its
data collection
systems 640 may include a wide range of systems for the collection of data.
This layer may
include, without limitation, real time monitoring systems 1520 (such as
onboard monitoring
systems like event and status reporting systems on ships and other floating
assets, on delivery
vehicles, on trucks and other hauling assets, and in shipyards, ports,
warehouses, distribution
centers and other locations; on-board diagnostic (OBD) and telematics systems
on floating assets,
vehicles and equipment; systems providing diagnostic codes and events via an
event bus,
communication port, or other communication system; monitoring infrastructure
(such as cameras,
motion sensors, beacons, RFID systems, smart lighting systems, asset tracking
systems, person
tracking systems, and ambient sensing systems located in various environments
where value
chain activities and other events take place), as well as removable and
replaceable monitoring
systems, such as portable and mobile data collectors, RFID and other tag
readers, smart phones,
tablets and other mobile devices that are capable of data collection and the
like); software
interaction observation systems 1500 (such as for logging and tracking events
involved in
interactions of users with software user interfaces, such as mouse movements,
touchpad
interactions, mouse clicks, cursor movements, keyboard interactions,
navigation actions, eye
movements, finger movements, gestures, menu selections, and many others, as
well as software
interactions that occur as a result of other programs, such as over APIs,
among many others);
mobile data collectors 1170 (such as described extensively herein and in
documents incorporated
by reference), visual monitoring systems 1930 (such as using video and still
imaging systems,
LIDAR, IR and other systems that allow visualization of items, people,
materials, components,
machines, equipment, personnel, gestures, expressions, positions, locations,
configurations, and
other factors or parameters of entities 652, as well as inspection systems
that monitor processes,
activities of workers and the like); point of interaction systems 1530 (such
as dashboards, user
interfaces, and control systems for value chain entities); physical process
observation systems
1510 (such as for tracking physical activities of operators, workers,
customers, or the like,
physical activities of individuals (such as shippers, delivery workers,
packers, pickers, assembly
personnel, customers, merchants, vendors, distributors and others), physical
interactions of
workers with other workers, interactions of workers with physical entities
like machines and
equipment, and interactions of physical entities with other physical entities,
including, without
limitation, by use of video and still image cameras, motion sensing systems
(such as including
optical sensors, LIDAR, IR and other sensor sets), robotic motion tracking
systems (such as
tracking movements of systems attached to a human or a physical entity) and
many others;
machine state monitoring systems 1940 (including onboard monitors and external
monitors of
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conditions, states, operating parameters, or other measures of the condition
of any value chain
entity, such as a machine or component thereof, such as a machine, such as a
client, a server, a
cloud resource, a control system, a display screen, a sensor, a camera, a
vehicle, a robot, or other
machine); sensors and cameras 1950 and other IoT data collection systems 1172
(including
onboard sensors, sensors or other data collectors (including click tracking
sensors) in or about a
value chain environment (such as, without limitation, a point of origin, a
loading or unloading
dock, a vehicle or floating asset used to convey goods, a container, a port, a
distribution center, a
storage facility, a warehouse, a delivery vehicle, and a point of
destination), cameras for
monitoring an entire environment, dedicated cameras for a particular machine,
process, worker,
or the like, wearable cameras, portable cameras, cameras disposed on mobile
robots, cameras of
portable devices like smart phones and tablets, and many others, including any
of the many
sensor types disclosed throughout this disclosure or in the documents
incorporated herein by
reference); indoor location monitoring systems 1532 (including cameras, IR
systems, motion-
detection systems, beacons, RFID readers, smart lighting systems,
triangulation systems, RF and
other spectrum detection systems, time-of-flight systems, chemical noses and
other chemical
sensor sets, as well as other sensors); user feedback systems 1534 (including
survey systems,
touch pads, voice-based feedback systems, rating systems, expression
monitoring systems, affect
monitoring systems, gesture monitoring systems, and others); behavioral
monitoring systems
1538 (such as for monitoring movements, shopping behavior, buying behavior,
clicking
behavior, behavior indicating fraud or deception, user interface interactions,
product return
behavior, behavior indicative of interest, attention, boredom or the like,
mood-indicating
behavior (such as fidgeting, staying still, moving closer, or changing
posture) and many others);
and any of a wide variety of Internet of Things (IoT) data collectors 1172,
such as those
described throughout this disclosure and in the documents incorporated by
reference herein.
[0299] In embodiments, the value chain monitoring systems layer 614 and its
data collection
systems 640 may include an entity discovery system 1900 for discovering one or
more value
chain network entities 652, such as any of the entities described throughout
this disclosure. This
may include components or sub-systems for searching for entities within the
value chain network
668, such as by device identifier, by network location, by geolocation (such
as by geofence), by
indoor location (such as by proximity to known resources, such as IoT-enabled
devices and
infrastructure, Wifi routers, switches, or the like), by cellular location
(such as by proximity to
cellular towers), by identity management systems (such as where an entity 652
is associated with
another entity 652, such as an owner, operator, user, or enterprise by an
identifier that is assigned
by and/or managed by the platform 604), and the like. Entity discovery 1900
may initiate a
handshake among a set of devices, such as to initiate interactions that serve
various applications
630 or other capabilities of the platform 604.
[0300] Referring to Fig. 14, a management platform of an information
technology system, such
as a management platform for a value chain of goods and/or services is
depicted as a block
diagram of functional elements and representative interconnections. The
management platform
includes a user interface 3020 that provides, among other things, a set of
adaptive intelligence
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systems 614. The adaptive intelligence systems 614 provide coordinated
intelligence (including
artificial intelligence 1160, expert systems 3002, machine learning 3004, and
the like) for a set of
demand management applications 824 and for a set of supply chain applications
812 for a
category of goods 3010, which may be produced and sold through the value
chain. The adaptive
intelligence systems 614 may deliver artificial intelligence 1160 through a
set of data processing,
artificial intelligence and computational systems 634. In embodiments, the
adaptive intelligence
systems 614 are selectable and/or configurable through the user interface 3020
so that one or
more of the adaptive intelligence systems 614 can operate on or in cooperation
with the sets of
value chain applications (e.g., demand management applications 824 and supply
chain
applications 812). The adaptive intelligence systems 614 may include
artificial intelligence,
including any of the various expert systems, artificial intelligence systems,
neural networks,
supervised learning systems, machine learning systems, deep learning systems,
and other systems
described throughout this disclosure and in the documents incorporated by
reference.
[0301] In embodiments, user interface may include interfaces for configuring
an artificial
intelligence system 1160 to take inputs from selected data sources of the
value chain (such as
data sources used by the set of demand management applications 824 and/or the
set of supply
chain applications 812) and supply them, such as to a neural network,
artificial intelligence
system 1160 or any of the other adaptive intelligence systems 614 described
throughout this
disclosure and in the documents incorporated herein by reference to enhance,
control, improve,
optimize, configure, adapt or have another impact on a value chain for the
category of goods
3010. In embodiments, the selected data sources of the value chain may be
applied either as
inputs for classification or prediction, or as outcomes relating to the value
chain, the category of
goods 3010 and the like.
[0302] In embodiments, providing coordinated intelligence may include
providing artificial
intelligence capabilities, such as artificial intelligence systems 1160 and
the like. Artificial
intelligence systems may facilitate coordinated intelligence for the set of
demand management
applications 824 or the set of supply chain applications 812 or both, such as
for a category of
goods, such as by processing data that is available in any of the data sources
of the value chain,
such as value chain processes, bills of materials, manifests, delivery
schedules, weather data,
traffic data, goods design specifications, customer complaint logs, customer
reviews, Enterprise
Resource Planning (ERP) System, Customer Relationship Management (CRM) System,
Customer Experience Management (CEM) System, Service Lifecycle Management
(SLM)
System, Product Lifecycle Management (PLM) System, and the like.
[0303] In embodiments, the user interface 3020 may provide access to, among
other things
artificial intelligence capabilities, applications, systems and the like for
coordinating intelligence
for applications of the value chain and particularly for value chain
applications for the category
of goods 3010. The user interface 3020 may be adapted to receive information
descriptive of the
category of goods 3010 and configure user access to the artificial
intelligence capabilities
responsive thereto, so that the user, through the user interface is guided to
artificial intelligence
capabilities that are suitable for use with value chain applications (e.g.,
the set of demand
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management applications 824 and supply chain applications 812) that contribute
to
goods/services in the category of goods 3010. The user interface 3020 may
facilitate providing
coordinated intelligence that comprises artificial intelligence capabilities
that provide coordinated
intelligence for a specific operator and/or enterprise that participates in
the supply chain for the
category of goods.
[0304] In embodiments, the user interface 3020 may be configured to facilitate
the user
selecting and/or configuring multiple artificial intelligence systems 1160 for
use with the value
chain. The user interface may present the set of demand management
applications 824 and
supply chain applications 812 as connected entities that receive, process, and
produce outputs
each of which may be shared among the applications. Types of artificial
intelligence systems
1160 may be indicated in the user interface 3020 responsive to sets of
connected applications or
their data elements being indicated in the user interface, such as by the user
placing a pointer
proximal to a connected set of applications and the like. In embodiments, the
user interface 3020
may facilitate access to the set of adaptive intelligence systems provides a
set of capabilities that
facilitate development and deployment of intelligence for at least one
function selected from a
list of functions consisting of supply chain application automation, demand
management
application automation, machine learning, artificial intelligence, intelligent
transactions,
intelligent operations, remote control, analytics, monitoring, reporting,
state management, event
management, and process management.
[0305] The adaptive intelligence systems 614 may be configured with data
processing, artificial
intelligence and computational systems 634 that may operate cooperatively to
provide
coordinated intelligence, such as when an artificial intelligence system 1160
operates on or
responds to data collected by or produced by other systems of the adaptive
intelligence systems
614, such as a data processing system and the like. In embodiments, providing
coordinated
intelligence may include operating a portion of a set of artificial
intelligence systems 1160 that
employs one or more types of neural network that is described herein and in
the documents
incorporated herein by reference and that processes any of the demand
management application
outputs and supply chain application outputs to provide the coordinated
intelligence.
[0306] In embodiments, providing coordinated intelligence for the set of
demand management
applications 824 may include configuring at least one of the adaptive
intelligence systems 614
(e.g., through the user interface 3020 and the like) for at least one or more
demand management
applications selected from a list of demand management applications including
a demand
planning application, a demand prediction application, a sales application, a
future demand
aggregation application, a marketing application, an advertising application,
an e-commerce
application, a marketing analytics application, a customer relationship
management application, a
search engine optimization application, a sales management application, an
advertising network
application, a behavioral tracking application, a marketing analytics
application, a location-based
product or service-targeting application, a collaborative filtering
application, a recommendation
engine for a product or service, and the like.
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[0307] Similarly, providing coordinated intelligence for the set of supply
chain applications 812
may include configuring at least one of the adaptive intelligence systems 614
for at least one or
more supply chain applications selected from a list of supply chain
applications including a
goods timing management application, a goods quantity management application,
a logistics
management application, a shipping application, a delivery application, an
order for goods
management application, an order for components management application, and
the like.
[0308] In embodiments, the management platform 102 may, such as through the
user interface
3020 facilitate access to the set of adaptive intelligence systems 614 that
provide coordinated
intelligence for a set of demand management applications 824 and supply chain
applications 812
through the application of artificial intelligence. In such embodiments, the
user may seek to align
supply with demand while ensuring profitability and the like of a value chain
for a category of
goods 3010. By providing access to artificial intelligence capabilities 1160,
the management
platform allows the user to focus on the applications of demand and supply
while gaining
advantages of techniques such as expert systems, artificial intelligence
systems, neural networks,
.. supervised learning systems, machine learning systems, deep learning
systems, and the like.
[0309] In embodiments, the management platform 102 may, through the user
interface 3020
and the like provide a set of adaptive intelligence systems 614 that provide
coordinated artificial
intelligence 1160 for the sets of demand management applications 824 and
supply chain
applications 812 for the category of goods 3020 by, for example, determining
(automatically)
.. relationships among demand management and supply chain applications based
on inputs used by
the applications, results produced by the applications, and value chain
outcomes. The artificial
intelligence 1160 may be coordinated by, for example, the set of data
processing, artificial
intelligence and computational systems 634 available through the adaptive
intelligence systems
614.
[0310] In embodiments, the management platform 102 may be configured with a
set of
artificial intelligence systems 1160 as part of a set of adaptive intelligence
systems 614 that
provide the coordinated intelligence for the sets of demand management
applications 824 and
supply chain applications 812 for a category of goods 3010. The set of
artificial intelligence
systems 1160 may provide the coordinated intelligence so that at least one
supply chain
application of the set of supply chain applications 812 produces results that
address at least one
aspect of supply for at least one of the goods in the category of goods as
determined by at least
one demand management application of the set of demand management applications
824. In
examples, a behavioral tracking demand management application may generate
results for
behavior of uses of a good in the category of goods 3010. The artificial
intelligence systems 1160
may process the behavior data and conclude that there is a perceived need for
greater consumer
access to a second product in the category of goods 3010. This coordinated
intelligence may be,
optionally automatically, applied to the set of supply chain applications 812
so that, for example,
production resources or other resources in the value chain for the category of
goods are allocated
to the second product. In examples, a distributor who handles stocking
retailer shelves may
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receive a new stocking plan that allocates more retail shelf space for the
second product, such as
by taking away space from a lower margin product and the like.
[0311] In embodiments, the set of artificial intelligence systems 1160 and the
like may provide
coordinated intelligence for the sets of supply chain and demand management
applications by,
for example, determining an optionally temporal prioritization of demand
management
application outputs that impact control of supply chain applications so that
an optionally
temporal demand for at least one of the goods in the category of goods 3010
can be met.
Seasonal adjustments in prioritization of demand application results are one
example of a
temporal change. Adjustments in prioritization may also be localized, such as
when a large
college football team is playing at their home stadium and local supply of
tailgating supplies may
temporally be adjusted even though demand management application results
suggest that small
propane stoves are not currently in demand in a wider region.
[0312] A set of adaptive intelligence systems 614 that provide coordinated
intelligence, such as
by providing artificial intelligence capabilities 1160 and the like may also
facilitate development
and deployment of intelligence for at least one function selected from a list
of functions
consisting of supply chain application automation, demand management
application automation,
machine learning, artificial intelligence, intelligent transactions,
intelligent operations, remote
control, analytics, monitoring, reporting, state management, event management,
and process
management. The set of adaptive intelligence systems 614 may be configured as
a layer in the
platform and an artificial intelligence system therein may operate on or be
responsive to data
collected by and/or produced by other systems (e.g., data processing systems,
expert systems,
machine learning systems and the like) of the adaptive intelligence systems
layer.
[0313] In addition to providing coordinated intelligence configured for
specific categories of
goods, the coordinated intelligence may be provided for a specific value chain
entity 652, such as
a supply chain operator, business, enterprise, and the like that participates
in the supply chain for
the category of goods.
[0314] Providing coordinated intelligence may include employing a neural
network to process
at least one of the inputs and outputs of the sets of demand management and
supply chain
applications. Neural networks may be used with demand applications, such as a
demand planning
application, a demand prediction application, a sales application, a future
demand aggregation
application, a marketing application, an advertising application, an e-
commerce application, a
marketing analytics application, a customer relationship management
application, a search engine
optimization application, a sales management application, an advertising
network application, a
behavioral tracking application, a marketing analytics application, a location-
based product or
service-targeting application, a collaborative filtering application, a
recommendation engine for a
product or service, and the like. Neural networks may also be used with supply
chain applications
such as a goods timing management application, a goods quantity management
application, a
logistics management application, a shipping application, a delivery
application, an order for
goods management application, an order for components management application,
and the like.
Neural networks may provide coordinated intelligence by processing data that
is available in any
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of a plurality of value chain data sources for the category of goods including
without limitation
processes, bill of materials, weather, traffic, design specification, customer
complaint logs,
customer reviews, Enterprise Resource Planning (ERP) System, Customer
Relationship
Management (CRM) System, Customer Experience Management (CEM) System, Service
Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System,
and the
like. Neural networks configured for providing coordinated intelligence may
share adaptation
capabilities with other adaptive intelligence systems 614, such as when these
systems are
configured in a topology that facilitates such shared adaptation. In
embodiments, neural networks
may facilitate provisioning available value chain/supply chain network
resources for both the set
of demand management applications and for the set of supply chain
applications. In
embodiments, neural networks may provide coordinated intelligence to improve
at least one of
the list of outputs consisting of a process output, an application output, a
process outcome, an
application outcome, and the like.
[0315] Referring to Fig. 15, a management platform of an information
technology system, such
as a management platform for a value chain of goods and/or services is
depicted as a block
diagram of functional elements and representative interconnections. The
management platform
includes a user interface 3020 that provides, among other things, a hybrid set
of adaptive
intelligence systems 614. The hybrid set of adaptive intelligence systems 614
provide
coordinated intelligence through the application of artificial intelligence,
such as through
application of a hybrid artificial intelligence system 3060, and optionally
through one or more
expert systems, machine learning systems, and the like for use with a set of
demand management
applications 824 and for a set of supply chain applications 812 for a category
of goods 3010,
which may be produced and sold through the value chain. The hybrid adaptive
intelligence
systems 614 may deliver two types of artificial intelligence systems, type A
3052 and type B
3054 through a set of data processing, artificial intelligence and
computational systems 634. In
embodiments, the hybrid adaptive intelligence systems 614 are selectable
and/or configurable
through the user interface 3020 so that one or more of the hybrid adaptive
intelligence systems
614 can operate on or in cooperation with the sets of supply chain
applications (e.g., demand
management applications 824 and supply chain applications 812). The hybrid
adaptive
intelligence systems 614 may include a hybrid artificial intelligence system
3060 that may
include at least two types of artificial intelligence capabilities including
any of the various expert
systems, artificial intelligence systems, neural networks, supervised learning
systems, machine
learning systems, deep learning systems, and other systems described
throughout this disclosure
and in the documents incorporated by reference. The hybrid adaptive
intelligence systems 614
may facilitate applying a first type of artificial intelligence system 1160 to
the set of demand
management applications 824 and a second type of artificial intelligence
system 1160 to the set
of supply chain applications 812, wherein each of the first type and second
type of artificial
intelligence system 1160 can operate independently, cooperatively, and
optionally coordinate
operation to provide coordinated intelligence for operation of the value chain
that produces at
least one of the goods in the category of goods 3010.
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[0316] In embodiments, the user interface 3020 may include interfaces for
configuring a hybrid
artificial intelligence system 3060 to take inputs from selected data sources
of the value chain
(such as data sources used by the set of demand management applications 824
and/or the set of
supply chain applications 812) and supply them, such as to at least one of the
two types of
artificial intelligence systems in the hybrid artificial intelligence system
3060, types of which are
described throughout this disclosure and in the documents incorporated herein
by reference to
enhance, control, improve, optimize, configure, adapt or have another impact
on a value chain for
the category of goods 3010. In embodiments, the selected data sources of the
value chain may be
applied either as inputs for classification or prediction, or as outcomes
relating to the value chain,
the category of goods 3010 and the like.
[0317] In embodiments, the hybrid adaptive intelligence systems 614 provides a
plurality of
distinct artificial intelligence systems 1160, a hybrid artificial
intelligence system 3060, and
combinations thereof. In embodiments, any of the plurality of distinct
artificial intelligence
systems 1160 and the hybrid artificial intelligence system 3060 may be
configured as a plurality
of neural network-based systems, such as a classification-adapted neural
network, a prediction-
adapted neural network and the like. As an example of hybrid adaptive
intelligence systems 614,
a machine learning-based artificial intelligence system may be provided for
the set of demand
management applications 824 and a neural network-based artificial intelligence
system may be
provided for the set of supply chain applications 812. As an example of a
hybrid artificial
intelligence system 3060, the hybrid adaptive intelligence systems 614 may
provide the hybrid
artificial intelligence system 3060 that may include a first type of
artificial intelligence that is
applied to the demand management applications 824 and which is distinct from a
second type of
artificial intelligence that is applied to the supply chain applications 812.
A hybrid artificial
intelligence system 3060 may include any combination of types of artificial
intelligence systems
including a plurality of a first type of artificial intelligence (e.g., neural
networks) and at least one
second type of artificial intelligence (e.g., an expert system) and the like.
In embodiments, a
hybrid artificial intelligence system may comprise a hybrid neural network
that applies a first
type of neural network with respect to the demand management applications 824
and a second
type of neural network with respect to the supply chain applications 812. Yet
further, a hybrid
artificial intelligence system 3060 may provide two types of artificial
intelligence to different
applications, such as different demand management applications 824 (e.g., a
sales management
application and a demand prediction application) or different supply chain
applications 812 (e.g.,
a logistics control application and a production quality control application).
[0318] In embodiments, hybrid adaptive intelligence systems 614 may be applied
as distinct
artificial intelligence capabilities to distinct demand management
applications 824. As examples,
coordinated intelligence through a hybrid artificial intelligence capabilities
may be provided to a
demand planning application by a feed-forward neural network, to a demand
prediction
application by a machine learning system, to a sales application by a self-
organizing neural
network, to a future demand aggregation application by a radial basis function
neural network, to
a marketing application by a convolutional neural network, to an advertising
application by a
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recurrent neural network, to an e-commerce application by a hierarchical
neural network, to a
marketing analytics application by a stochastic neural network, to a customer
relationship
management application by an associative neural network and the like.
[0319] Referring to Fig. 16, a management platform of an information
technology system, such
as a management platform for a value chain of goods and/or services is
depicted as a block
diagram of functional elements and representative interconnections for
providing a set of
predictions 3070. The management platform includes a user interface 3020 that
provides, among
other things, a set of adaptive intelligence systems 614. The adaptive
intelligence systems 614
provide a set of predictions 3070 through the application of artificial
intelligence, such as through
application of an artificial intelligence system 1160, and optionally through
one or more expert
systems, machine learning systems, and the like for use with a coordinated set
of demand
management applications 824 and supply chain applications 812 for a category
of goods 3010,
which may be produced and sold through the value chain. The adaptive
intelligence systems 614
may deliver the set of prediction 3070 through a set of data processing,
artificial intelligence and
computational systems 634. In embodiments, the adaptive intelligence systems
614 are selectable
and/or configurable through the user interface 3020 so that one or more of the
adaptive
intelligence systems 614 can operate on or in cooperation with the coordinated
sets of value
chain applications. The adaptive intelligence systems 614 may include an
artificial intelligence
system that provides artificial intelligence capabilities known to be
associated with artificial
intelligence including any of the various expert systems, artificial
intelligence systems, neural
networks, supervised learning systems, machine learning systems, deep learning
systems, and
other systems described throughout this disclosure and in the documents
incorporated by
reference. The adaptive intelligence systems 614 may facilitate applying
adapted intelligence
capabilities to the coordinated set of demand management applications 824 and
supply chain
applications 812 such as by producing a set of predictions 3070 that may
facilitate coordinating
the two sets of value chain applications, or at least facilitate coordinating
at least one demand
management application and at least one supply chain application from their
respective sets.
[0320] In embodiments, the set of predictions 3070 includes a least one
prediction of an impact
on a supply chain application based on a current state of a coordinated demand
management
application, such as a prediction that a demand for a good will decrease
earlier than previously
anticipated. The converse may also be true in that the set of predictions 3070
includes at least one
prediction of an impact on a demand management application based on a current
state of a
coordinated supply chain application, such as a prediction that a lack of
supply of a good will
likely impact a measure of demand of related goods. In embodiments, the set of
predictions 3070
is a set of predictions of adjustments in supply required to meet demand.
Other predictions
include at least one prediction of change in demand that impacts supply. Yet
other predictions in
the set of predictions predict a change in supply that impacts at least one of
the set of demand
management applications, such as a promotion application for at least one good
in the category of
goods. A prediction in the set of predictions may be as simple as setting a
likelihood that a supply
of a good in the category of goods will not meet demand set by a demand
setting application.
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[0321] In embodiments, the adaptive intelligence systems 614 may provide a set
of artificial
intelligence capabilities to facilitate providing the set of predictions for
the coordinated set of
demand management applications and supply chain applications. In one non-
limiting example,
the set of artificial intelligence capabilities may include a probabilistic
neural network that may
be used to predict a fault condition or a problem state of a demand management
application such
as a lack of sufficient validated feedback. The probabilistic neural network
may be used to
predict a problem state with a machine performing a value chain operation
(e.g., a production
machine, an automated handling machine, a packaging machine, a shipping
machine and the like)
based on a collection of machine operating information and preventive
maintenance information
for the machine.
[0322] In embodiments, the set of predictions 3070 may be provided by the
management
platform 102 directly through a set of adaptive artificial intelligence
systems.
[0323] In embodiments, the set of predictions 3070 may be provided for the
coordinated set of
demand management applications and supply chain applications for a category of
goods by
applying artificial intelligence capabilities for coordinating the set of
demand management
applications and supply chain applications.
[0324] In embodiments, the set of predictions 3070 may be predictions of
outcomes for
operating a value chain with the coordinated set demand management
applications and supply
chain applications for the category of goods, so that a user may conduct test
cases of coordinated
sets of demand management applications and supply chain applications to
determine which sets
may produce desirable outcomes (viable candidates for a coordinated set of
applications) and
which may produce undesirable outcomes.
[0325] Referring to Fig. 17, a management platform of an information
technology system, such
as a management platform for a value chain of goods and/or services is
depicted as a block
diagram of functional elements and representative interconnections for
providing a set of
classifications 3080. The management platform includes a user interface 3020
that provides,
among other things, a set of adaptive intelligence systems 614. The adaptive
intelligence systems
614 provide a set of classifications 3080 through, for example, the
application of artificial
intelligence, such as through application of an artificial intelligence system
1160, and optionally
through one or more expert systems, machine learning systems, and the like for
use with a
coordinated set of demand management applications 824 and supply chain
applications 812 for a
category of goods 3010, which may be produced, marketed, sold, resold, rented,
leased, given
away, serviced, recycled, renewed, enhanced, and the like through the value
chain. The adaptive
intelligence systems 614 may deliver the set of classifications 3080 through a
set of data
processing, artificial intelligence and computational systems 634. In
embodiments, the adaptive
intelligence systems 614 are selectable and/or configurable through the user
interface 3020 so
that one or more of the adaptive intelligence systems 614 can operate on or in
cooperation with
the coordinated sets of value chain applications. The adaptive intelligence
systems 614 may
include an artificial intelligence system that provides, among other things
classification
capabilities through any of the various expert systems, artificial
intelligence systems, neural
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networks, supervised learning systems, machine learning systems, deep learning
systems, and
other systems described throughout this disclosure and in the documents
incorporated by
reference. The adaptive intelligence systems 614 may facilitate applying
adapted intelligence
capabilities to the coordinated set of demand management applications 824 and
supply chain
applications 812 such as by producing a set of classifications 3080 that may
facilitate
coordinating the two sets of value chain applications, or at least facilitate
coordinating at least
one demand management application and at least one supply chain application
from their
respective sets.
[0326] In embodiments, the set of classifications 3080 includes at least one
classification of a
current state of a supply chain application for use by a coordinated demand
management
application, such as a classification of a problem state that may impact
operation of a demand
management application, such as a marketing application and the like. Such a
classification may
be useful in determining how to adjust a market expectation for a good that is
going to have a
lower yield than previously anticipated. The converse may also be true in that
the set of
classifications 3080 includes at least one classification of a current state
of a demand
management application and its relationship to a coordinated supply chain
application. In
embodiments, the set of classifications 3080 is a set of classifications of
adjustments in supply
required to meet demand, such as adjustments to production worker needs would
be classified
differently that adjustments in third-party logistics providers. Other
classifications may include at
least one classification of perceived changes in demand and a resulting
potential impact on
supply management. Yet other classifications in the set of classifications may
include a supply
chain application impact on at least one of the set of demand management
applications, such as a
promotion application for at least one good in the category of goods. A
classification in the set of
classifications may be as simple as classifying a likelihood that a supply of
a good in the category
of goods will not meet demand set by a demand setting application.
[0327] In embodiments, the adaptive intelligence systems 614 may provide a set
of artificial
intelligence capabilities to facilitate providing the set of classifications
3080 for the coordinated
set of demand management applications and supply chain applications. In one
non-limiting
example, the set of artificial intelligence capabilities may include a
probabilistic neural network
that may be used to classify fault conditions or problem states of a demand
management
application, such as a classification of a lack of sufficient validated
feedback. The probabilistic
neural network may be used to classify a problem state of a machine performing
a value chain
operation (e.g., a production machine, an automated handling machine, a
packaging machine, a
shipping machine and the like) as pertaining to at least one of machine
operating information and
preventive maintenance information for the machine.
[0328] In embodiments, the set of classifications 3080 may be provided by the
management
platform 102 directly through a set of adaptive artificial intelligence
systems. Further, the set of
classifications 3080 may be provided for the coordinated set of demand
management applications
and supply chain applications for a category of goods by applying artificial
intelligence
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capabilities for coordinating the set of demand management applications and
supply chain
applications.
[0329] In embodiments, the set of classifications 3080 may be classifications
of outcomes for
operating a value chain with the coordinated set demand management
applications and supply
chain applications for the category of goods, so that a user may conduct test
cases of coordinated
sets of demand management applications and supply chain applications to
determine which sets
may produce outcomes that are classified as desirable (e.g., viable candidates
for a coordinated
set of applications) and outcomes that are classified as undesirable.
[0330] In embodiments, the set of classifications may comprise a set of
adaptive intelligence
functions, such as a neural network that may be adapted to classify
information associated with
the category of goods. In an example, the neural network may be a multilayered
feed forward
neural network.
[0331] In embodiments, performing classifications may include classifying
discovered value
chain entities as one of demand centric and supply centric.
[0332] In embodiments, the set of classifications 3080 may be achieved through
use of artificial
intelligence systems 1160 for coordinating the set of coordinated demand
management and
supply chain applications. Artificial intelligence systems may configure and
generate sets of
classifications 3080 as a means by which demand management applications and
supply chain
applications can be coordinated. In an example, classification of information
flow throughout a
value chain may be classified as being relevant to both a demand management
application and a
supply chain application; this common relevance may be a point of coordination
among the
applications. In embodiments, the set of classifications may be artificial
intelligence generated
classifications of outcomes of operating a supply chain that is dependent on
the coordinated
demand management applications 824 and supply chain applications 812.
[0333] Referring to Fig. 18, a management platform of an information
technology system, such
as a management platform for a value chain of goods and/or services is
depicted as a block
diagram of functional elements and representative interconnections for
achieving automated
control intelligence. The management platform includes a user interface 3020
that provides,
among other things, a set of adaptive intelligence systems 614. The adaptive
intelligence systems
614 provide automated control signaling 3092 for a coordinated set of demand
management
applications 824 and supply chain applications 812 for a category of goods
3010, which may be
produced and sold through the value chain. The adaptive intelligence systems
614 may deliver
the automated control signals 3092 through a set of data processing,
artificial intelligence and
computational systems 634. In embodiments, the adaptive intelligence systems
614 are selectable
and/or configurable through the user interface 3020 so that one or more of the
adaptive
intelligence systems 614 can automatically control the sets of supply chain
applications (e.g.,
demand management applications 824 and supply chain applications 812). The
adaptive
intelligence systems 614 may include artificial intelligence including any of
the various expert
systems, artificial intelligence systems, neural networks, supervised learning
systems, machine
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learning systems, deep learning systems, and other systems described
throughout this disclosure
and in the documents incorporated by reference.
[0334] In embodiments, the user interface 3020 may include interfaces for
configuring an
adaptive intelligence systems 614 to take inputs from selected data sources of
the value chain
3094 (such as data sources used by the coordinated set of demand management
applications 824
and/or the set of supply chain applications 812) and supply them, such as to a
neural network,
artificial intelligence system 1160 or any of the other adaptive intelligence
systems 614 described
throughout this disclosure and in the documents incorporated herein by
reference for producing
automated control signals 3092, such as to enhance, control, improve,
optimize, configure, adapt
or have another impact on a value chain for the category of goods 3010. In
embodiments, the
selected data sources of the value chain may be used for determining aspects
of the automated
control signals, such as for temporal adjustments to control outcomes relating
to the value chain
at least for the category of goods 3010 and the like.
[0335] In an example, the set of automated control signals may include at
least one control
signal for automating execution of a supply chain application, such as a
production start, an
automated material order, an inventory check, a billing application and the
like in the coordinated
set of demand management applications and supply chain applications. In yet
another example of
automated control signal generation, the set of automated control signals may
include at least one
control signal for automating execution of a demand management application,
such as a product
recall application, an email distribution application and the like in the
coordinated set of demand
management applications and supply chain applications. In yet other examples,
the automate
control signals may control timing of demand management applications based on
goods supply
status.
[0336] In embodiments, the adaptive intelligence systems 614 may apply machine
learning to
outcomes of supply to automatically adapt a set of demand management
application control
signals. Similarly, the adaptive intelligence systems 614 may apply machine
learning to
outcomes of demand management to automatically adapt a set of supply chain
application control
signals. The adaptive intelligence systems 614 may provide further processing
for automated
control signal generation, such as by applying artificial intelligence to
determine aspects of a
value chain that impact automated control of the coordinated set of demand
management
applications and supply chain applications for a category of goods. The
determined aspects could
be used in the generation and operation of automated control
intelligence/signals, such as by
filtering out value chain information for aspects that do not impact the
targeted demand
management and supply chain applications.
[0337] Automated control of, for example, supply chain applications may be
restricted, such as
by policy, operational limits, safety constraints and the like. The set of
adaptive intelligence
systems may determine a range of supply chain application control values
within which control
can be automated. In embodiments, the range may be associated with a supply
rate, a supply
timing rate, a mix of goods in a category of goods, and the like.
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[0338] Embodiments are described herein for using artificial intelligence
systems or
capabilities to identify, configure and regulate automated control signals.
Such embodiments may
further include a closed loop of feedback from the coordinated set of demand
management and
supply chain applications (e.g., state information, output information,
outcomes and the like) that
is optionally processed with machine learning and used to adapt the automated
control signals for
at least one of the goods in the category of goods. An automated control
signal may be adapted
based on, for example, an indication of feedback from a supply chain
application that yield of a
good suggests a production problem. In this example, the automated control
signal may impact
production rate and the feedback may cause the signal to automatically self-
adjust to a slower
production rate until the production problem is resolved.
[0339] Referring to Fig. 19, a management platform of an information
technology system, such
as a management platform for a value chain of goods and/or services is
depicted as a block
diagram of functional elements and representative interconnections for
providing information
routing recommendations. The management platform includes a set of value chain
networks 3102
from which network data 3110 is collected from a set of information routing
activities, the
information including outcomes, parameters, routing activity information and
the like. Within the
set of value chain networks 3102 is selected a select value chain network 3104
for which at least
one information routing recommendation 3130 is provided. An artificial
intelligence system 1160
may include a machine learning system and may be trained using a training set
derived from the
network data 3110 outcomes, parameters and routing activity information for
the set of value
chain networks 3102. The artificial intelligence system 1160 may further
provide an information
routing recommendation 3130 based on a current status 3120 of the select value
chain network
3104. The artificial intelligence system may use machine learning to train on
information
transaction types within the set of value chain networks 3102, thereby
learning pertinent factors
regarding different transaction types (e.g., real-time inventory updates,
buyer credit checks,
engineering signoff, and the like) and contributing to the information routing
recommendation
accordingly. The artificial intelligence system may also use machine learning
to train on
information value for different types and/or classes of information routed in
and throughout the
set of value chain networks 3102. Information may be valued on a wide range of
factors,
including timing of information availability and timing of information
consumption as well as
information content-based value, such as information without which a value
chain network
element (e.g., a production provider) cannot perform a desired action (e.g.,
starting volume
production without a work order). Therefore information routing
recommendations may be based
on training on transaction type, information value, and a combination thereof.
These are merely
exemplary information routing recommendation training and recommendation basis
factors and
are presented here without limitation on other elements for training and
recommendation basis.
[0340] In embodiments, the artificial intelligence system 1160 may provide an
information
routing recommendation 3130 based on transaction type, transaction type and
information type,
network type and the like. An information routing recommendation may be based
on
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combinations of factors, such as information type and network type, such as
when an information
type (streaming) is not compatible with a network type (small transactions).
[0341] In embodiments, the artificial intelligence system 1160 may use machine
learning to
develop an understanding of networks within the selected value chain network
3104, such as
network topology, network loading, network reliability, network latency and
the like. This
understanding may be combined with, for example, detected or anticipated
network conditions to
form an information routing recommendation. Aspects such as existence of edge
intelligence in a
value chain network 3104 can influence one or more information routing
recommendations. In an
example, a type of information may be incompatible with a network type;
however the network
may be configured with edge intelligence that can be leveraged by the
artificial intelligence
system 1160 to adapt the form of the information being routed so that it is
compatible with a
targeted network type. This is also an example of more general consideration
for information
routing recommendation ¨ network resources (e.g., presence, availability, and
capability), such as
edge computing, server access, network-based storage resources and the like.
Likewise, value
chain network entities may impact information routing recommendations. In
embodiments, an
information routing recommendation may avoid routing information that is
confidential to a first
supplier in the value chain through network nodes controlled by competitors of
the supplier. In
embodiments, an information routing recommendation may include routing
information to a first
node where it is partially consumed and partially processed for further
routing, such as by
splitting up the portion partially processed for further routing into
destination-specific
information sets.
[0342] In embodiments, an artificial intelligence system 1160 may provide an
information
routing recommendation based on goals, such as goals of a value chain network,
goals of
information routing, and the like. Goal-based information routing
recommendations may include
routing goals, such as Quality of Service routing goals, routing reliability
goals (which may be
measured based on a transmission failure rate and the like). Other goals may
include a measure
of latency associated with one or more candidate routes. An information
routing recommendation
may be based on the availability of information in a selected value chain
network, such as when
information is available and when it needs to be delivered. For information
that is available well
ahead of when it is needed (e.g., a nightly production report that is
available for routing at 2 AM
is first needed by 7 AM), routing recommendations may include using resources
that are lower
cost, may involve short delays in routing and the like. For information that
is available just before
it is needed (e.g., a result of product testing is needed within a few hundred
milliseconds of when
the test is finished to maintain a production operation rate, and the like).
[0343] An information routing recommendation may be formed by the artificial
intelligence
system 1160 based on information persistence factors, such as how long
information is available
for immediate routing within the value chain network. An information routing
recommendation
that factors information persistence may select network resources based on
availability, cost and
the like during a time of information persistence.
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[0344] Information value and an impact on information value may factor into an
information
routing recommendation. As an example, information that is valid for a single
shipment (e.g., a
production run of a good) may substantively lose value once the shipment has
been satisfactorily
received. In such an example, an information routing recommendation may
indicate routing the
relevant information to all of the highest priority consumers of the
information while it is still
valid. Likewise, routing of information that is consumed by more than one
value chain entity
may need to be coordinated so that each value chain entity receives the
information at a desired
time/moment, such as during the same production shift, at their start of day,
which may be
different if the entities are in different time zones, and the like.
[0345] In embodiments, information routing recommendations may be based on a
topology of a
value chain, based on location and availability of network storage resources,
and the like.
[0346] In embodiments, one or more information routing recommendations may be
adapted
while the information is routed based on, for example, changes in network
resource availability,
network resource discovery, network dynamic loading, priority of
recommendations that are
generated after information for a first recommendation is in-route, and the
like.
[0347] Referring to Fig. 20, a management platform of an information
technology system, such
as a management platform for a value chain of goods and/or services is
depicted as a block
diagram of functional elements and representative interconnections for semi-
sentient problem
recognitions of pain points in a value chain network. The management platform
includes a set of
value chain network entities 3152 from which entity-related data 3160 is
collected and includes
outcomes, parameters, activity information and the like associated with the
entities. Within the
set of value chain network entities 3152 is selected a set of select value
chain network entities
3154 for which at least one pain point problem state 3172 is detected. An
artificial intelligence
system 1160 may be training on a training set derived from the entity-related
data 3160 including
training on outcomes associated with value chain entities, parameters
associated with, for
example, operation of the value chain, value chain activity information and
the like. The artificial
intelligence system may further employ machine learning to facilitate learning
problem state
factors 3180 that may characterize problem states input as training data.
These factors 3180 may
further be used by an instance of artificial intelligence 1160' that operates
on computing
resources 3170 that are local to value chain network entities that are
experiencing the
problem/result of a pain point. A goal of such a configuration of artificial
intelligence systems,
data sets, and value chain networks is to recognize a problem state in a
portion of the selected
value chain.
[0348] In embodiments, recognizing problem states may be based on variance
analysis, such as
variances that occur in value chain measures (e.g., loading, latency, delivery
time, cost, and the
like), particularly in a specific measure over time. Variances that exceed a
variance threshold
(e.g., an optionally dynamic range of results of a value chain operation, such
as production,
shipping, clearing customs, and the like) may be indicative of a pain point.
[0349] In addition to detecting problem states, the platform 102, such as
through the methods of
semi-sentient problem recognition, predict a pain point based at least in part
on a correlation with
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a detected problem state. The correlation may be derived from the value chain,
such as a shipper
cannot deliver international goods until they are processed through customs,
or a sales forecast
cannot be provided with a high degree of confidence without high quality field
data and the like.
In embodiments, a predicted pain point may be a point of value chain activity
further along a
supply chain, an activity that occurs in a related activity (e.g., tax
planning is related to tax laws),
and the like. A predicted pain point may be assigned a risk value based on
aspects of the detected
problem state and correlations between the predicted pain point activity and
the problem state
activity. If a production operation can receive materials from two suppliers,
a problem state with
one of the suppliers may indicate a low risk of a pain point of use of the
material. Likewise, if a
demand management application indicates high demand for a good and a problem
is detected
with information on which the demand is based, a risk of excess inventory
(pain point) may be
high depending on, for example how far along in the value chain the good has
progressed.
[0350] In embodiments, semi-sentient problem recognition may involve more than
mere
linkages of data and operational states of entities engaged in a value chain.
Problem recognition
may also be based on human factors, such as perceived stress of production
supervisors, shippers,
and the like. Human factors for use in semi-sentient problem recognition may
be collected from
sensors that facilitate detection of human stress level and the like (e.g.,
wearable physiological
sensors, and the like).
[0351] In embodiments, semi-sentient problem recognition may also be based on
unstructured
information, such as digital communication, voice messaging, and the like that
may be shared
among, originate with, or be received by humans involved in the value chain
operations. As an
example, natural language processing of email communications among workers in
an enterprise
may indicate a degree of discomfort with, for example, a supplier to a value
chain. While data
associated with the supplier (e.g., on-time production, quality, and the like)
may be within a
variance range deemed acceptable, information within this unstructured content
may indicate a
potential pain point, such as a personal issue with a key participant at the
supplier and the like.
By employing natural language processing, artificial intelligence, and
optionally machine
learning, problem state recognition may be enhanced.
[0352] In embodiments, semi-sentient problem recognition may be based on
analysis of
variances of measures of a value chain operation/entity/application including
variance of a given
measure over time, variance of two related measures, and the like. In
embodiments, variance in
outcomes over time may indicate a problem state and/or suggest a pain point.
In embodiments, an
artificial intelligence-based system may determine an acceptable range of
outcome variance and
apply that range to measures of a select set of value chain network entities,
such as entities that
share one or more similarities, to facilitate detection of a problem state. In
embodiments, an
acceptable range of outcome variance may indicate a problem state trigger
threshold that may be
used by a local instance of artificial intelligence to signal a problem state.
In such a scenario, a
problem state may be detected when at least one measure of the value chain
activity/entity and
the like is greater than the artificial intelligence-determined problem state
threshold. Variance
analysis for problem state detection may include detecting variances in
start/end times of
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scheduled value chain network entity activities, variances in at least one of
production time,
production quality, production rate, production start time, production
resource availability or
trends thereof, variances in a measure of shipping supply chain entity,
variances in a duration of
time for transfer from one mode of transport to another (e.g., when the
variance is greater than a
transport mode problem state threshold), variances in quality testing, and the
like.
[0353] In embodiments, a semi-sentient problem recognition system may include
a machine
learning/artificial intelligence prediction of a correlated pain point further
along a supply chain
due to a detected pain point, such as a risk and/or need for overtime,
expedited shipping,
discounting goods prices, and the like.
[0354] In embodiments, a machine learning / artificial intelligence system may
process
outcomes, parameters, and data collected from a set of data sources relating
to a set of value
chain entities and activities to detect at least one pain point selected from
the list of pain points
consisting of late shipment, damaged container, damaged goods, wrong goods,
customs delay,
unpaid duties, weather event, damaged infrastructure, blocked waterway,
incompatible
infrastructure, congested port, congested handling infrastructure, congested
roadway, congested
distribution center, rejected goods, returned goods, waste material, wasted
energy, wasted labor
force, untrained workforce, poor customer service, empty transport vehicle on
return route,
excessive fuel prices, excessive tariffs, and the like.
[0355] Referring to Fig. 21, a management platform of an information
technology system, such
as a management platform for a value chain of goods and/or services is
depicted as a block
diagram of functional elements and representative interconnections automated
coordination of a
set of value chain network activities for a set of products of an enterprise.
The management
platform includes a set of network-connected value chain network entities 3202
that produce
activity information 3208 that is used by an artificial intelligence system
1160 to provide
automate coordination 3220 of value chain network activities 3212 for a set of
products 3210 for
an enterprise 3204. In embodiments, value chain monitoring systems 614 may
monitor activities
of the set of network-connected value chain entities 3202 and work
cooperatively with data
collection and management systems 640 to gather and store value chain entity
monitored
information, such as activity information, configuration information, and the
like. This gathered
information may be configured as activity information 3208 for a set of
activities associated with
a set of products 3210 of an enterprise 3204. In embodiments, the artificial
intelligence systems
1160 may use application programming connectivity facilities 642 for
automating access to the
monitored activity information 3208.
[0356] A value chain may include a plurality of interconnected entities that
each perform
.. several activities for completing the value chain. While humans play a
critical role in some
activities within a value chain network, greater automated coordination and
unified orchestration
of supply and demand may be achieved using artificial intelligence-type
systems (e.g., machine
learning, expert systems, self-organizing systems, and the like including such
systems describe
herein and in the documents incorporated herein by reference) for coordinating
supply chain
activities. Use of artificial intelligence may further enrich the emerging
nature of self-adapting
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systems, including Internet of Things (IoT) devices and intelligent products
and the like that not
only provide greater capabilities to end users, but can play a critical role
in automated
coordination of supply chain activities.
[0357] For example, an IoT system deployed in a fulfillment center 628 may
coordinate with an
intelligent product 650 that takes customer feedback about the product 650,
and an application
630 for the fulfillment center 628 may, upon receiving customer feedback via a
connection path
to the intelligent product 650 about a problem with the product 650, initiate
a workflow to
perform corrective actions on similar products 650 before the products 650 are
sent out from the
fulfillment center 628. The workflow may be configured by an artificial
intelligence system 1160
that analyzes the problem with the product 650, develops an understanding of
value chain
network activities that produce the product, determines resources required for
the workflow,
coordinates with inventory and production systems to adapt any existing
workflows and the like.
Artificial intelligence systems 1160 may further coordinate with demand
management
applications to address any temporary impact on product availability and the
like.
[0358] In embodiments, automated coordination of a set of value chain network
activities for a
set of products for an enterprise may rely on the methods and systems of
coordinated intelligence
described herein, such as to facilitate coordinating demand management
activities, supply chain
activities and the like, optionally using artificial intelligence for
providing the coordinated
intelligence, coordinating the activities and the like. As an example,
artificial intelligence may
facilitate determining relationships among value change network activities
based on inputs used
by the activities and results produced by the activities. Artificial
intelligence may be integrated
with and/or work cooperatively with activities of the platform, such as value
chain network entity
activities to continuously monitor activities, identify temporal aspects
needing coordination (e.g.,
when changes in supply temporally impact demand activities), and automate such
coordination.
Automated coordination of value chain network activities within and across
value chain network
entity activities may benefit from advanced artificial intelligence systems
that may enable use of
differing artificial intelligence capabilities for any given value chain set
of entities, applications,
or conditions. Use of hybrid artificial intelligence systems may provide
benefits by applying
more than one type of intelligence to a set of conditions to facilitate human
and/or computer
automated selection thereof. Artificial intelligence can further enhance
automated coordination of
value chain network entity activities through intelligent operations such as
generating sets of
predictions, sets of classifications, generation of automate control signals
(that may be
communicated across value chain network entities and the like). Other
exemplary artificial
intelligence-based influences on automated coordination of value chain network
entity activities
include machine learning-based information routing and recommendations
thereto, semi-sentient
problem recognition based on both structured (e.g., production data) and
unstructured (e.g.,
human emotions) sources, and the like. Artificial intelligence systems may
facilitate automated
coordination of value chain network entity activities for a set of products or
an enterprise based
on adaptive intelligence provided by the platform for a category of goods
under which the set of
products of an enterprise may be grouped. In an example, adaptive intelligence
may be provided
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by the platform for a drapery hanging category of goods and a set of products
for an enterprise
may include a line of adaptable drapery hangers. Through understanding
developed for the
overall drapery hanging category, artificial intelligence capabilities may be
applied to value chain
network activities of the enterprise for automating aspects of the value
chain, such as information
exchange among activities and the like.
DIGITAL TWIN SYSTEM IN VALUE CHAIN ENTITY MANAGEMENT PLATFORM
[0359] Referring to Fig. 22, the adaptive intelligence layer 614 may include a
value chain
network digital twin system 1700, which may include a set of components,
processes, services,
interfaces and other elements for development and deployment of digital twin
capabilities for
visualization of various value chain entities 652, environments, and
applications 630, as well as
for coordinated intelligence (including artificial intelligence 1160, edge
intelligence 1400,
analytics and other capabilities) and other value-added services and
capabilities that are enabled
or facilitated with a digital twin 1700. Without limitation, a digital twin
1700 may be used for
and/or applied to each of the processes that are managed, controlled, or
mediated by each of the
set of applications 630 of the platform application layer.
[0360] In embodiments, the digital twin 1700 may take advantage of the
presence of multiple
applications 630 within the value chain management platform layer 604, such
that a pair of
applications may share data sources (such as in the data storage layer 624)
and other inputs (such
as from the monitoring layer 614) that are collected with respect to value
chain entities 652, as
well as sharing outputs, events, state information and outputs, which
collectively may provide a
much richer environment for enriching content in a digital twin 1700,
including through use of
artificial intelligence 1160 (including any of the various expert systems,
artificial intelligence
systems, neural networks, supervised learning systems, machine learning
systems, deep learning
systems, and other systems described throughout this disclosure and in the
documents
incorporated by reference) and through use of content collected by the
monitoring layer 614 and
data collection systems 640.
[0361] In embodiments, a digital twin 1700 may be used in connection with
shared or
converged processes among the various pairs of the applications 630 of the
application layer 604,
such as, without limitation, of a converged process involving a security
application 834 and an
inventory management application 820, integrated automation of blockchain-
based applications
844 with facility management applications 850, and many others. In
embodiments, converged
processes may include shared data structures for multiple applications 630
(including ones that
track the same transactions on a blockchain but may consume different subsets
of available
attributes of the data objects maintained in the blockchain or ones that use a
set of nodes and
links in a common knowledge graph) that may be connected to with the digital
twin 1700 such
that the digital twin 1700 is updated accordingly. For example, a transaction
indicating a change
of ownership of an entity 652 may be stored in a blockchain and used by
multiple applications
630, such as to enable role-based access control, role-based permissions for
remote control,
identity-based event reporting, and the like that may be connected to and
shared with the digital
twin 1700 such that the digital twin 1700 may be updated accordingly. In
embodiments,
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converged processes may include shared process flows across applications 630,
including subsets
of larger flows that are involved in one or more of a set of applications 630
that may be
connected to and shared with the digital twin 1700 such that the digital twin
1700 may be
updated accordingly. For example, an inspection flow about a value chain
network entity 652
may serve an analytics solution 838, an asset management solution 814, and
others.
[0362] In embodiments, a digital twin 1700 may be provided for the wide range
of value chain
network applications 630 mentioned throughout this disclosure and the
documents incorporated
herein by reference. An environment for development of a digital twin 1700 may
include a set of
interfaces for developers in which a developer may configure an artificial
intelligence system
1160 to take inputs from selected data sources of the data storage layer 624
and events or other
data from the monitoring systems layer 614 and supply them for inclusion in a
digital twin 1700.
A digital twin 1700 development environment may be configured to take outputs
and outcomes
from various applications 630.
VALUE CHAIN NETWORK DIGITAL TWINS
[0363] Referring to Fig. 23, any of the value chain network entities 652 can
be depicted in a set
of one or more digital twins 1700, such as by populating the digital twin 1700
with value chain
network data object 1004, such as event data 1034, state data 1140, or other
data with respect to
value chain network entities 652, applications 630, or components or elements
of the platform
604 as described throughout this disclosure.
[0364] Thus, the platform 604 may include, integrate, integrate with, manage,
control,
coordinate with, or otherwise handle any of a wide variety of digital twins
1700, such as
distribution twins 1714 (such as representing distribution facilities, assets,
objects, workers, or
the like); warehousing twins 1712 (such as representing warehouse facilities,
assets, objects,
workers and the like); port infrastructure twins 1714 (such as representing a
seaport, an airport,
or other facility, as well as assets, objects, workers and the like); shipping
facility twins 1720;
operating facility twins 1722; customer twins 1730 (such as representing
physical, behavioral,
demographic, psychographic, financial, historical, affinity, interest, and
other characteristics of
groups of customers or individual customers) ; worker twins 1740 (such as
representing physical
attributes, physiologic data, status data, psychographic information,
emotional states, states of
fatigue/energy, states of attention, skills, training, competencies, roles,
authority, responsibilities,
work status, activities, and other attributes of or involving workers);
wearable/portable device
twins 1750; process twins 1760; machine twins 1770 (such as for various
machines used to
support a value chain network 668); product twins 1780; point of origin twins
1560; supplier
twins 1630; supply factor twins 1650; maritime facility twins 1572; floating
asset twins 1570;
shipyard twins 1620; destination twins 1562; fulfillment twins 1600; delivery
system twins 1610;
demand factor twins 1640; retailer twins 1790; ecommerce and online site and
operator twins
1800; waterway twins 1810; roadway twins 1820; railway twins 1830; air
facility twins 1840
(such as twins of aircraft, runways, airports, hangars, warehouses, air travel
routes, refueling
facilities and other assets, objects, workers and the like used in connection
with air transport of
products 650); autonomous vehicle twins 1850; robotics twins 1860; drone twins
1870; and
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logistics factor twins 1880; among others. Each of these may have
characteristics of digital twins
described throughout this disclosure and the documents incorporated by
reference herein, such as
mirroring or reflecting changes in states of associated physical objects or
other entities, providing
capabilities for modeling behavior or interactions of associated physical
objects or other entities,
enabling simulations, providing indications of status, and many others.
[0365] In example embodiments, a digital twin system may be configured to
generate a variety
of enterprise digital twins 1700 in connection with a value chain (e.g.,
specifically value chain
network entities 652). For example, an enterprise that produces goods
internationally (or at
multiple facilities) may configure a set of digital twins 1700, such as
supplier twins that depict
the enterprise's supply chain, factory twins of the various production
facilities, product twins that
represent the products made by the enterprise, distribution twins that
represent the enterprise's
distribution chains, and other suitable twins. In doing so, the enterprise may
define the structural
elements of each respective digital twin as well as any system data that
corresponds to the
structural elements of the digital twin. For instance, in generating a
production facility twin, the
enterprise may the layout and spatial definitions of the facility and any
processes that are
performed in the facility. The enterprise may also define data sources
corresponding to the value
chain network entities 652, such as sensor systems, smart manufacturing
equipment, inventory
systems, logistics systems, and the like that provide data relevant to the
facility. The enterprise
may associate the data sources with elements of the production facility and/or
the processes
occurring the facility. Similarly, the enterprise may define the structural,
process, and layout
definitions of its supply chain and its distribution chain and may connect
relevant data sources,
such as supplier databases, logistics platforms, to generate respective
distribution chain and
supply chain twins. The enterprise may further associate these digital twins
to have a view of its
value chain. In embodiments, the digital twin system may perform simulations
of the enterprise's
value chain that incorporate real-time data obtained from the various value
chain network entities
652 of the enterprise. In some of these embodiments, the digital twin system
may recommend
decisions to a user interacting with the enterprise digital twins 1700, such
as when to order
certain parts for manufacturing a certain product given a predicted demand for
the manufactured
product, when to schedule maintenance on machinery and/or replace machinery
(e.g., when
digital simulations on the digital twin indicates the demand for certain
products may be the
lowest or when it would have the least effect on the enterprise's profits and
losses statement),
what time of day to ship items, or the like. The foregoing example is a non-
limiting example of
the manner by which a digital twin may ingest system data and perform
simulations in order to
further one or more goals.
ENTITY DISCOVERY AND INTERACTION MANAGEMENT
[0366] Referring to Fig. 24, the monitoring systems layer 614, including
various data collection
systems 640 (such as IoT data collection systems, data collection systems that
search social
networks, websites, and other online resources, crowdsourcing systems, and
others) may include
a set of entity discovery systems 1900, such as for identifying sets of value
chain network entities
652, identifying types of value chain network entities 652, identifying
specific value chain
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network entities 652 and the like, as well as for managing identities of the
value chain network
entities 652, including for resolving ambiguities (such as where a single
entity is identified
differently in different systems, where different entities are identified
similarly, and the like), for
entity identity deduplication, for entity identity resolution, for entity
identity enhancement (such
as by enriching data objects with additional data that is collected about an
entity within the
platform), and the like. Entity discovery 1900 may also include discovery of
interactions among
entities, such as how entities are connected (e.g., by what network
connections, data integration
systems, and/or interfaces), what data is exchanged among entities (including
what types of data
objects are exchanged, what common workflows involve entities, what inputs and
outputs are
exchanged between entities, and the like), what rules or policies govern the
entities, and the like.
The platform 604 may include a set of entity interaction management systems
1902, which may
comprise one or more artificial intelligence systems (including any of the
types described
throughout this disclosure) for managing a set of interactions among entities
that are discovered
through entity discovery 1900, including ones that learn on a training set of
data to manage
interactions among entities based on how entities have been managed by human
supervisors or
by other systems.
[0367] As an illustrative example among many possible ones, the entity
discovery system 1900
may be used to discover a network-connected camera that shows the loading dock
of facility that
produces a product for an enterprise, as well as to identify what interfaces
or protocols are
needed to access a feed of video content from the camera. The entity
interaction management
system 1902 may then be used to interact with the interfaces or protocols to
set up access to the
feed and to provide the feed to another system for further processing, such as
to have an artificial
intelligence system 1160 process the feed to discovery content that is
relevant to an activity of
the enterprise. For example, the artificial intelligence system 1160 may
process image frames of
the video feed to find markings (such as produce labels, SKUs, images, logos,
or the like), shapes
(such as packages of a particular size or shape), activities (such as loading
or unloading
activities) or the like that may indicate that a product has moved through the
loading dock. This
information may substitute for, augment, or be used to validate other
information, such as RFID
tracking information or the like. Similar discovery and interaction management
activities may be
undertaken with any of the types of value chain network entities 652 described
throughout this
disclosure.
ROBOTIC PROCESS AUTOMATION IN VALUE CHAIN NETWORK
[0368] Referring to Fig. 25, the adaptive intelligence layer 614 may include a
robotic process
automation (RPA) system 1442, which may include a set of components,
processes, services,
interfaces and other elements for development and deployment of automation
capabilities for
various value chain entities 652, environments, and applications 630. Without
limitation, robotic
process automation 1442 may be applied to each of the processes that are
managed, controlled, or
mediated by each of the set of applications 630 of the platform application
layer, to functions,
components, workflows, processes of the VCNP 604 itself, to processes
involving value chain
network entities 652 and other processes.
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[0369] In embodiments, robotic process automation 1442 may take advantage of
the presence
of multiple applications 630 within the value chain management platform layer
604, such that a
pair of applications may share data sources (such as in the data storage layer
624) and other
inputs (such as from the monitoring layer 614) that are collected with respect
to value chain
entities 652, as well as sharing outputs, events, state information and
outputs, which collectively
may provide a much richer environment for process automation, including
through use of
artificial intelligence 1160 (including any of the various expert systems,
artificial intelligence
systems, neural networks, supervised learning systems, machine learning
systems, deep learning
systems, and other systems described throughout this disclosure and in the
documents
incorporated by reference). For example, an asset management application 814
may use robotic
process automation 1442 for automation of an asset inspection process that is
normally
performed or supervised by a human (such as by automating a process involving
visual
inspection using video or still images from a camera or other that displays
images of an entity
652, such as where the robotic process automation 1442 system is trained to
automate the
inspection by observing interactions of a set of human inspectors or
supervisors with an interface
that is used to identify, diagnose, measure, parameterize, or otherwise
characterize possible
defects or favorable characteristics of a facility or other asset. In
embodiments, interactions of the
human inspectors or supervisors may include a labeled data set where labels or
tags indicate
types of defects, favorable properties, or other characteristics, such that a
machine learning
system can learn, using the training data set, to identify the same
characteristics, which in turn
can be used to automate the inspection process such that defects or favorable
properties are
automatically classified and detected in a set of video or still images, which
in turn can be used
within the value chain network asset management application 814 to flag items
that require
further inspection, that should be rejected, that should be disclosed to a
prospective buyer, that
should be remediated, or the like. In embodiments, robotic process automation
1442 may involve
multi-application or cross-application sharing of inputs, data structures,
data sources, events,
states, outputs or outcomes. For example, the asset management application 814
may receive
information from a marketplace application 854 that may enrich the robotic
process automation
1442 of the asset management application 814, such as information about the
current
characteristics of an item from a particular vendor in the supply chain for an
asset, which may
assist in populating the characteristics about the asset for purposes of
facilitating an inspection
process, a negotiation process, a delivery process, or the like. These and
many other examples of
multi-application or cross-application sharing for robotic process automation
1442 across the
applications 630 are encompassed by the present disclosure. Robotic process
automation 1442
may be used with various functionality of the VCNP 604. For example, in some
embodiments,
robotic process automation 1442 may be described as training a robot to
operate and automate a
task that was, to at least a large extent, governed by a human. One of these
tasks may be used to
train a robot that may train other robots. The robotic process automation 1442
may be trained
(e.g., through machine learning) to mimic interactions on a training set, and
then have this trained
robotic process automation 1442 (e.g., trained agent or trained robotic
process automation
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system) execute these tasks that were previously performed by people. For
example, the robotic
process automation 1442 may utilize software that may provide software
interaction observations
(such as mouse movements, mouse clicks, cursor movements, navigation actions,
menu
selections, keyboard typing, and many others), such as logged and/or tracked
by software
interaction observation system 1500, purchase of the product by a customer
714, and the like.
This may include monitoring of a user's mouse clicks, mouse movements, and/or
keyboard
typing to learn to do the same clicks and/or typing. In another example, the
robotic process
automation 1442 may utilize software to learn physical interactions with
robots and other
systems to train a robotic system to sequence or undertake the same physical
interactions. For
example, the robot may be trained to rebuild a set of bearings by having the
robot watch a video
of someone doing this task. This may include tracking physical interactions
and tracking
interactions at a software level. The robotic process automation 1442 may
understand what the
underlying competencies are that are being deployed such that the VCNP 604
preconfigure
combinations of neural networks that may be used to replicate performance of
human
capabilities.
[0370] In embodiments, robotic process automation may be applied to shared or
converged
processes among the various pairs of the applications 630 of the application
layer 604, such as,
without limitation, of a converged process involving a security application
834 and an inventory
application 820, integrated automation of blockchain-based applications 844
with vendor
management applications 832, and many others. In embodiments, converged
processes may
include shared data structures for multiple applications 630 (including ones
that track the same
transactions on a blockchain but may consume different subsets of available
attributes of the data
objects maintained in the blockchain or ones that use a set of nodes and links
in a common
knowledge graph). For example, a transaction indicating a change of ownership
of an entity 652
may be stored in a blockchain and used by multiple applications 630, such as
to enable role-
based access control, role-based permissions for remote control, identity-
based event reporting,
and the like. In embodiments, converged processes may include shared process
flows across
applications 630, including subsets of larger flows that are involved in one
or more of a set of
applications 630. For example, a risk management or inspection flow about an
entity 652 may
serve an inventory management application 832, an asset management application
814, a demand
management application 824, and a supply chain application 812, among others.
[0371] In embodiments, robotic process automation 1442 may be provided for the
wide range
of value chain network processes mentioned throughout this disclosure and the
documents
incorporated herein by reference, including without limitation all of the
applications 630. An
environment for development of robotic process automation for value chain
networks may
include a set of interfaces for developers in which a developer may configure
an artificial
intelligence system 1160 to take inputs from selected data sources of the VCN
data storage layer
624 and event data 1034, state data 1140 or other value chain network data
objects 1004 from the
monitoring systems layer 614 and supply them, such as to a neural network,
either as inputs for
classification or prediction, or as outcomes relating to the platform 102,
value chain network
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entities 652, applications 630, or the like. The RPA development environment
1442 may be
configured to take outputs and outcomes 1040 from various applications 630,
again to facilitate
automated learning and improvement of classification, prediction, or the like
that is involved in a
step of a process that is intended to be automated. In embodiments, the
development
environment, and the resulting robotic process automation 1442 may involve
monitoring a
combination of both software program interaction observations 1500 (e.g., by
workers interacting
with various software interfaces of applications 630 involving value chain
network entities 652)
and physical process interaction observations 1510 (e.g., by watching workers
interacting with or
using machines, equipment, tools or the like in a value chain network 668). In
embodiments,
observation of software interactions 1500 may include interactions among
software components
with other software components, such as how one application 630 interacts via
APIs with another
application 630. In embodiments, observation of physical process interactions
1510 may include
observation (such as by video cameras, motion detectors, or other sensors, as
well as detection of
positions, movements, or the like of hardware, such as robotic hardware) of
how human workers
interact with value chain entities 652 (such as locations of workers
(including routes taken
through a location, where workers of a given type are located during a given
set of events,
processes or the like, how workers manipulate pieces of equipment, cargo,
containers, packages,
products 650 or other items using various tools, equipment, and physical
interfaces, the timing of
worker responses with respect to various events (such as responses to alerts
and warnings),
procedures by which workers undertake scheduled deliveries, movements,
maintenance, updates,
repairs and service processes; procedures by which workers tune or adjust
items involved in
workflows, and many others). Physical process observation 1510 may include
tracking positions,
angles, forces, velocities, acceleration, pressures, torque, and the like of a
worker as the worker
operates on hardware, such as on a container or package, or on a piece of
equipment involved in
handling products, with a tool. Such observations may be obtained by any
combination of video
data, data detected within a machine (such as of positions of elements of the
machine detected
and reported by position detectors), data collected by a wearable device (such
as an exoskeleton
that contains position detectors, force detectors, torque detectors and the
like that is configured to
detect the physical characteristics of interactions of a human worker with a
hardware item for
purposes of developing a training data set). By collecting both software
interaction observations
1500 and physical process interaction observations 1510 the RPA system 1442
can more
comprehensively automate processes involving value chain entities 652, such as
by using
software automation in combination with physical robots.
[0372] In embodiments, robotic process automation 1442 is configured to train
a set of physical
robots that have hardware elements that facilitate undertaking tasks that are
conventionally
performed by humans. These may include robots that walk (including walking up
and down
stairs to deliver a package), climb (such as climbing ladders in a warehouse
to reach shelves
where products 650 are stored), move about a facility, attach to items, grip
items (such as using
robotic arms, hands, pincers, or the like), lift items, carry items, remove
and replace items, use
tools and many others.
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VALUE CHAIN MANAGEMENT PLATFORM ¨ UNIFIED ROBOTIC PROCESS AUTOMATION FOR
DEMAND MANAGEMENT AND SUPPLY CHAIN
[0373] In embodiments, provided herein are methods, systems, components and
other elements
for an information technology system that may include a cloud-based management
VCNP 604
with a micro-services architecture, a set of interfaces 702, a set of network
connectivity facilities
642, adaptive intelligence facilities 614, data storage facilities 624, data
collection systems 640,
and monitoring facilities 614 that are coordinated for monitoring and
management of a set of
value chain network entities 652; a set of applications for enabling an
enterprise to manage a set
of value chain network entities from a point of origin to a point of customer
use; and a unified set
of robotic process automation systems 1442 that provide coordinated automation
among various
applications 630, including demand management applications, supply chain
applications,
intelligent product applications and enterprise resource management
applications for a category
of goods.
[0374] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a unified set of robotic process automation systems
that provide
coordinated automation among at least two types of applications from among a
set of demand
management applications, a set of supply chain applications, a set of
intelligent product
applications and a set of enterprise resource management applications for a
category of goods.
VALUE CHAIN MANAGEMENT PLATFORM ¨ ROBOTIC PROCESS AUTOMATION SERVICES IN
MICROSERVICES ARCHITECTURE FOR VALUE CHAIN NETWORK
[0375] In embodiments, provided herein are methods, systems, components and
other elements
for an information technology system that may include a cloud-based management
VCNP 102
with a micro-services architecture, a set of interfaces 702, a set of network
connectivity facilities
642, adaptive intelligence facilities 614, data storage facilities 624, data
collection systems 640,
and monitoring facilities 614 that are coordinated for monitoring and
management of a set of
value chain network entities 652; a set of applications for enabling an
enterprise to manage a set
of value chain network entities from a point of origin to a point of customer
use; and a set of
microservices layers including an application layer supporting at least one
supply chain
application and at least one demand management application, wherein the
microservice layers
include a robotic process automation layer 1442 that uses information
collected by a data
collection layer 640 and a set of outcomes and activities 1040 involving the
applications of the
application layer 630 to automate a set of actions for at least a subset of
the applications 630.
[0376] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
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intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a set of microservices layers including an
application layer supporting
at least one supply chain application and at least one demand management
application, wherein
the microservice layers include a robotic process automation layer that uses
information collected
by a data collection layer and a set of outcomes and activities involving the
applications of the
application layer to automate a set of actions for at least a subset of the
applications.
VALUE CHAIN MANAGEMENT PLATFORM ¨ ROBOTIC PROCESS AUTOMATION FOR VALUE
CHAIN NETWORK PROCESSES
[0377] In embodiments, provided herein are methods, systems, components and
other elements
for an information technology system that may include a cloud-based management
VCNP 102
with a micro-services architecture, a set of interfaces 702, a set of network
connectivity facilities
642, adaptive intelligence facilities 614, data storage facilities 624, data
collection systems 640,
and monitoring facilities 614 that are coordinated for monitoring and
management of a set of
value chain network entities 652; a set of applications for enabling an
enterprise to manage a set
of value chain network entities from a point of origin to a point of customer
use; and a set of
robotic process automation systems 1442 for automating a set of processes in a
value chain
network, wherein the robotic process automation systems 1442 learn on a
training set of data
involving a set of user interactions with a set of interfaces 702 of a set of
software systems that
are used to monitor and manage the value chain network entities 652, as well
as from various
process and application outputs and outcomes 1040 that may occur with or
within the VCNP 102.
[0378] In embodiments, the value chain network entities 652 may include, for
example,
products, suppliers, producers, manufacturers, retailers, businesses, owners,
operators, operating
facilities, customers, consumers, workers, mobile devices, wearable devices,
distributors,
resellers, supply chain infrastructure facilities, supply chain processes,
logistics processes,
reverse logistics processes, demand prediction processes, demand management
processes,
demand aggregation processes, machines, ships, barges, warehouses, maritime
ports, airports,
airways, waterways, roadways, railways, bridges, tunnels, online retailers,
ecommerce sites,
demand factors, supply factors, delivery systems, floating assets, points of
origin, points of
destination, points of storage, points of use, networks, information
technology systems, software
platforms, distribution centers, fulfillment centers, containers, container
handling facilities,
customs, export control, border control, drones, robots, autonomous vehicles,
hauling facilities,
drones/robots/AVs, waterways, port infrastructure facilities, or many others.
[0379] In embodiments, the robotic process automation layer automates a
process that may
include, for example, without limitation, selection of a quantity of product
for an order, selection
of a carrier for a shipment, selection of a vendor for a component, selection
of a vendor for a
finished goods order, selection of a variation of a product for marketing,
selection of an
assortment of goods for a shelf, determination of a price for a finished good,
configuration of a
service offer related to a product, configuration of product bundle,
configuration of a product kit,
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configuration of a product package, configuration of a product display,
configuration of a product
image, configuration of a product description, configuration of a website
navigation path related
to a product, determination of an inventory level for a product, selection of
a logistics type,
configuration of a schedule for product delivery, configuration of a logistics
schedule,
.. configuration of a set of inputs for machine learning, preparation of
product documentation,
preparation of required disclosures about a product, configuration of a
product for a set of local
requirements, configuration of a set of products for compatibility,
configuration of a request for
proposals, ordering of equipment for a warehouse, ordering of equipment for a
fulfillment center,
classification of a product defect in an image, inspection of a product in an
image, inspection of
product quality data from a set of sensors, inspection of data from a set of
onboard diagnostics on
a product, inspection of diagnostic data from an Internet of Things system,
review of sensor data
from environmental sensors in a set of supply chain environments, selection of
inputs for a digital
twin, selection of outputs from a digital twin, selection of visual elements
for presentation in a
digital twin, diagnosis of sources of delay in a supply chain, diagnosis of
sources of scarcity in a
.. supply chain, diagnosis of sources of congestion in a supply chain,
diagnosis of sources of cost
overruns in a supply chain, diagnosis of sources of product defects in a
supply chain, prediction
of maintenance requirements in supply chain infrastructure, or others.
[0380] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
.. micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; and a set
of robotic process
automation systems for automating a set of processes in a value chain network,
wherein the
robotic process automation systems learn on a training set of data involving a
set of user
interactions with a set of interfaces of a set of software systems that are
used to monitor and
manage the value chain network entities.
[0381] In embodiments, one of the processes automated by robotic process
automation as
described in any of the embodiments disclosed herein may involve the
following. In
embodiments, RPA involves selection of a quantity of product for an order. In
embodiments, one
of the processes automated by robotic process automation involves selection of
a carrier for a
shipment. In embodiments, one of the processes automated by robotic process
automation
involves selection of a vendor for a component. In embodiments, one of the
processes automated
by robotic process automation involves selection of a vendor for a finished
goods order. In
embodiments, one of the processes automated by robotic process automation
involves selection
of a variation of a product for marketing. In embodiments, one of the
processes automated by
robotic process automation involves selection of an assortment of goods for a
shelf. In
embodiments, one of the processes automated by robotic process automation
involves
determination of a price for a finished good. In embodiments, one of the
processes automated by
robotic process automation involves configuration of a service offer related
to a product. In
embodiments, one of the processes automated by robotic process automation
involves
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configuration of product bundle. In embodiments, one of the processes
automated by robotic
process automation involves configuration of a product kit. In embodiments,
one of the processes
automated by robotic process automation involves configuration of a product
package. In
embodiments, one of the processes automated by robotic process automation
involves
configuration of a product display. In embodiments, one of the processes
automated by robotic
process automation involves configuration of a product image. In embodiments,
one of the
processes automated by robotic process automation involves configuration of a
product
description. In embodiments, one of the processes automated by robotic process
automation
involves configuration of a website navigation path related to a product. In
embodiments, one of
the processes automated by robotic process automation involves determination
of an inventory
level for a product. In embodiments, one of the processes automated by robotic
process
automation involves selection of a logistics type. In embodiments, one of the
processes
automated by robotic process automation involves configuration of a schedule
for product
delivery. In embodiments, one of the processes automated by robotic process
automation
involves configuration of a logistics schedule. In embodiments, one of the
processes automated
by robotic process automation involves configuration of a set of inputs for
machine learning. In
embodiments, one of the processes automated by robotic process automation
involves
preparation of product documentation. In embodiments, one of the processes
automated by
robotic process automation involves preparation of required disclosures about
a product. In
embodiments, one of the processes automated by robotic process automation
involves
configuration of a product for a set of local requirements. In embodiments,
one of the processes
automated by robotic process automation involves configuration of a set of
products for
compatibility. In embodiments, one of the processes automated by robotic
process automation
involves configuration of a request for proposals.
[0382] In embodiments, one of the processes automated by robotic process
automation involves
ordering of equipment for a warehouse. In embodiments, one of the processes
automated by
robotic process automation involves ordering of equipment for a fulfillment
center. In
embodiments, one of the processes automated by robotic process automation
involves
classification of a product defect in an image. In embodiments, one of the
processes automated
by robotic process automation involves inspection of a product in an image.
[0383] In embodiments, one of the processes automated by robotic process
automation involves
inspection of product quality data from a set of sensors. In embodiments, one
of the processes
automated by robotic process automation involves inspection of data from a set
of onboard
diagnostics on a product. In embodiments, one of the processes automated by
robotic process
automation involves inspection of diagnostic data from an Internet of Things
system. In
embodiments, one of the processes automated by robotic process automation
involves review of
sensor data from environmental sensors in a set of supply chain environments.
[0384] In embodiments, one of the processes automated by robotic process
automation involves
selection of inputs for a digital twin. In embodiments, one of the processes
automated by robotic
process automation involves selection of outputs from a digital twin. In
embodiments, one of the
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processes automated by robotic process automation involves selection of visual
elements for
presentation in a digital twin. In embodiments, one of the processes automated
by robotic process
automation involves diagnosis of sources of delay in a supply chain. In
embodiments, one of the
processes automated by robotic process automation involves diagnosis of
sources of scarcity in a
supply chain. In embodiments, one of the processes automated by robotic
process automation
involves diagnosis of sources of congestion in a supply chain.
[0385] In embodiments, one of the processes automated by robotic process
automation involves
diagnosis of sources of cost overruns in a supply chain. In embodiments, one
of the processes
automated by robotic process automation involves diagnosis of sources of
product defects in a
supply chain. In embodiments, one of the processes automated by robotic
process automation
involves prediction of maintenance requirements in supply chain
infrastructure.
[0386] In embodiments, the set of demand management applications, supply chain
applications,
intelligent product applications and enterprise resource management
applications may include,
for example, ones involving supply chain, asset management, risk management,
inventory
management, demand management, demand prediction, demand aggregation, pricing,
positioning, placement, promotion, blockchain, smart contract, infrastructure
management,
facility management, analytics, finance, trading, tax, regulatory, identity
management,
commerce, ecommerce, payments, security, safety, vendor management, process
management,
compatibility testing, compatibility management, infrastructure testing,
incident management,
predictive maintenance, logistics, monitoring, remote control, automation,
self-configuration,
self-healing, self-organization, logistics, reverse logistics, waste
reduction, augmented reality,
virtual reality, mixed reality, demand customer profiling, entity profiling,
enterprise profiling,
worker profiling, workforce profiling, component supply policy management,
product design,
product configuration, product updating, product maintenance, product support,
product testing,
warehousing, distribution, fulfillment, kit configuration, kit deployment, kit
support, kit updating,
kit maintenance, kit modification, kit management, shipping fleet management,
vehicle fleet
management, workforce management, maritime fleet management, navigation,
routing, shipping
management, opportunity matching, search, advertisement, entity discovery,
entity search,
distribution, delivery, enterprise resource planning, and many others.
[0387] Introduction of Opportunity Miners for Automated Improvement of
Adaptive
Intelligence
[0388] Referring to Fig. 26, a set of opportunity miners 1460 may be provided
as part of the
adaptive intelligence layer 614, which may be configured to seek and recommend
opportunities
to improve one or more of the elements of the platform 604, such as via
addition of artificial
intelligence 1160, automation (including robotic process automation 1442), or
the like to one or
more of the systems, sub-systems, components, applications or the like of the
VCNP 102 or with
which the VCNP 102 interacts. In embodiments, the opportunity miners 1460 may
be configured
or used by developers of Al or RPA solutions to find opportunities for better
solutions and to
optimize existing solutions in a value chain network 668. In embodiments, the
opportunity
miners 1460 may include a set of systems that collect information within the
VCNP 102 and
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collect information within, about and for a set of value chain network
entities 652 and
environments, where the collected information has the potential to help
identify and prioritize
opportunities for increased automation and/or intelligence about the value
chain network 668,
about applications 630, about value chain network entities 652, or about the
VCNP 102 itself. For
example, the opportunity miners 1460 may include systems that observe clusters
of value chain
network workers by time, by type, and by location, such as using cameras,
wearables, or other
sensors, such as to identify labor-intensive areas and processes in a set of
value chain network
668 environments. These may be presented, such as in a ranked or prioritized
list, or in a
visualization (such as a heat map showing dwell times of customers, workers or
other individuals
on a map of an environment or a heat map showing routes traveled by customers
or workers
within an environment) to show places with high labor activity. In
embodiments, analytics 838
may be used to identify which environments or activities would most benefit
from automation for
purposes of improved delivery times, mitigation of congestion, and other
performance
improvements.
[0389] In embodiments, opportunity mining may include facilities for
solicitation of
appropriate training data sets that may be used to facilitate process
automation. For example,
certain kinds of inputs, if available, would provide very high value for
automation, such as video
data sets that capture very experienced and/or highly expert workers
performing complex tasks.
Opportunity miners 1460 may search for such video data sets as described
herein; however, in
the absence of success (or to supplement available data), the platform may
include systems by
which a user, such as a developer, may specify a desired type of data, such as
software
interaction data (such as of an expert working with a program to perform a
particular task), video
data (such as video showing a set of experts performing a certain kind of
delivery process,
packing process, picking process, a container movement process, or the like),
and/or physical
process observation data (such as video, sensor data, or the like). The
resulting library of
interactions captured in response to specification may be captured as a data
set in the data storage
layer 624, such as for consumption by various applications 630, adaptive
intelligence systems
614, and other processes and systems. In embodiments, the library may include
videos that are
specifically developed as instructional videos, such as to facilitate
developing an automation map
that can follow instructions in the video, such as providing a sequence of
steps according to a
procedure or protocol, breaking down the procedure or protocol into sub-steps
that are candidates
for automation, and the like. In embodiments, such videos may be processed by
natural language
processing, such as to automatically develop a sequence of labeled
instructions that can be used
by a developer to facilitate a map, a graph, or other models of a process that
assists with
development of automation for the process. In embodiments, a specified set of
training data sets
may be configured to operate as inputs to learning. In such cases the training
data may be time-
synchronized with other data within the platform 604, such as outputs and
outcomes from
applications 630, outputs and outcomes of value chain entities 652, or the
like, so that a given
video of a process can be associated with those outputs and outcomes, thereby
enabling feedback
on learning that is sensitive to the outcomes that occurred when a given
process that was captured
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(such as on video, or through observation of software interactions or physical
process
interactions). For example, this may relate to an instruction video such as a
video of a person
who may be building or rebuilding (e.g., rebuilding a bearing set). This
instruction video may
include individual steps for rebuild that may allow a staging of the training
to provide
instructions such as parsing the video into stages that mimic the experts
staging in the video. For
example, this may include tagging of the video to include references to each
stage and status
(e.g., stage one complete, stage two, etc.) This type of example may utilize
artificial intelligence
that may understand that there may be a series of sub-functions that add up to
a final function.
[0390] In embodiments, opportunity miners 1460 may include methods, systems,
processes,
components, services and other elements for mining for opportunities for smart
contract
definition, formation, configuration and execution. Data collected within the
platform 604, such
as any data handled by the data handling layers 624, stored by the data
storage layer 624,
collected by the monitoring layer 614 and collection systems 640, collected
about or from entities
652 or obtained from external sources may be used to recognize beneficial
opportunities for
application or configuration of smart contracts. For example, pricing
information about an entity
652, handled by a pricing application 842, or otherwise collected, may be used
to recognize
situations in which the same item or items is disparately priced (in a spot
market, futures market,
or the like), and the opportunity miner 1460 may provide an alert indicating
an opportunity for
smart contract formation, such as a contract to buy in one environment at a
price below a given
threshold and sell in another environment at a price above a given threshold,
or vice versa.
[0391] In some examples, as shown in Fig. 26, the adaptive intelligent systems
614 may
include value translators 1470. The value translators 1470 may relate to
demand side of
transactions. Specifically, for example, the value translators 1470 may
understand negative
currencies of two marketplaces and may be able to translate value currencies
into other
currencies (e.g., not only fiat currencies that already have clear translation
functions). In some
examples, value translators 1470 may be associated with points of a point-
based system (e.g., in a
cost-based routing system). In an example embodiment, value translators 1470
may be loyalty
points offered that may be convertible into airline seats and/or may translate
to refund policies
for staying in a hotel room. In some examples, different types of entities may
be connected as
having native pricing or cost functions that do not always use the same
currency or any currency.
In another example, value translators 1470 may be used with network
prioritization or cost-based
routing that happens in networks off of priorities where the point system in
these cost-based
routing systems is not monetary-based.
BROAD MANAGEMENT PLATFORM
[0392] Referring to Fig. 28, additional details of an embodiment of the
platform 604 are
provided, in particular relating to an overall architecture for the platform
604. These may include,
for the cloud-based management platform 604, employing a micro-services
architecture, a set of
network connectivity facilities 642 (which may include or connect to a set of
interfaces 702 of
various layers of the platform 604), a set of adaptive intelligence facilities
or adaptive intelligent
systems 1160, a set of data storage facilities or systems 624, and a set of
monitoring facilities or
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systems 614. The platform 604 may support a set of applications 630 (including
processes,
workflows, activities, events, use cases and applications) for enabling an
enterprise to manage a
set of value chain network entities 652, such as from a point of origin to a
point of customer use
of a product 650, which may be an intelligent product.
[0393] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture; a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities;
and a set of applications
for enabling an enterprise to manage a set of value chain network entities
from a point of origin
to a point of customer use.
[0394] Also provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, the platform having: a set of interfaces for
accessing and configuring
features of the platform; a set of network connectivity facilities for
enabling a set of value chain
network entities to connect to the platform; a set of adaptive intelligence
facilities for automating
a set of capabilities of the platform; a set of data storage facilities for
storing data collected and
handled by the platform; and a set of monitoring facilities for monitoring the
value chain network
entities; wherein the platform hosts a set of applications for enabling an
enterprise to manage a
set of value chain network entities from a point of origin of a product of the
enterprise to a point
of customer use.
BROAD MANAGEMENT PLATFORM - DETAILS
[0395] Referring to Fig. 29, additional details of an embodiment of the
platform 604 are
provided, in particular relating to an overall architecture for the platform
604. These may include,
for the cloud-based management platform 604, employing a micro-services
architecture, a set of
network connectivity facilities 642 (which may include or connect to a set of
interfaces 702 of
various layers of the platform 604), a set of adaptive intelligence facilities
or adaptive intelligent
systems 1160, a set of data storage facilities or systems 624, and a set of
monitoring facilities or
systems 614. The platform 604 may support a set of applications 630 (including
processes,
workflows, activities, events, use cases and applications) for enabling an
enterprise to manage a
set of value chain network entities 652, such as from a point of origin to a
point of customer use
of a product 650, which may be an intelligent product.
[0396] In embodiments, the set of interfaces 702 may include a demand
management interface
MPVC104 and a supply chain management interface MPVC108.
[0397] In embodiments, the set of network connectivity facilities 642 for
enabling a set of value
chain network entities 652 to connect to the platform 604 may include a 5G
network system
MPVC110, such as one that is deployed in a supply chain infrastructure
facility operated by the
enterprise.
[0398] In embodiments, the set of network connectivity facilities 642 for
enabling a set of value
chain network entities 652 to connect to the platform 604 may include an
Internet of Things
system 1172, such as one that is deployed in a supply chain infrastructure
facility operated by the
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enterprise, in, on or near a value chain network entity 652, in a network
system, and/or in a cloud
computing environment (such as where data collection systems 640 are
configured to collect and
organize IoT data).
[0399] In embodiments, the set of network connectivity facilities 642 for
enabling a set of value
chain network entities 652 to connect to the VCNP 102 may include a cognitive
networking
system MPVC114 deployed in a supply chain infrastructure facility operated by
the enterprise.
[0400] In embodiments, the set of network connectivity facilities 642 for
enabling a set of value
chain network entities 652 to connect to the VCNP 102 may include a peer-to-
peer network
system MPVC118, such as one that is deployed in a supply chain infrastructure
facility operated
by the enterprise.
[0401] In embodiments, the set of adaptive intelligence facilities or adaptive
intelligent systems
614 for automating a set of capabilities of the platform 604 may include an
edge intelligence
system 1420, such as one that is deployed in a supply chain infrastructure
facility operated by the
enterprise.
[0402] In embodiments, the set of adaptive intelligence facilities or adaptive
intelligent systems
614 for automating a set of capabilities of the platform 604 may include a
robotic process
automation system 1442.
[0403] In embodiments, the set of adaptive intelligence facilities or adaptive
intelligent systems
614 for automating a set of capabilities of the platform 604 may include or
may integrate with a
self-configuring data collection system 1440, such as one that deployed in a
supply chain
infrastructure facility operated by the enterprise, one that is deployed in a
network, and/or one
that is deployed in a cloud computing environment. This may include elements
of the data
collection systems 640 of the data handling layers 624 that interact with or
integrate with
elements of the adaptive intelligent systems 614.
[0404] In embodiments, the set of adaptive intelligence facilities or adaptive
intelligent systems
614 for automating a set of capabilities of the platform 604 may include a
digital twin system
1700, such as one representing attributes of a set of value chain network
entities, such as the ones
controlled by an enterprise.
[0405] In embodiments, the set of adaptive intelligence facilities or adaptive
intelligent systems
614 for automating a set of capabilities of the platform 604 may include a
smart contract system
848, such as one for automating a set of interactions or transactions among a
set of value chain
network entities 652 based on status data, event data, or other data handled
by the data handling
layers 624.
[0406] In embodiments, the set of data storage facilities or data storage
systems 624 for storing
data collected and handled by the platform 604 uses a distributed data
architecture 1122.
[0407] In embodiments, the set of data storage facilities for storing data
collected and handled
by the platform uses a blockchain 844.
[0408] In embodiments, the set of data storage facilities for storing data
collected and handled
by the platform uses a distributed ledger 1452.
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[0409] In embodiments, the set of data storage facilities for storing data
collected and handled
by the platform uses graph database 1124 representing a set of hierarchical
relationships of value
chain network entities.
[0410] In embodiments, the set of monitoring facilities 614 for monitoring the
value chain
network entities 652 includes an Internet of Things monitoring system 1172,
such as for
collecting data from IoT systems and devices deployed throughout a value chain
network.
[0411] In embodiments, the set of monitoring facilities 614 for monitoring the
value chain
network entities 652 includes a set of sensor systems 1462, such as ones
deployed in a value
chain environment or in, one or near a value chain network entity 652, such as
in or on a product
650.
[0412] In embodiments, the set of applications 630 includes a set of
applications, which may
include a variety of types from among, for example, a set of supply chain
management
applications 1500, demand management applications 1502, intelligent product
applications 1510
and enterprise resource management applications 1520.
[0413] In embodiments, the set of applications includes an asset management
application 1530.
[0414] In embodiments, the value chain network entities 652 as mentioned
throughout this
disclosure may include, for example, without limitation, products, suppliers,
producers,
manufacturers, retailers, businesses, owners, operators, operating facilities,
customers,
consumers, workers, mobile devices, wearable devices, distributors, resellers,
supply chain
infrastructure facilities, supply chain processes, logistics processes,
reverse logistics processes,
demand prediction processes, demand management processes, demand aggregation
processes,
machines, ships, barges, warehouses, maritime ports, airports, airways,
waterways, roadways,
railways, bridges, tunnels, online retailers, ecommerce sites, demand factors,
supply factors,
delivery systems, floating assets, points of origin, points of destination,
points of storage, points
of use, networks, information technology systems, software platforms,
distribution centers,
fulfillment centers, containers, container handling facilities, customs,
export control, border
control, drones, robots, autonomous vehicles, hauling facilities,
drones/robots/AVs, waterways,
port infrastructure facilities, or others.
[0415] In embodiments, the platform 604 manages a set of demand factors 1540,
a set of supply
factors 1550 and a set of value chain infrastructure facilities 1560.
[0416] In embodiments, the supply factors 1550 as mentioned throughout this
disclosure may
include, for example and without limitation, ones involving component
availability, material
availability, component location, material location, component pricing,
material pricing, taxation,
tariff, impost, duty, import regulation, export regulation, border control,
trade regulation,
customs, navigation, traffic, congestion, vehicle capacity, ship capacity,
container capacity,
package capacity, vehicle availability, ship availability, container
availability, package
availability, vehicle location, ship location, container location, port
location, port availability,
port capacity, storage availability, storage capacity, warehouse availability,
warehouse capacity,
fulfillment center location, fulfillment center availability, fulfillment
center capacity, asset owner
identity, system compatibility, worker availability, worker competency, worker
location, goods
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pricing, fuel pricing, energy pricing, route availability, route distance,
route cost, route safety,
and many others.
[0417] In embodiments, the demand factors 1540 as mentioned throughout this
disclosure may
include, for example and without limitation, ones involving product
availability, product pricing,
delivery timing, need for refill, need for replacement, manufacturer recall,
need for upgrade, need
for maintenance, need for update, need for repair, need for consumable, taste,
preference,
inferred need, inferred want, group demand, individual demand, family demand,
business
demand, need for workflow, need for process, need for procedure, need for
treatment, need for
improvement, need for diagnosis, compatibility to system, compatibility to
product, compatibility
to style, compatibility to brand, demographic, psychographic, geolocation,
indoor location,
destination, route, home location, visit location, workplace location,
business location,
personality, mood, emotion, customer behavior, business type, business
activity, personal
activity, wealth, income, purchasing history, shopping history, search
history, engagement
history, clickstream history, website history, online navigation history,
group behavior, family
behavior, family membership, customer identity, group identity, business
identity, customer
profile, business profile, group profile, family profile, declared interest,
inferred interest, and
many others.
[0418] In embodiments, the supply chain infrastructure facilities 1560 as
mentioned throughout
this disclosure may include, for example and without limitation, ship,
container ship, boat, barge,
maritime port, crane, container, container handling, shipyard, maritime dock,
warehouse,
distribution, fulfillment, fueling, refueling, nuclear refueling, waste
removal, food supply,
beverage supply, drone, robot, autonomous vehicle, aircraft, automotive,
truck, train, lift, forklift,
hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport,
depot, vehicle
station, train station, weigh station, inspection, roadway, railway, highway,
customs house,
border control, and other facilities.
[0419] In embodiments, the set of applications 630 as mentioned throughout
this disclosure
may include, for example and without limitation, supply chain, asset
management, risk
management, inventory management, demand management, demand prediction, demand
aggregation, pricing, positioning, placement, promotion, blockchain, smart
contract,
infrastructure management, facility management, analytics, finance, trading,
tax, regulatory,
identity management, commerce, ecommerce, payments, security, safety, vendor
management,
process management, compatibility testing, compatibility management,
infrastructure testing,
incident management, predictive maintenance, logistics, monitoring, remote
control, automation,
self-configuration, self-healing, self-organization, logistics, reverse
logistics, waste reduction,
augmented reality, virtual reality, mixed reality, demand customer profiling,
entity profiling,
enterprise profiling, worker profiling, workforce profiling, component supply
policy
management, product design, product configuration, product updating, product
maintenance,
product support, product testing, warehousing, distribution, fulfillment, kit
configuration, kit
deployment, kit support, kit updating, kit maintenance, kit modification, kit
management,
shipping fleet management, vehicle fleet management, workforce management,
maritime fleet
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management, navigation, routing, shipping management, opportunity matching,
search,
advertisement, entity discovery, entity search, distribution, delivery,
enterprise resource planning
and other applications.
CONTROL TOWER
[0420] Referring to Fig. 30, an embodiment of the platform 604 is provided.
The platform 604
may employ a micro-services architecture with the various data handling layers
614, a set of
network connectivity facilities 642 (which may include or connect to a set of
interfaces 702 of
various layers of the platform 604), a set of adaptive intelligence facilities
or adaptive intelligent
systems 1160, a set of data storage facilities or systems 624, and a set of
monitoring facilities or
systems 614. The platform 604 may support a set of applications 630 (including
processes,
workflows, activities, events, use cases and applications) for enabling an
enterprise to manage a
set of value chain network entities 652, such as from a point of origin to a
point of customer use
of a product 650, which may be an intelligent product.
[0421] In embodiments, the platform 604 may include a user interface 1570 that
provides a set
of unified views for a set of demand management information and supply chain
information for a
category of goods, such as one that displays status information, event
information, activity
information, analytics, reporting, or other elements of, relating to, or
produced by a set of supply
chain management applications 1500, demand management applications 1502,
intelligent product
applications 1510 and enterprise resource management applications 1520 that
monitor and/or
manage a value chain network and a set of value chain network entities 652.
The unified view
interface 1570 may thus provide, in embodiments, a control tower for an
enterprise over a range
of assets, such as supply chain infrastructure facilities 1560 and other value
chain network
entities 652 that are involved as a product 650 travels from a point of origin
through distribution
and retail channels to an environment where it is used by a customer. These
may include views of
demand factors 1540 and supply factors 1550, so that a user may develop
insights about
connections among the factors and control one or both of them with coordinated
intelligence.
Population of a set of unified views may be adapted over time, such as by
learning on outcomes
1040 or other operations of the adaptive intelligent systems 614, such as to
determine which
views of the interface 1570 provide the most impactful insights, control
features, or the like.
[0422] In embodiments, the user interface includes a voice operated assistant
1580.
[0423] In embodiments, the user interface includes a set of digital twins 1700
for presenting a
visual representation of a set of attributes of a set of value chain network
entities 652.
[0424] In embodiments, the user interface 1570 may include capabilities for
configuring the
adaptive intelligent systems 614 or adaptive intelligence facilities, such as
to allow user selection
of attributes, parameters, data sources, inputs to learning, feedback to
learning, views, formats,
arrangements, or other elements.
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VALUE CHAIN MANAGEMENT PLATFORM ¨ CONTROL TOWER UI FOR DEMAND MANAGEMENT
AND SUPPLY CHAIN
[0425] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a user interface that provides a set of unified
views for a set of
demand management information and supply chain information for a category of
goods.
UNIFIED DATABASE
[0426] Referring to Fig. 31, an embodiment of the platform 604 is provided. As
with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 614, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 1160, a set of data storage
facilities or systems 624, and
a set of monitoring facilities or systems 614. The platform 604 may support a
set of applications
630 (including processes, workflows, activities, events, use cases and
applications) for enabling
an enterprise to manage a set of value chain network entities 652, such as
from a point of origin
to a point of customer use of a product 650, which may be an intelligent
product.
[0427] In embodiments, the platform 604 may include a unified database 1590
that supports a
set of applications of multiple types, such as ones among a set of supply
chain management
applications 1500, demand management applications 1502, intelligent product
applications 1510
and enterprise resource management applications 1520 that monitor and/or
manage a value chain
network and a set of value chain network entities 652. The unified database
1590 may thus
provide, in embodiments, unification of data storage, access and handling for
an enterprise over a
range of assets, such as supply chain infrastructure facilities 1560 and other
value chain network
entities 652 that are involved as a product 650 travels from a point of origin
through distribution
and retail channels to an environment where it is used by a customer. This
unification may
provide a number of advantages, including reduced need for data entry,
consistency across
applications 630, reduced latency (and better real-time reporting), reduced
need for data
transformation and integration, and others. These may include data relating to
demand factors
1540 and supply factors 1550, so that an application 630 may benefit from
information collected
by, processed, or produced by other applications 630 of the platform 604 and a
user can develop
insights about connections among the factors and control one or both of them
with coordinated
intelligence. Population of the unified database 1590 may be adapted over
time, such as by
learning on outcomes 1040 or other operations of the adaptive intelligent
systems 614, such as to
determine which elements of the database 1590 should be made available to
which applications,
what data structures provide the most benefit, what data should be stored or
cached for
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immediate retrieval, what data can be discarded versus saved, what data is
most beneficial to
support adaptive intelligent systems 614, and for other uses.
[0428] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a unified database that supports a set of
applications of at least two
types from among a set of demand management applications, a set of supply
chain applications, a
set of intelligent product applications and a set of enterprise resource
management applications
for a category of goods.
[0429] In embodiments, the unified database that supports a set of demand
management
applications, a set of supply chain applications, a set of intelligent product
applications and a set
of enterprise resource management applications for a category of goods is a
distributed database.
[0430] In embodiments, the unified database that supports a set of demand
management
applications, a set of supply chain applications, a set of intelligent product
applications and a set
of enterprise resource management applications for a category of goods uses a
graph database
architecture. In embodiments, the set of demand management applications
includes a demand
prediction application. In embodiments, the set of demand management
applications includes a
demand aggregation application. In embodiments, the set of demand management
applications
includes a demand activation application.
[0431] In embodiments, the set of supply chain management applications
includes a vendor
search application. In embodiments, the set of supply chain management
applications includes a
route configuration application. In embodiments, the set of supply chain
management
applications includes a logistics scheduling application.
UNIFIED DATA COLLECTION SYSTEMS
[0432] Referring to Fig. 32, an embodiment of the platform 604 is provided. As
with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 614, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 1160, a set of data storage
facilities or systems 624, and
a set of monitoring facilities or systems 614. The platform 604 may support a
set of applications
630 (including processes, workflows, activities, events, use cases and
applications) for enabling
an enterprise to manage a set of value chain network entities 652, such as
from a point of origin
to a point of customer use of a product 650, which may be an intelligent
product.
[0433] In embodiments, the platform 604 may include a set of unified set of
data collection and
management systems 640 of the set of monitoring facilities or systems 614 that
support a set of
applications 630 of various types, including a set of supply chain management
applications 1500,
demand management applications 1502, intelligent product applications 1510 and
enterprise
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resource management applications 1520 that monitor and/or manage a value chain
network and a
set of value chain network entities 652. The unified data collection and
management systems 640
may thus provide, in embodiments, unification of data monitoring, search,
discovery, collection,
access and handling for an enterprise or other user over a range of assets,
such as supply chain
infrastructure facilities 1560 and other value chain network entities 652 that
are involved as a
product 650 travels from a point of origin through distribution and retail
channels to an
environment where it is used by a customer. This unification may provide a
number of
advantages, including reduced need for data entry, consistency across
applications 630, reduced
latency (and better real-time reporting), reduced need for data transformation
and integration, and
others. These may include collection of data relating to demand factors 1540
and supply factors
1550, so that an application 630 may benefit from information collected by,
processed, or
produced by other applications 630 of the platform 604 and a user can develop
insights about
connections among the factors and control one or both of them with coordinated
intelligence. The
unified data collection and management systems 640 may be adapted over time,
such as by
learning on outcomes 1040 or other operations of the adaptive intelligent
systems 614, such as to
determine which elements of the data collection and management systems 640
should be made
available to which applications 630, what data types or sources provide the
most benefit, what
data should be stored or cached for immediate retrieval, what data can be
discarded versus saved,
what data is most beneficial to support adaptive intelligent systems 614, and
for other uses. In
example embodiments, the unified data collection and management systems 640
may use a
unified data schema which relates data collection and management for various
applications. This
may be a single point of truth database at the most tightly bound or a set of
distributed data
systems that may follow a schema that may be sufficiently common enough that a
wide variety of
applications may consume the same data as received. For example, sensor data
may be pulled
from a smart product that may be consumed by a logistics application, a
financial application, a
demand prediction application, or a genetic programming artificial
intelligence (Al) application
to change the product, and the like. All of these applications may consume
data from a data
framework. In an example, this may occur from blockchains that may contain a
distributed ledger
or transactional data for purchase and sales or blockchains where there may be
an indication of
whether or not events had occurred. In some example embodiments, as data moves
through a
supply chain, this data flow may occur through distributed databases,
relational databases, graph
databases of all types, and the like that may be part of the unified data
collection and
management systems 640. In other examples, the unified data collection and
management
systems 640 may utilize memory that may be dedicated memory on an asset, in a
tag or part of a
memory structure of the device itself that may come from a robust pipeline
tied to the value chain
network entities. In other examples, the unified data collection and
management systems 640
may use classic data integration capabilities that may include adapting
protocols such that they
can ultimately get to the unified system or schema.
[0434] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
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micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a unified set of data collection systems that
support a set of
applications of at least two types from among a set of demand management
applications, a set of
supply chain applications, a set of intelligent product applications and a set
of enterprise resource
management applications for a category of goods.
[0435] In embodiments, the unified set of data collection systems includes a
set of
crowdsourcing data collection systems. In embodiments, the unified set of data
collection
systems includes a set of Internet of Things data collection systems. In
embodiments, the unified
set of data collection systems includes a set of self-configuring sensor
systems. In embodiments,
the unified set of data collection systems includes a set of data collection
systems that interact
with a network-connected product.
[0436] In embodiments, the unified set of data collection systems includes a
set of mobile data
collectors deployed in a set of value chain network environments operated by
an enterprise. In
embodiments, the unified set of data collection systems includes a set of edge
intelligence
systems deployed in set of value chain network environments operated by an
enterprise. In
embodiments, the unified set of data collection systems includes a set of
crowdsourcing data
collection systems. In embodiments, the unified set of data collection systems
includes a set of
Internet of Things data collection systems. In embodiments, the unified set of
data collection
systems includes a set of self-configuring sensor systems. In embodiments, the
unified set of data
collection systems includes a set of data collection systems that interact
with a network-
connected product. In embodiments, the unified set of data collection systems
includes a set of
mobile data collectors deployed in a set of value chain network environments
operated by an
enterprise. In embodiments, the unified set of data collection systems
includes a set of edge
intelligence systems deployed in a set of value chain network environments
operated by an
enterprise.
[0437] Unified IoT Monitoring Systems
[0438] Referring to Fig. 33, an embodiment of the platform 604 is provided. As
with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 614, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 1160, a set of data storage
facilities or systems 624, and
a set of monitoring facilities or systems 614. The platform 604 may support a
set of applications
630 (including processes, workflows, activities, events, use cases and
applications) for enabling
an enterprise to manage a set of value chain network entities 652, such as
from a point of origin
to a point of customer use of a product 650, which may be an intelligent
product.
[0439] In embodiments, the platform 604 may include a unified set of Internet
of Things
systems 1172 that provide coordinated monitoring of various value chain
entities 652 in service
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of a set of multiple applications 630 of various types, such as a set of
supply chain management
applications 1500, demand management applications 1502, intelligent product
applications 1510
and enterprise resource management applications 1520 that monitor and/or
manage a value chain
network and a set of value chain network entities 652.
[0440] The unified set of Internet of Things systems 1172 may thus provide, in
embodiments,
unification of monitoring of, and communication with, a wide range of
facilities, devices,
systems, environments, and assets, such as supply chain infrastructure
facilities 1560 and other
value chain network entities 652 that are involved as a product 650 travels
from a point of origin
through distribution and retail channels to an environment where it is used by
a customer. This
unification may provide a number of advantages, including reduced need for
data entry,
consistency across applications 630, reduced latency, real-time reporting and
awareness, reduced
need for data transformation and integration, and others. These may include
Internet of Things
systems 1172 that are used in connection with demand factors 1540 and supply
factors 1550, so
that an application 630 may benefit from information collected by, processed,
or produced by the
unified set of Internet of Things systems 1172 for other applications 630 of
the platform 604, and
a user can develop insights about connections among the factors and control
one or both of them
with coordinated intelligence. The unified set of Internet of Things systems
1172 may be adapted
over time, such as by learning on outcomes 1040 or other operations of the
adaptive intelligent
systems 614, such as to determine which elements of the unified set of
Internet of Things systems
1172 should be made available to which applications 630, what IoT systems 1172
provide the
most benefit, what data should be stored or cached for immediate retrieval,
what data can be
discarded versus saved, what data is most beneficial to support adaptive
intelligent systems 614,
and for other uses. In some examples, the unified set of Internet of Things
(IoT) systems 1172
may be IoT devices that may be installed in various environments. One goal of
the unified set of
Internet of Things systems 1172 may be coordination across a city or town
involving citywide
deployments where collectively a set of IOT devices may be connected by wide
area network
protocols (e.g., longer range protocols). In another example, the unified set
of Internet of Things
systems 1172 may involve connecting a mesh of devices across several different
distribution
facilities. The IoT devices may identify collection for each warehouse and the
warehouses may
use the IoT devices to communicate with each other. The IoT devices may be
configured to
process data without using the cloud.
[0441] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications
integrated with the platform for enabling an enterprise user of the platform
to manage a set of
value chain network entities from a point of origin to a point of customer
use; and a unified set of
Internet of Things systems that provide coordinated monitoring of a set of
applications of at least
two types from among a set of demand management applications, a set of supply
chain
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applications, a set of intelligent product applications and a set of
enterprise resource management
applications for a category of goods.
[0442] In embodiments, the unified set of Internet of Things systems includes
a set of smart
home Internet of Things devices to enable monitoring of a set of demand
factors and a set of
Internet of Things devices deployed in proximity to a set of supply chain
infrastructure facilities
to enable monitoring of a set of supply factors.
[0443] In embodiments, the unified set of Internet of Things systems includes
a set of
workplace Internet of Things devices to enable monitoring of a set of demand
factors for a set of
business customers and a set of Internet of Things devices deployed in
proximity to a set of
.. supply chain infrastructure facilities to enable monitoring of a set of
supply factors.
[0444] In embodiments, the unified set of Internet of Things systems includes
a set of Internet
of Things devices to monitor a set of consumer goods stores to enable
monitoring of a set of
demand factors for a set of consumers and a set of Internet of Things devices
deployed in
proximity to a set of supply chain infrastructure facilities to enable
monitoring of a set of supply
factors.
[0445] In embodiments, the Internet of Things systems as mentioned throughout
this disclosure
may include, for example and without limitations, camera systems, lighting
systems, motion
sensing systems, weighing systems, inspection systems, machine vision systems,
environmental
sensor systems, onboard sensor systems, onboard diagnostic systems,
environmental control
systems, sensor-enabled network switching and routing systems, RF sensing
systems, magnetic
sensing systems, pressure monitoring systems, vibration monitoring systems,
temperature
monitoring systems, heat flow monitoring systems, biological measurement
systems, chemical
measurement systems, ultrasonic monitoring systems, radiography systems, LIDAR-
based
monitoring systems, access control systems, penetrating wave sensing systems,
SONAR-based
monitoring systems, radar-based monitoring systems, computed tomography
systems, magnetic
resonance imaging systems, network monitoring systems, and many others.
[0446] Machine Vision Feeding Digital Twin
[0447] Referring to Fig. 34, an embodiment of the platform 604 is provided. As
with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 614, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 1160, a set of data storage
facilities or systems 624, and
a set of monitoring facilities or systems 614. The platform 604 may support a
set of applications
630 (including processes, workflows, activities, events, use cases and
applications) for enabling
an enterprise to manage a set of value chain network entities 652, such as
from a point of origin
to a point of customer use of a product 650, which may be an intelligent
product.
[0448] In embodiments, the platform 604 may include a machine vision system
1600 and a
digital twin system 1700, wherein the machine vision system 1600 feeds data to
the digital twin
system 1700 (which may be enabled by a set of adaptive intelligent systems
614, including
.. artificial intelligence 1160, and may be used as interfaces or components
of interfaces 702, such
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as ones by which an operator may monitor twins 1700 of various value chain
network entities
652). The machine vision system 1600 and digital twin system 1700 may operate
in coordination
for a set of multiple applications 630 of various types, such as a set of
supply chain management
applications 1500, demand management applications 1502, intelligent product
applications 1510
and enterprise resource management applications 1520 that monitor and/or
manage a value chain
network and a set of value chain network entities 652.
[0449] The machine vision system 1600 and digital twin system 1700 may thus
provide, in
embodiments, image-based monitoring (with automated processing of image data)
a wide range
of facilities, devices, systems, environments, and assets, such as supply
chain infrastructure
facilities 1560 and other value chain network entities 652 that are involved
as a product 650
travels from a point of origin through distribution and retail channels to an
environment where it
is used by a customer, as well as representation of images, as well as
extracted data from images,
in a digital twin 1700. This unification may provide a number of advantages,
including improved
monitoring, improved visualization and insight, improved visibility, and
others. These may
include machine vision systems 1600 and digital twin systems 1700 that are
used in connection
with demand factors 1540 and supply factors 1550, so that an application 630
may benefit from
information collected by, processed, or produced by the machine vision system
1600 and digital
twin system 1700 for other applications 630 of the platform 604, and a user
can develop insights
about connections among the factors and control one or both of them with
coordinated
intelligence. The machine vision system 1600 and/or digital twin system 1700
may be adapted
over time, such as by learning on outcomes 1040 or other operations of the
adaptive intelligent
systems 614, such as to determine which elements collected and/or processed by
the machine
vision system 1600 and/or digital twin system 1700 should be made available to
which
applications 630, what elements and/or content provide the most benefit, what
data should be
stored or cached for immediate retrieval, what data can be discarded versus
saved, what data is
most beneficial to support adaptive intelligent systems 614, and for other
uses.
[0450] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and for a set of applications of at least two types
from among a set of
supply chain applications, a set of demand management applications, a set of
intelligent product
applications and a set of enterprise resource management applications and
having a machine
vision system and a digital twin system, wherein the machine vision system
feeds data to the
digital twin system.
[0451] In embodiments, the set of supply chain applications and demand
management
applications is among any described throughout this disclosure or in the
documents incorporated
by reference herein.
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[0452] In embodiments, the set of supply chain applications and demand
management
applications includes, for example and without limitation one or more
involving inventory
management, demand prediction, demand aggregation, pricing, blockchain, smart
contract,
positioning, placement, promotion, analytics, finance, trading, arbitrage,
customer identity
management, store planning, shelf-planning, customer route planning, customer
route analytics,
commerce, ecommerce, payments, customer relationship management, sales,
marketing,
advertising, bidding, customer monitoring, customer process monitoring,
customer relationship
monitoring, collaborative filtering, customer profiling, customer feedback,
similarity analytics,
customer clustering, product clustering, seasonality factor analytics,
customer behavior tracking,
customer behavior analytics, product design, product configuration, A/B
testing, product
variation analytics, augmented reality, virtual reality, mixed reality,
customer demand profiling,
customer mood, emotion or affect detection, customer mood, emotion of affect
analytics,
business entity profiling, customer enterprise profiling, demand matching,
location-based
targeting, location-based offering, point of sale interface, point of use
interface, search,
advertisement, entity discovery, entity search, enterprise resource planning,
workforce
management, customer digital twin, product pricing, product bundling, product
and service
bundling, product assortment, upsell offer configuration, customer feedback
engagement,
customer survey, or others.
[0453] In embodiments, the set of supply chain applications and demand
management
applications may include, without limitation, one or more of supply chain,
asset management,
risk management, inventory management, blockchain, smart contract,
infrastructure
management, facility management, analytics, finance, trading, tax, regulatory,
identity
management, commerce, ecommerce, payments, security, safety, vendor
management, process
management, compatibility testing, compatibility management, infrastructure
testing, incident
management, predictive maintenance, logistics, monitoring, remote control,
automation, self-
configuration, self-healing, self-organization, logistics, reverse logistics,
waste reduction,
augmented reality, virtual reality, mixed reality, supply chain digital twin,
vendor profiling,
supplier profiling, manufacturer profiling, logistics entity profiling,
enterprise profiling, worker
profiling, workforce profiling, component supply policy management,
warehousing, distribution,
fulfillment, shipping fleet management, vehicle fleet management, workforce
management,
maritime fleet management, navigation, routing, shipping management,
opportunity matching,
search, entity discovery, entity search, distribution, delivery, enterprise
resource planning or
other applications.
[0454] In embodiments, the set of supply chain applications and demand
management
applications may include, without limitation, one or more of asset management,
risk
management, inventory management, blockchain, smart contract, analytics,
finance, trading, tax,
regulatory, identity management, commerce, ecommerce, payments, security,
safety,
compatibility testing, compatibility management, incident management,
predictive maintenance,
monitoring, remote control, automation, self-configuration, self-healing, self-
organization, waste
reduction, augmented reality, virtual reality, mixed reality, product design,
product configuration,
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product updating, product maintenance, product support, product testing, kit
configuration, kit
deployment, kit support, kit updating, kit maintenance, kit modification, kit
management, product
digital twin, opportunity matching, search, advertisement, entity discovery,
entity search,
variation, simulation, user interface, application programming interface,
connectivity
management, natural language interface, voice/speech interface, robotic
interface, touch
interface, haptic interface, vision system interface, enterprise resource
planning, or other
applications.
[0455] In embodiments, the set of supply chain applications and demand
management
applications may include, without limitation, one or more of operations,
finance, asset
management, supply chain management, demand management, human resource
management,
product management, risk management, regulatory and compliance management,
inventory
management, infrastructure management, facilities management, analytics,
trading, tax, identity
management, vendor management, process management, project management,
operations
management, customer relationship management, workforce management, incident
management,
research and development, sales management, marketing management, fleet
management,
opportunity analytics, decision support, strategic planning, forecasting,
resource management,
property management, or other applications.
[0456] In embodiments, the machine vision system includes an artificial
intelligence system
that is trained to recognize a type of value chain asset based on a labeled
data set of images of
such type of value chain assets.
[0457] In embodiments, the digital twin presents an indicator of the type of
asset based on the
output of the artificial intelligence system.
[0458] In embodiments, the machine vision system includes an artificial
intelligence system
that is trained to recognize a type of activity involving a set of value chain
entities based on a
labeled data set of images of such type of activity.
[0459] In embodiments, the digital twin presents an indicator of the type of
activity based on
the output of the artificial intelligence system.
[0460] In embodiments, the machine vision system includes an artificial
intelligence system
that is trained to recognize a safety hazard involving a value chain entity
based on a training data
set that includes a set of images of value chain network activities and a set
of value chain
network safety outcomes.
[0461] In embodiments, the digital twin presents an indicator of the hazard
based on the output
of the artificial intelligence system.
[0462] In embodiments, the machine vision system includes an artificial
intelligence system
that is trained to predict a delay based on a training data set that includes
a set of images of value
chain network activities and a set of value chain network timing outcomes.
[0463] In embodiments, the digital twin presents an indicator of a likelihood
of delay based on
the output of the artificial intelligence system.
[0464] As noted elsewhere herein and in documents incorporated by reference,
artificial
intelligence (such as any of the techniques or systems described throughout
this disclosure) in
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connection with value chain network entities 652 and related processes and
applications may be
used to facilitate, among other things: (a) the optimization, automation
and/or control of various
functions, workflows, applications, features, resource utilization and other
factors, (b)
recognition or diagnosis of various states, entities, patterns, events,
contexts, behaviors, or other
elements; and/or (c) the forecasting of various states, events, contexts or
other factors. As
artificial intelligence improves, a large array of domain-specific and/or
general artificial
intelligence systems have become available and are likely to continue to
proliferate. As
developers seek solutions to domain-specific problems, such as ones relevant
to value chain
entities 652 and applications 630 described throughout this disclosure they
face challenges in
selecting artificial intelligence models (such as what set of neural networks,
machine learning
systems, expert systems, or the like to select) and in discovering and
selecting what inputs may
enable effective and efficient use of artificial intelligence for a given
problem. As noted above,
opportunity miners 1460 may assist with the discovery of opportunities for
increased automation
and intelligence; however, once opportunities are discovered, selection and
configuration of an
artificial intelligence solution still presents a significant challenge, one
that is likely to continue
to grow as artificial intelligence solutions proliferate.
[0465] One set of solutions to these challenges is an artificial intelligence
store 3504 that is
configured to enable collection, organization, recommendation and presentation
of relevant sets
of artificial intelligence systems based on one or more attributes of a domain
and/or a domain-
related problem. In embodiments, an artificial intelligence store 3504 may
include a set of
interfaces to artificial intelligence systems, such as enabling the download
of relevant artificial
intelligence applications, establishment of links or other connections to
artificial intelligence
systems (such as links to cloud-deployed artificial intelligence systems via
APIs, ports,
connectors, or other interfaces) and the like. The artificial intelligence
store 3504 may include
descriptive content with respect to each of a variety of artificial
intelligence systems, such as
metadata or other descriptive material indicating suitability of a system for
solving particular
types of problems (e.g., forecasting, NLP, image recognition, pattern
recognition, motion
detection, route optimization, or many others) and/or for operating on domain-
specific inputs,
data or other entities. In embodiments, the artificial intelligence store 3504
may be organized by
category, such as domain, input types, processing types, output types,
computational
requirements and capabilities, cost, energy usage, and other factors. In
embodiments, an interface
to the application store 3504 may take input from a developer and/or from the
platform (such as
from an opportunity miner 1460) that indicates one or more attributes of a
problem that may be
addressed through artificial intelligence and may provide a set of
recommendations, such as via
an artificial intelligence attribute search engine, for a subset of artificial
intelligence solutions
that may represent favorable candidates based on the developer's domain-
specific problem.
Search results or recommendations may, in embodiments, be based at least in
part on
collaborative filtering, such as by asking developers to indicate or select
elements of favorable
models, as well as by clustering, such as by using similarity matrices, k-
means clustering, or
other clustering techniques that associate similar developers, similar domain-
specific problems,
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and/or similar artificial intelligence solutions. The artificial intelligence
store 3504 may include
e-commerce features, such as ratings, reviews, links to relevant content, and
mechanisms for
provisioning, licensing, delivery and payment (including allocation of
payments to affiliates and
or contributors), including ones that operate using smart contract and/or
blockchain features to
automate purchasing, licensing, payment tracking, settlement of transactions,
or other features.
[0466] Referring to Fig. 43, the artificial intelligence system 1160 may
define a machine
learning model 3000 for performing analytics, simulation, decision making, and
prediction
making related to data processing, data analysis, simulation creation, and
simulation analysis of
one or more of the value chain entities 652. The machine learning model 3000
is an algorithm
and/or statistical model that performs specific tasks without using explicit
instructions, relying
instead on patterns and inference. The machine learning model 3000 builds one
or more
mathematical models based on training data to make predictions and/or
decisions without being
explicitly programmed to perform the specific tasks. The machine learning
model 3000 may
receive inputs of sensor data as training data, including event data 1034 and
state data 1140
related to one or more of the value chain entities 652. The sensor data input
to the machine
learning model 3000 may be used to train the machine learning model 3000 to
perform the
analytics, simulation, decision making, and prediction making relating to the
data processing,
data analysis, simulation creation, and simulation analysis of the one or more
of the value chain
entities 652. The machine learning model 3000 may also use input data from a
user or users of
the information technology system. The machine learning model 3000 may include
an artificial
neural network, a decision tree, a support vector machine, a Bayesian network,
a genetic
algorithm, any other suitable form of machine learning model, or a combination
thereof. The
machine learning model 3000 may be configured to learn through supervised
learning,
unsupervised learning, reinforcement learning, self learning, feature
learning, sparse dictionary
learning, anomaly detection, association rules, a combination thereof, or any
other suitable
algorithm for learning.
[0467] The artificial intelligence system 1160 may also define the digital
twin system 1700 to
create a digital replica of one or more of the value chain entities 652. The
digital replica of the
one or more of the value chain entities 652 may use substantially real-time
sensor data to provide
for substantially real-time virtual representation of the value chain entity
652 and provides for
simulation of one or more possible future states of the one or more value
chain entities 652. The
digital replica exists simultaneously with the one or more value chain
entities 652 being
replicated. The digital replica provides one or more simulations of both
physical elements and
properties of the one or more value chain entities 652 being replicated and
the dynamics thereof,
in embodiments, throughout the lifestyle of the one or more value chain
entities 652 being
replicated. The digital replica may provide a hypothetical simulation of the
one or more value
chain entities 652, for example during a design phase before the one or more
value chain entities
are constructed or fabricated, or during or after construction or fabrication
of the one or more
value chain entities by allowing for hypothetical extrapolation of sensor data
to simulate a state
of the one or more value chain entities 652, such as during high stress, after
a period of time has
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passed during which component wear may be an issue, during maximum throughput
operation,
after one or more hypothetical or planned improvements have been made to the
one or more
value chain entities 652, or any other suitable hypothetical situation. In
some embodiments, the
machine learning model 3000 may automatically predict hypothetical situations
for simulation
with the digital replica, such as by predicting possible improvements to the
one or more value
chain entities 652, predicting when one or more components of the one or more
value chain
entities 652 may fail, and/or suggesting possible improvements to the one or
more value chain
entities 652, such as changes to timing settings, arrangement, components, or
any other suitable
change to the value chain entities 652. The digital replica allows for
simulation of the one or
more value chain entities 652 during both design and operation phases of the
one or more value
chain entities 652, as well as simulation of hypothetical operation conditions
and configurations
of the one or more value chain entities 652. The digital replica allows for
invaluable analysis and
simulation of the one or more value chain entities, by facilitating
observation and measurement
of nearly any type of metric, including temperature, wear, light, vibration,
etc. not only in, on,
and around each component of the one or more value chain entities 652, but in
some
embodiments within the one or more value chain entities 652. In some
embodiments, the
machine learning model 3000 may process the sensor data including the event
data 1034 and the
state data 1140 to define simulation data for use by the digital twin system
1700. The machine
learning model 3000 may, for example, receive state data 1140 and event data
1034 related to a
particular value chain entity 652 of the plurality of value chain entities 652
and perform a series
of operations on the state data 1140 and the event data 1034 to format the
state data 1140 and the
event data 1034 into a format suitable for use by the digital twin system 1700
in creation of a
digital replica of the value chain entity 652. For example, one or more value
chain entities 652
may include a robot configured to augment products on an adjacent assembly
line. The machine
learning model 3000 may collect data from one or more sensors positioned on,
near, in, and/or
around the robot. The machine learning model 3000 may perform operations on
the sensor data
to process the sensor data into simulation data and output the simulation data
to the digital twin
system 1700. The digital twin simulation 1700 may use the simulation data to
create one or more
digital replicas of the robot, the simulation including for example metrics
including temperature,
wear, speed, rotation, and vibration of the robot and components thereof. The
simulation may be
a substantially real-time simulation, allowing for a human user of the
information technology to
view the simulation of the robot, metrics related thereto, and metrics related
to components
thereof, in substantially real time. The simulation may be a predictive or
hypothetical situation,
allowing for a human user of the information technology to view a predictive
or hypothetical
simulation of the robot, metrics related thereto, and metrics related to
components thereof.
[0468] In some embodiments, the machine learning model 3000 and the digital
twin system
1700 may process sensor data and create a digital replica of a set of value
chain entities of the
plurality of value chain entities 652 to facilitate design, real-time
simulation, predictive
simulation, and/or hypothetical simulation of a related group of value chain
entities. The digital
replica of the set of value chain entities may use substantially real-time
sensor data to provide for
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substantially real-time virtual representation of the set of value chain
entities and provide for
simulation of one or more possible future states of the set of value chain
entities. The digital
replica exists simultaneously with the set of value chain entities being
replicated. The digital
replica provides one or more simulations of both physical elements and
properties of the set of
value chain entities being replicated and the dynamics thereof, in embodiments
throughout the
lifestyle of the set of value chain entities being replicated. The one or more
simulations may
include a visual simulation, such as a wire-frame virtual representation of
the one or more value
chain entities 652 that may be viewable on a monitor, using an augmented
reality (AR)
apparatus, or using a virtual reality (VR) apparatus. The visual simulation
may be able to be
manipulated by a human user of the information technology system, such as
zooming or
highlighting components of the simulation and/or providing an exploded view of
the one or more
value chain entities 652. The digital replica may provide a hypothetical
simulation of the set of
value chain entities, for example during a design phase before the one or more
value chain
entities are constructed or fabricated, or during or after construction or
fabrication of the one or
more value chain entities by allowing for hypothetical extrapolation of sensor
data to simulate a
state of the set of value chain entities, such as during high stress, after a
period of time has passed
during which component wear may be an issue, during maximum throughput
operation, after one
or more hypothetical or planned improvements have been made to the set of
value chain entities,
or any other suitable hypothetical situation. In some embodiments, the machine
learning model
3000 may automatically predict hypothetical situations for simulation with the
digital replica,
such as by predicting possible improvements to the set of value chain
entities, predicting when
one or more components of the set of value chain entities may fail, and/or
suggesting possible
improvements to the set of value chain entities, such as changes to timing
settings, arrangement,
components, or any other suitable change to the value chain entities 652. The
digital replica
allows for simulation of the set of value chain entities during both design
and operation phases of
the set of value chain entities, as well as simulation of hypothetical
operation conditions and
configurations of the set of value chain entities. The digital replica allows
for invaluable analysis
and simulation of the one or more value chain entities, by facilitating
observation and
measurement of nearly any type of metric, including temperature, wear, light,
vibration, etc. not
only in, on, and around each component of the set of value chain entities, but
in some
embodiments within the set of value chain entities. In some embodiments, the
machine learning
model 3000 may process the sensor data including the event data 1034 and the
state data 1140 to
define simulation data for use by the digital twin system 1700. The machine
learning model 3000
may, for example, receive state data 1140 and event data 1034 related to a
particular value chain
entity 652 of the plurality of value chain entities 652 and perform a series
of operations on the
state data 1140 and the event data 1034 to format the state data 1140 and the
event data 1034 into
a format suitable for use by the digital twin system 1700 in the creation of a
digital replica of the
set of value chain entities. For example, a set of value chain entities may
include a die machine
configured to place products on a conveyor belt, the conveyor belt on which
the die machine is
configured to place the products, and a plurality of robots configured to add
parts to the products
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as they move along the assembly line. The machine learning model 3000 may
collect data from
one or more sensors positioned on, near, in, and/or around each of the die
machines, the conveyor
belt, and the plurality of robots. The machine learning model 3000 may perform
operations on
the sensor data to process the sensor data into simulation data and output the
simulation data to
the digital twin system 1700. The digital twin simulation 1700 may use the
simulation data to
create one or more digital replicas of the die machine, the conveyor belt, and
the plurality of
robots, the simulation including for example metrics including temperature,
wear, speed, rotation,
and vibration of the die machine, the conveyor belt, and the plurality of
robots and components
thereof. The simulation may be a substantially real-time simulation, allowing
for a human user of
the information technology to view the simulation of the die machine, the
conveyor belt, and the
plurality of robots, metrics related thereto, and metrics related to
components thereof, in
substantially real time. The simulation may be a predictive or hypothetical
situation, allowing for
a human user of the information technology to view a predictive or
hypothetical simulation of the
die machine, the conveyor belt, and the plurality of robots, metrics related
thereto, and metrics
related to components thereof.
[0469] In some embodiments, the machine learning model 3000 may prioritize
collection of
sensor data for use in digital replica simulations of one or more of the value
chain entities 652.
The machine learning model 3000 may use sensor data and user inputs to train,
thereby learning
which types of sensor data are most effective for creation of digital
replicate simulations of one
or more of the value chain entities 652. For example, the machine learning
model 3000 may find
that a particular value chain entity 652 has dynamic properties such as
component wear and
throughput affected by temperature, humidity, and load. The machine learning
model 3000 may,
through machine learning, prioritize collection of sensor data related to
temperature, humidity,
and load, and may prioritize processing sensor data of the prioritized type
into simulation data for
output to the digital twin system 1700. In some embodiments, the machine
learning model 3000
may suggest to a user of the information technology system that more and/or
different sensors of
the prioritized type be implemented in the information technology and value
chain system near
and around the value chain entity 652 being simulation such that more and/or
better data of the
prioritized type may be used in simulation of the value chain entity 652 via
the digital replica
thereof.
[0470] In some embodiments, the machine learning model 3000 may be configured
to learn to
determine which types of sensor data are to be processed into simulation data
for transmission to
the digital twin system 1700 based on one or both of a modeling goal and a
quality or type of
sensor data. A modeling goal may be an objective set by a user of the
information technology
system or may be predicted or learned by the machine learning model 3000.
Examples of
modeling goals include creating a digital replica capable of showing dynamics
of throughput on
an assembly line, which may include collection, simulation, and modeling of,
e.g., thermal,
electrical power, component wear, and other metrics of a conveyor belt, an
assembly machine,
one or more products, and other components of the value chain. The machine
learning model
3000 may be configured to learn to determine which types of sensor data are
necessary to be
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processed into simulation data for transmission to the digital twin system
1700 to achieve such a
model. In some embodiments, the machine learning model 3000 may analyze which
types of
sensor data are being collected, the quality and quantity of the sensor data
being collected, and
what the sensor data being collected represents, and may make decisions,
predictions, analyses,
and/or determinations related to which types of sensor data are and/or are not
relevant to
achieving the modeling goal and may make decisions, predictions, analyses,
and/or
determinations to prioritize, improve, and/or achieve the quality and quantity
of sensor data being
processed into simulation data for use by the digital twin system 1700 in
achieving the modeling
goal.
[0471] In some embodiments, a user of the information technology system may
input a
modeling goal into the machine learning model 3000. The machine learning model
3000 may
learn to analyze training data to output suggestions to the user of the
information technology
system regarding which types of sensor data are most relevant to achieving the
modeling goal,
such as one or more types of sensors positioned in, on, or near a value chain
entity or a plurality
of value chain entities that is relevant to the achievement of the modeling
goal is and/or are not
sufficient for achieving the modeling goal, and how a different configuration
of the types of
sensors, such as by adding, removing, or repositioning sensors, may better
facilitate achievement
of the modeling goal by the machine learning model 3000 and the digital twin
system 1700. In
some embodiments, the machine learning model 3000 may automatically increase
or decrease
collection rates, processing, storage, sampling rates, bandwidth allocation,
bitrates, and other
attributes of sensor data collection to achieve or better achieve the modeling
goal. In some
embodiments, the machine learning model 3000 may make suggestions or
predictions to a user of
the information technology system related to increasing or decreasing
collection rates,
processing, storage, sampling rates, bandwidth allocation, bitrates, and other
attributes of sensor
data collection to achieve or better achieve the modeling goal. In some
embodiments, the
machine learning model 3000 may use sensor data, simulation data, previous,
current, and/or
future digital replica simulations of one or more value chain entities 652 of
the plurality of value
chain entities 652 to automatically create and/or propose modeling goals. In
some embodiments,
modeling goals automatically created by the machine learning model 3000 may be
automatically
implemented by the machine learning model 3000. In some embodiments, modeling
goals
automatically created by the machine learning model 3000 may be proposed to a
user of the
information technology system, and implemented only after acceptance and/or
partial acceptance
by the user, such as after modifications are made to the proposed modeling
goal by the user.
[0472] In some embodiments, the user may input the one or more modeling goals,
for example,
by inputting one or more modeling commands to the information technology
system. The one or
more modeling commands may include, for example, a command for the machine
learning model
3000 and the digital twin system 1700 to create a digital replica simulation
of one value chain
entity 652 or a set of value chain entities of the plurality of 652, may
include a command for the
digital replica simulation to be one or more of a real-time simulation, and a
hypothetical
simulation. The modeling command may also include, for example, parameters for
what types of
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sensor data should be used, sampling rates for the sensor data, and other
parameters for the
sensor data used in the one or more digital replica simulations. In some
embodiments, the
machine learning model 3000 may be configured to predict modeling commands,
such as by
using previous modeling commands as training data. The machine learning model
3000 may
propose predicted modeling commands to a user of the information technology
system, for
example, to facilitate simulation of one or more of the value chain entities
652 that may be useful
for the management of the value chain entities 652 and/or to allow the user to
easily identify
potential issues with or possible improvements to the value chain entities
652.
[0473] In some embodiments, the machine learning model 3000 may be configured
to evaluate
a set of hypothetical simulations of one or more of the value chain entities
652. The set of
hypothetical simulations may be created by the machine learning model 3000 and
the digital twin
system 1700 as a result of one or more modeling commands, as a result of one
or more modeling
goals, one or more modeling commands, by prediction by the machine learning
model 3000, or a
combination thereof. The machine learning model 3000 may evaluate the set of
hypothetical
simulations based on one or more metrics defined by the user, one or more
metrics defined by the
machine learning model 3000, or a combination thereof. In some embodiments,
the machine
learning model 3000 may evaluate each of the hypothetical simulations of the
set of hypothetical
simulations independently of one another. In some embodiments, the machine
learning model
3000 may evaluate one or more of the hypothetical simulations of the set of
hypothetical
simulations in relation to one another, for example by ranking the
hypothetical simulations or
creating tiers of the hypothetical simulations based on one or more metrics.
[0474] In some embodiments, the machine learning model 3000 may include one or
more
model interpretability systems to facilitate human understanding of outputs of
the machine
learning model 3000, as well as information and insight related to cognition
and processes of the
machine learning model 3000, i.e., the one or more model interpretability
systems allow for
human understanding of not only "what" the machine learning model 3000 is
outputting, but also
"why" the machine learning model 3000 is outputting the outputs thereof, and
what process led
to the 3000 formulating the outputs. The one or more model interpretability
systems may also be
used by a human user to improve and guide training of the machine learning
model 3000, to help
debug the machine learning model 3000, to help recognize bias in the machine
learning model
3000. The one or more model interpretability systems may include one or more
of linear
regression, logistic regression, a generalized linear model (GLM), a
generalized additive model
(GAM), a decision tree, a decision rule, RuleFit, Naive Bayes Classifier, a K-
nearest neighbors
algorithm, a partial dependence plot, individual conditional expectation
(ICE), an accumulated
local effects (ALE) plot, feature interaction, permutation feature importance,
a global surrogate
model, a local surrogate (LIME) model, scoped rules, i.e. anchors, Shapley
values, Shapley
additive explanations (SHAP), feature visualization, network dissection, or
any other suitable
machine learning interpretability implementation. In some embodiments, the one
or more model
interpretability systems may include a model dataset visualization system. The
model dataset
visualization system is configured to automatically provide to a human user of
the information
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technology system visual analysis related to distribution of values of the
sensor data, the
simulation data, and data nodes of the machine learning model 3000.
[0475] In some embodiments, the machine learning model 3000 may include and/or
implement
an embedded model interpretability system, such as a Bayesian case model (BCM)
or glass box.
The Bayesian case model uses Bayesian case-based reasoning, prototype
classification, and
clustering to facilitate human understanding of data such as the sensor data,
the simulation data,
and data nodes of the machine learning model 3000. In some embodiments, the
model
interpretability system may include and/or implement a glass box
interpretability method, such as
a Gaussian process, to facilitate human understanding of data such as the
sensor data, the
simulation data, and data nodes of the machine learning model 3000.
[0476] In some embodiments, the machine learning model 3000 may include and/or
implement
testing with concept activation vectors (TCAV). The TCAV allows the machine
learning model
3000 to learn human-interpretable concepts, such as "running," "not running,"
"powered," "not
powered," "robot," "human," "truck," or "ship" from examples by a process
including defining
the concept, determining concept activation vectors, and calculating
directional derivatives. By
learning human-interpretable concepts, objects, states, etc., TCAV may allow
the machine
learning model 3000 to output useful information related to the value chain
entities 652 and data
collected therefrom in a format that is readily understood by a human user of
the information
technology system.
[0477] In some embodiments, the machine learning model 3000 may be and/or
include an
artificial neural network, e.g. a connectionist system configured to "learn"
to perform tasks by
considering examples and without being explicitly programmed with task-
specific rules. The
machine learning model 3000 may be based on a collection of connected units
and/or nodes that
may act like artificial neurons that may in some ways emulate neurons in a
biological brain. The
units and/or nodes may each have one or more connections to other units and/or
nodes. The units
and/or nodes may be configured to transmit information, e.g. one or more
signals, to other units
and/or nodes, process signals received from other units and/or nodes, and
forward processed
signals to other units and/or nodes. One or more of the units and/or nodes and
connections
therebetween may have one or more numerical "weights" assigned. The assigned
weights may be
configured to facilitate learning, i.e. training, of the machine learning
model 3000. The weights
assigned weights may increase and/or decrease one or more signals between one
or more units
and/or nodes, and in some embodiments may have one or more thresholds
associated with one or
more of the weights. The one or more thresholds may be configured such that a
signal is only
sent between one or more units and/or nodes, if a signal and/or aggregate
signal crosses the
threshold. In some embodiments, the units and/or nodes may be assigned to a
plurality of layers,
each of the layers having one or both of inputs and outputs. A first layer may
be configured to
receive training data, transform at least a portion of the training data, and
transmit signals related
to the training data and transformation thereof to a second layer. A final
layer may be configured
to output an estimate, conclusion, product, or other consequence of processing
of one or more
inputs by the machine learning model 3000. Each of the layers may perform one
or more types of
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transformations, and one or more signals may pass through one or more of the
layers one or more
times. In some embodiments, the machine learning model 3000 may employ deep
learning and
being at least partially modeled and/or configured as a deep neural network, a
deep belief
network, a recurrent neural network, and/or a convolutional neural network,
such as by being
configured to include one or more hidden layers.
[0478] In some embodiments, the machine learning model 3000 may be and/or
include a
decision tree, e.g. a tree-based predictive model configured to identify one
or more observations
and determine one or more conclusions based on an input. The observations may
be modeled as
one or more "branches" of the decision tree, and the conclusions may be
modeled as one or more
"leaves" of the decision tree. In some embodiments, the decision tree may be a
classification tree.
the classification tree may include one or more leaves representing one or
more class labels, and
one or more branches representing one or more conjunctions of features
configured to lead to the
class labels. In some embodiments, the decision tree may be a regression tree.
The regression tree
may be configured such that one or more target variables may take continuous
values.
[0479] In some embodiments, the machine learning model 3000 may be and/or
include a
support vector machine, e.g. a set of related supervised learning methods
configured for use in
one or both of classification and regression-based modeling of data. The
support vector machine
may be configured to predict whether a new example falls into one or more
categories, the one or
more categories being configured during training of the support vector
machine.
[0480] In some embodiments, the machine learning model 3000 may be configured
to perform
regression analysis to determine and/or estimate a relationship between one or
more inputs and
one or more features of the one or more inputs. Regression analysis may
include linear
regression, wherein the machine learning model 3000 may calculate a single
line to best fit input
data according to one or more mathematical criteria.
[0481] In embodiments, inputs to the machine learning model 3000 (such as a
regression
model, Bayesian network, supervised model, or other type of model) may be
tested, such as by
using a set of testing data that is independent from the data set used for the
creation and/or
training of the machine learning model, such as to test the impact of various
inputs to the
accuracy of the model 3000. For example, inputs to the regression model may be
removed,
including single inputs, pairs of inputs, triplets, and the like, to determine
whether the absence of
inputs creates a material degradation of the success of the model 3000. This
may assist with
recognition of inputs that are in fact correlated (e.g., are linear
combinations of the same
underlying data), that are overlapping, or the like. Comparison of model
success may help select
among alternative input data sets that provide similar information, such as to
identify the inputs
(among several similar ones) that generate the least "noise" in the model,
that provide the most
impact on model effectiveness for the lowest cost, or the like. Thus, input
variation and testing of
the impact of input variation on model effectiveness may be used to prune or
enhance model
performance for any of the machine learning systems described throughout this
disclosure.
[0482] In some embodiments, the machine learning model 3000 may be and/or
include a
Bayesian network. The Bayesian network may be a probabilistic graphical model
configured to
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represent a set of random variables and conditional independence of the set of
random variables.
The Bayesian network may be configured to represent the random variables and
conditional
independence via a directed acyclic graph. The Bayesian network may include
one or both of a
dynamic Bayesian network and an influence diagram.
[0483] In some embodiments, the machine learning model 3000 may be defined via
supervised
learning, i.e. one or more algorithms configured to build a mathematical model
of a set of
training data containing one or more inputs and desired outputs. The training
data may consist of
a set of training examples, each of the training examples having one or more
inputs and desired
outputs, i.e. a supervisory signal. Each of the training examples may be
represented in the
machine learning model 3000 by an array and/or a vector, i.e. a feature
vector. The training data
may be represented in the machine learning model 3000 by a matrix. The machine
learning
model 3000 may learn one or more functions via iterative optimization of an
objective function,
thereby learning to predict an output associated with new inputs. Once
optimized, the objective
function may provide the machine learning model 3000 with the ability to
accurately determine
an output for inputs other than inputs included in the training data. In some
embodiments, the
machine learning model 3000 may be defined via one or more supervised learning
algorithms
such as active learning, statistical classification, regression analysis, and
similarity learning.
Active learning may include interactively querying, by the machine learning
model AILD102T, a
user and/or an information source to label new data points with desired
outputs. Statistical
classification may include identifying, by the machine learning model 3000, to
which a set of
subcategories, i.e. subpopulations, a new observation belongs based on a
training set of data
containing observations having known categories. Regression analysis may
include estimating,
by the machine learning model 3000 relationships between a dependent variable,
i.e. an outcome
variable, and one or more independent variables, i.e. predictors, covariates,
and/or features.
Similarity learning may include learning, by the machine learning model 3000,
from examples
using a similarity function, the similarity function being designed to measure
how similar or
related two objects are.
[0484] In some embodiments, the machine learning model 3000 may be defined via
unsupervised learning, i.e. one or more algorithms configured to build a
mathematical model of a
set of data containing only inputs by finding structure in the data such as
grouping or clustering
of data points. In some embodiments, the machine learning model 3000 may learn
from test data,
i.e. training data, that has not been labeled, classified, or categorized. The
unsupervised learning
algorithm may include identifying, by the machine learning model 3000,
commonalities in the
training data and learning by reacting based on the presence or absence of the
identified
commonalities in new pieces of data. In some embodiments, the machine learning
model 3000
may generate one or more probability density functions. In some embodiments,
the machine
learning model 3000 may learn by performing cluster analysis, such as by
assigning a set of
observations into subsets, i.e. clusters, according to one or more
predesignated criteria, such as
according to a similarity metric of which internal compactness, separation,
estimated density,
and/or graph connectivity are factors.
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[0485] In some embodiments, the machine learning model 3000 may be defined via
semi-
supervised learning, i.e. one or more algorithms using training data wherein
some training
examples may be missing training labels. The semi-supervised learning may be
weakly
supervised learning, wherein the training labels may be noisy, limited, and/or
imprecise. The
noisy, limited, and/or imprecise training labels may be cheaper and/or less
labor intensive to
produce, thus allowing the machine learning model 3000 to train on a larger
set of training data
for less cost and/or labor.
[0486] In some embodiments, the machine learning model 3000 may be defined via
reinforcement learning, such as one or more algorithms using dynamic
programming techniques
such that the machine learning model 3000 may train by taking actions in an
environment in
order to maximize a cumulative reward. In some embodiments, the training data
is represented as
a Markov Decision Process.
[0487] In some embodiments, the machine learning model 3000 may be defined via
self-
learning, wherein the machine learning model 3000 is configured to train using
training data with
no external rewards and no external teaching, such as by employing a Crossbar
Adaptive Array
(CAA). The CAA may compute decisions about actions and/or emotions about
consequence
situations in a crossbar fashion, thereby driving teaching of the machine
learning model 3000 by
interactions between cognition and emotion.
[0488] In some embodiments, the machine learning model 3000 may be defined via
feature
learning, i.e. one or more algorithms designed to discover increasingly
accurate and/or apt
representations of one or more inputs provided during training, e.g. training
data. Feature
learning may include training via principal component analysis and/or cluster
analysis. Feature
learning algorithms may include attempting, by the machine learning model
3000, to preserve
input training data while also transforming the input training data such that
the transformed input
training data is useful. In some embodiments, the machine learning model 3000
may be
configured to transform the input training data prior to performing one or
more classifications
and/or predictions of the input training data. Thus, the machine learning
model 3000 may be
configured to reconstruct input training data from one or more unknown data-
generating
distributions without necessarily conforming to implausible configurations of
the input training
data according to the distributions. In some embodiments, the feature learning
algorithm may be
performed by the machine learning model 3000 in a supervised, unsupervised, or
semi-
supervised manner.
[0489] In some embodiments, the machine learning model 3000 may be defined via
anomaly
detection, i.e. by identifying rare and/or outlier instances of one or more
items, events and/or
observations. The rare and/or outlier instances may be identified by the
instances differing
significantly from patterns and/or properties of a majority of the training
data. Unsupervised
anomaly detection may include detecting of anomalies, by the machine learning
model 3000, in
an unlabeled training data set under an assumption that a majority of the
training data is
"normal." Supervised anomaly detection may include training on a data set
wherein at least a
portion of the training data has been labeled as "normal" and/or "abnormal."
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[0490] In some embodiments, the machine learning model 3000 may be defined via
robot
learning. Robot learning may include generation, by the machine learning model
3000, of one or
more curricula, the curricula being sequences of learning experiences, and
cumulatively
acquiring new skills via exploration guided by the machine learning model 3000
and social
interaction with humans by the machine learning model 3000. Acquisition of new
skills may be
facilitated by one or more guidance mechanisms such as active learning,
maturation, motor
synergies, and/or imitation.
[0491] In some embodiments, the machine learning model 3000 can be defined via
association
rule learning. Association rule learning may include discovering
relationships, by the machine
learning model 3000, between variables in databases, in order to identify
strong rules using some
measure of "interestingness." Association rule learning may include
identifying, learning, and/or
evolving rules to store, manipulate and/or apply knowledge. The machine
learning model 3000
may be configured to learn by identifying and/or utilizing a set of relational
rules, the relational
rules collectively representing knowledge captured by the machine learning
model 3000.
Association rule learning may include one or more of learning classifier
systems, inductive logic
programming, and artificial immune systems. Learning classifier systems are
algorithms that may
combine a discovery component, such as one or more genetic algorithms, with a
learning
component, such as one or more algorithms for supervised learning,
reinforcement learning, or
unsupervised learning. Inductive logic programming may include rule-learning,
by the machine
learning model 3000, using logic programming to represent one or more of input
examples,
background knowledge, and hypothesis determined by the machine learning model
3000 during
training. The machine learning model 3000 may be configured to derive a
hypothesized logic
program entailing all positive examples given an encoding of known background
knowledge and
a set of examples represented as a logical database of facts.
[0492] In embodiments, another set of solutions, which may be deployed alone
or in connection
with other elements of the platform, including the artificial intelligence
store 3504, may include a
set of functional imaging capabilities FMRP102, which may comprise monitoring
systems 640
and in some cases physical process observation systems 1510 and/or software
interaction
observation systems 1500, such as for monitoring various value chain entities
652. Functional
imaging systems FMRP102 may, in embodiments, provide considerable insight into
the types of
artificial intelligence that are likely to be most effective in solving
particular types of problems
most effectively. As noted elsewhere in this disclosure and in the documents
incorporated by
reference herein, computational and networking systems, as they grow in scale,
complexity and
interconnections, manifest problems of information overload, noise, network
congestion, energy
waste, and many others. As the Internet of Things grows to hundreds of
billions of devices, and
virtually countless potential interconnections, optimization becomes
exceedingly difficult. One
source for insight is the human brain, which faces similar challenges and has
evolved, over
millennia, reasonable solutions to a wide range of very difficult optimization
problems. The
human brain operates with a massive neural network organized into
interconnected modular
systems, each of which has a degree of adaptation to solve particular
problems, from regulation
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of biological systems and maintenance of homeostasis, to detection of a wide
range of static and
dynamic patterns, to recognition of threats and opportunities, among many
others. Functional
imaging FMRP102, such as functional magnetic resonance imaging (fMRI),
electroencephalogram (EEG), computed tomography (CT) and other brain imaging
systems have
improved to the point that patterns of brain activity can be recognized in
real time and temporally
associated with other information, such behaviors, stimulus information,
environmental condition
data, gestures, eye movements, and other information, such that via functional
imaging
FMRP102, either alone or in combination with other information collected by
monitoring
systems IPX106, the platform may determine and classify what brain modules,
operations,
systems, and/or functions are employed during the undertaking of a set of
tasks or activities, such
as ones involving software interaction 1500, physical process observations
1510, or a
combination thereof. This classification may assist in selection and/or
configuration of a set of
artificial intelligence solutions, such as from an artificial intelligence
store 3504, that includes a
similar set of capabilities and/or functions to the set of modules and
functions of the human brain
when undertaking an activity, such as for the initial configuration of a
robotic process automation
(RPA) system 1442 that automates a task performed by an expert human. Thus,
the platform may
include a system that takes input from a functional imaging system FRMP102 to
configure,
optionally automatically based on matching of attributes between one or more
biological
systems, such as brain systems, and one or more artificial intelligence
systems, a set of artificial
intelligence capabilities for a robotic process automation system. Selection
and configuration
may further comprise selection of inputs to robotic process automation and/or
artificial
intelligence that are configured at least in part based on functional imaging
of the brain while
workers undertake tasks, such as selection of visual inputs (such as images
from cameras) where
vision systems of the brain are highly activated, selection of acoustic inputs
where auditory
systems of the brain are highly activated, selection of chemical inputs (such
as chemical sensors)
where olfactory systems of the brain are highly activated, or the like. Thus,
a biologically aware
robotic process automation system may be improved by having initial
configuration, or iterative
improvement, be guided, either automatically or under developer control, by
imaging-derived
information collected as workers perform expert tasks that may benefit from
automation.
[0493] Referring to Fig. 27, additional details of an embodiment of the
platform 604 are
provided, in particular relating to elements of the adaptive intelligence
layer 614 that facilitate
improved edge intelligence, including the adaptive edge compute management
system 1400 and
the edge intelligence system 1420. These elements provide a set of systems
that adaptively
manage "edge" computation, storage and processing, such as by varying storage
locations for
data and processing locations (e.g., optimized by Al) between on-device
storage, local systems,
in the network and in the cloud. These elements enable facilitation of a
dynamic definition by a
user, such as a developer, operator, or host of the platform 102, of what
constitutes the "edge" for
purposes of a given application. For example, for environments where data
connections are slow
or unreliable (such as where a facility does not have good access to cellular
networks (such as
due to remoteness of some environments (such as in geographies with poor
cellular network
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infrastructure), shielding or interference (such as where density of network-
using systems, thick
metals hulls of container ships, thick metal container walls, underwater or
underground location,
or presence of large metal objects (such as vaults, hulls, containers and the
like) interferes with
networking performance), and/or congestion (such as where there are many
devices seeking
access to limited networking facilities), edge computing capabilities can be
defined and deployed
to operate on the local area network of an environment, in peer-to-peer
networks of devices, or
on computing capabilities of local value chain entities 652. For example, in
an environment with
a limited set of computational and/or networking resources, tasks may be
intelligently load
balanced based on a current context (e.g., network availability, latency,
congestion, and the like)
and, in an example, one type of data may be prioritized for processing, or one
workflow
prioritized over another workflow, and the like. Where strong data connections
are available
(such as where good backhaul facilities exist), edge computing capabilities
can be disposed in the
network, such as for caching frequently used data at locations that improve
input/output
performance, reduce latency, or the like. Thus, adaptive definition and
specification of where
edge computing operations are enabled, under control of a developer or
operator, or optionally
determined automatically, such as by an expert system or automation system,
such as based on
detected network conditions for an environment, for a financial entity 652, or
for a network as a
whole.
[0494] In embodiments, edge intelligence 1420 enables adaptation of edge
computation
(including where computation occurs within various available networking
resources, how
networking occurs (such as by protocol selection), where data storage occurs,
and the like) that is
multi-application aware, such as accounting for QoS, latency requirements,
congestion, and cost
as understood and prioritized based on awareness of the requirements, the
prioritization, and the
value (including ROI, yield, and cost information, such as costs of failure)
of edge computation
capabilities across more than one application, including any combinations and
subsets of the
applications 630 described herein or in the documents incorporated herein by
reference.
[0495] Referring to Fig. 35, an embodiment of the platform 604 is provided. As
with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 614, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 1160, a set of data storage
facilities or systems 624, and
a set of monitoring facilities or systems 614. The platform 604 may support a
set of applications
630 (including processes, workflows, activities, events, use cases and
applications) for enabling
an enterprise to manage a set of value chain network entities 652, such as
from a point of origin
to a point of customer use of a product 650, which may be an intelligent
product.
[0496] In embodiments, the platform 604 may include a unified set of adaptive
edge computing
and other edge intelligence systems 1420 that provide coordinated edge
computation and other
edge intelligence 1420 capabilities for a set of multiple applications 630 of
various types, such as
a set of supply chain management applications 1500, demand management
applications 1502,
intelligent product applications 1510 and enterprise resource management
applications 1520 that
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monitor and/or manage a value chain network and a set of value chain network
entities 652. In
embodiments, edge intelligence capabilities of the systems and methods
described herein may
include, but are not limited to, on-premise edge devices and resources, such
as local area network
resources, and network edge devices, such as those deployed at the edge of a
cellular network or
within a peripheral data center, both of which may deploy edge intelligence,
as described herein,
to, for example, carry out intelligent processing tasks at these edge
locations before transferring
data or other matter, to the primary or core cellular network command or
central data center.
[0497] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a unified set of adaptive edge computing systems
that provide
.. coordinated edge computation for a set of applications of at least two
types from among a set of
demand management applications, a set of supply chain applications, a set of
intelligent product
applications and a set of enterprise resource management applications for a
category of goods.
[0498] The adaptive edge computing and other edge intelligence systems 1420
may thus
provide, in embodiments, intelligence for monitoring, managing, controlling,
or otherwise
handling a wide range of facilities, devices, systems, environments, and
assets, such as supply
chain infrastructure facilities 1560 and other value chain network entities
652 that are involved as
a product 650 travels from a point of origin through distribution and retail
channels to an
environment where it is used by a customer. This unification may provide a
number of
advantages, including improved monitoring, improved remote control, improved
autonomy,
improved prediction, improved classification, improved visualization and
insight, improved
visibility, and others. These may include adaptive edge computing and other
edge intelligence
systems 1420 that are used in connection with demand factors 1540 and supply
factors 1550, so
that an application 630 may benefit from information collected by, processed
by, or produced by
the adaptive edge computing and other edge intelligence systems 1420 for other
applications 630
of the platform 604, and a user can develop insights about connections among
the factors and
control one or both of them with coordinated intelligence. For example,
coordinated intelligence
may include, but is not limited to, analytics and processing for monitoring
data streams, as
described herein, for the purposes of classification, prediction or some other
type of analytic
modeling. Such coordinated intelligence methods and systems may be applied in
an automated
manner in which differing combinations of intelligence assets are applied. As
an example, within
an industrial environment the coordinated intelligence system may monitor
signals coming from
machinery deployed in the environment. The coordinated intelligence system may
classify,
predict or perform some other intelligent analytics, in combination, for the
purpose of, for
example, determining a state of a machine, such as a machine in a deteriorated
state, in an at-risk
state, or some other state. The determination of a state may cause a control
system to alter a
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control regime, for example, slowing or shutting down a machine that is in a
deteriorating state.
In embodiments, the coordinated intelligence system may coordinate across
multiple entities of a
value chain, supply chain and the like. For example, the monitoring of the
deteriorating machine
in the industrial environment may simultaneously occur with analytics related
to parts suppliers
and availability, product supply and inventory predictions, or some other
coordinated intelligence
operation. The adaptive edge computing and other edge intelligence systems
1420 may be
adapted over time, such as by learning on outcomes 1040 or other operations of
the other
adaptive intelligent systems 614, such as to determine which elements
collected and/or processed
by the adaptive edge computing and other edge intelligence systems 1420 should
be made
available to which applications 630, what elements and/or content provide the
most benefit, what
data should be stored or cached for immediate retrieval, what data can be
discarded versus saved,
what data is most beneficial to support adaptive intelligent systems 614, and
for other uses.
[0499] Referring to Fig. 36, in embodiments, the unified set of adaptive edge
computing
systems that provide coordinated edge computation include a wide range of
systems, such as
classification systems 1610 (such as image classification systems, object type
recognition
systems, and others), video processing systems 1612 (such as video compression
systems), signal
processing systems 1614 (such as analog-to-digital transformation systems,
digital-to-analog
transformation systems, RF filtering systems, analog signal processing
systems, multiplexing
systems, statistical signal processing systems, signal filtering systems,
natural language
processing systems, sound processing systems, ultrasound processing systems,
and many others),
data processing systems 1630 (such as data filtering systems, data integration
systems, data
extraction systems, data loading systems, data transformation systems, point
cloud processing
systems, data normalization systems, data cleansing system, data deduplication
systems, graph-
based data storage systems, object-oriented data storage systems, and others),
predictive systems
1620 (such as motion prediction systems, output prediction systems, activity
prediction systems,
fault prediction systems, failure prediction systems, accident prediction
systems, event
predictions systems, event prediction systems, and many others), configuration
systems 1630
(such as protocol selection systems, storage configuration systems, peer-to-
peer network
configuration systems, power management systems, self-configuration systems,
self-healing
.. systems, handshake negotiation systems, and others), artificial
intelligence systems 1160 (such as
clustering systems, variation systems, machine learning systems, expert
systems, rule-based
systems, deep learning systems, and many others), system management and
control systems 1640
(such as autonomous control systems, robotic control systems, RF spectrum
management
systems, network resource management systems, storage management systems, data
management
systems, and others), robotic process automation systems, analytic and
modeling systems 1650
(such as data visualization systems, clustering systems, similarity analysis
systems, random forest
systems, physical modeling systems, interaction modeling systems, simulation
systems, and
many others), entity discovery systems, security systems 1670 (such as
cybersecurity systems,
biometric systems, intrusion detection systems, firewall systems, and others),
rules engine
systems, workflow automation systems, opportunity discovery systems, testing
and diagnostic
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systems 1660, software image propagation systems, virtualization systems,
digital twin systems,
Internet of Things monitoring systems, routing systems, switching systems,
indoor location
systems, geolocation systems, and others.
[0500] In embodiments, the interface is a user interface for a command center
dashboard by
which an enterprise orchestrates a set of value chain entities related to a
type of product.
[0501] In embodiments, the interface is a user interface of a local management
system located
in an environment that hosts a set of value chain entities.
[0502] In embodiments, the local management system user interface facilitates
configuration of
a set of network connections for the adaptive edge computing systems.
[0503] In embodiments, the local management system user interface facilitates
configuration of
a set of data storage resources for the adaptive edge computing systems.
[0504] In embodiments, the local management system user interface facilitates
configuration of
a set of data integration capabilities for the adaptive edge computing
systems.
[0505] In embodiments, the local management system user interface facilitates
configuration of
a set of machine learning input resources for the adaptive edge computing
systems.
[0506] In embodiments, the local management system user interface facilitates
configuration of
a set of power resources that support the adaptive edge computing systems.
[0507] In embodiments, the local management system user interface facilitates
configuration of
a set of workflows that are managed by the adaptive edge computing systems.
[0508] In embodiments, the interface is a user interface of a mobile computing
device that has a
network connection to the adaptive edge computing systems.
[0509] In embodiments, the interface is an application programming interface.
[0510] In embodiments, the application programming interface facilitates
exchange of data
between the adaptive edge computing systems and a cloud-based artificial
intelligence system.
[0511] In embodiments, the application programming interface facilitates
exchange of data
between the adaptive edge computing systems and a real-time operating system
of a cloud data
management platform.
[0512] In embodiments, the application programming interface facilitates
exchange of data
between the adaptive edge computing systems and a computational facility of a
cloud data
management platform.
[0513] In embodiments, the application programming interface facilitates
exchange of data
between the adaptive edge computing systems and a set of environmental sensors
that collect
data about an environment that hosts a set of value chain network entities.
[0514] In embodiments, the application programming interface facilitates
exchange of data
between the adaptive edge computing systems and a set of sensors that collect
data about a
product.
[0515] In embodiments, the application programming interface facilitates
exchange of data
between the adaptive edge computing systems and a set of sensors that collect
data published by
an intelligent product.
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[0516] In embodiments, the application programming interface facilitates
exchange of data
between the adaptive edge computing systems and a set of sensors that collect
data published by
a set of Internet of Things systems that are disposed in an environment that
hosts a set of value
chain network entities.
[0517] In embodiments, the set of demand management applications, supply chain
applications,
intelligent product applications and enterprise resource management
applications may include,
for example, any of the applications mentioned throughout this disclosure or
in the documents
incorporated by reference herein.
[0518] Unified Adaptive Intelligence
[0519] Referring to Fig. 37, an embodiment of the platform 604 is provided. As
with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 614, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 1160, a set of data storage
facilities or systems 624, and
a set of monitoring facilities or systems 614. The platform 604 may support a
set of applications
630 (including processes, workflows, activities, events, use cases and
applications) for enabling
an enterprise to manage a set of value chain network entities 652, such as
from a point of origin
to a point of customer use of a product 650, which may be an intelligent
product.
[0520] In embodiments, the VCNP 102 may include a unified set of adaptive
intelligent
systems 614 that provide coordinated intelligence for a set of various
applications, such as
demand management applications 1502, a set of supply chain applications 1500,
a set of
intelligent product applications 1510, a set of enterprise resource management
applications 1520
and a set of asset management applications 1530 for a category of goods.
[0521] In embodiments, the unified set of adaptive intelligence systems
include a wide variety
of systems described throughout this disclosure and in the documents
incorporated herein by
reference, such as, without limitation, the edge intelligence systems 1420,
classification systems
1610, data processing systems 1612, signal processing systems 1614, artificial
intelligence
systems 1160, prediction systems 1620, configuration systems 1630, control
systems 1640,
analytic systems 1650, testing/diagnostic systems 1660, security systems 1670
and other systems,
whether used for edge intelligence or for intelligence within a network,
within an application, or
in the cloud, as well as to serve various layers of the platform 604. These
include neural
networks, deep learning systems, model-based systems, expert systems, machine
learning
systems, rule-based systems, opportunity miners, robotic process automation
systems, data
transformation systems, data extraction systems, data loading systems, genetic
programming
systems, image classification systems, video compression systems, analog-to-
digital
transformation systems, digital-to-analog transformation systems, signal
analysis systems, RF
filtering systems, motion prediction systems, object type recognition systems,
point cloud
processing systems, analog signal processing systems, signal multiplexing
systems, data fusion
systems, sensor fusion systems, data filtering systems, statistical signal
processing systems,
signal filtering systems, signal processing systems, protocol selection
systems, storage
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configuration systems, power management systems, clustering systems, variation
systems,
machine learning systems, event prediction systems, autonomous control
systems, robotic control
systems, robotic process automation systems, data visualization systems, data
normalization
systems, data cleansing systems, data deduplication systems, graph-based data
storage systems,
intelligent agent systems, object-oriented data storage systems, self-
configuration systems, self-
healing systems, self-organizing systems, self-organizing map systems, cost-
based routing
systems, handshake negotiation systems, entity discovery systems,
cybersecurity systems,
biometric systems, natural language processing systems, speech processing
systems, voice
recognition systems, sound processing systems, ultrasound processing systems,
artificial
intelligence systems, rules engine systems, workflow automation systems,
opportunity discovery
systems, physical modeling systems, testing systems, diagnostic systems,
software image
propagation systems, peer-to-peer network configuration systems, RF spectrum
management
systems, network resource management systems, storage management systems, data
management
systems, intrusion detection systems, firewall systems, virtualization
systems, digital twin
systems, Internet of Things monitoring systems, routing systems, switching
systems, indoor
location systems, geolocation systems, parsing systems, semantic filtering
systems, machine
vision systems, fuzzy logic systems, recommendation systems, dialog management
systems, and
others.
[0522] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a unified set of adaptive intelligence systems that
provide coordinated
intelligence for a set of demand management applications, a set of supply
chain applications, a
set of intelligent product applications and a set of enterprise resource
management applications
for a category of goods.
[0523] In embodiments, the unified set of adaptive intelligent systems
includes a set of artificial
intelligence systems. In embodiments, the unified set of adaptive intelligent
systems includes a
set of neural networks. In embodiments, the unified set of adaptive
intelligent systems includes a
set of deep learning systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of model-based systems.
[0524] In embodiments, the unified set of adaptive intelligent systems
includes a set of expert
systems. In embodiments, the unified set of adaptive intelligent systems
includes a set of machine
learning systems. In embodiments, the unified set of adaptive intelligent
systems includes a set of
rule-based systems. In embodiments, the unified set of adaptive intelligent
systems includes a set
of opportunity miners.
[0525] In embodiments, the unified set of adaptive intelligent systems
includes a set of robotic
process automation systems. In embodiments, the unified set of adaptive
intelligent systems
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includes a set of data transformation systems. In embodiments, the unified set
of adaptive
intelligent systems includes a set of data extraction systems. In embodiments,
the unified set of
adaptive intelligent systems includes a set of data loading systems. In
embodiments, the unified
set of adaptive intelligent systems includes a set of genetic programming
systems.
[0526] In embodiments, the unified set of adaptive intelligent systems
includes a set of image
classification systems. In embodiments, the unified set of adaptive
intelligent systems includes a
set of video compression systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of analog-to-digital transformation systems. In embodiments,
the unified set of
adaptive intelligent systems includes a set of digital-to-analog
transformation systems. In
embodiments, the unified set of adaptive intelligent systems includes a set of
signal analysis
systems.
[0527] In embodiments, the unified set of adaptive intelligent systems
includes a set of RF
filtering systems. In embodiments, the unified set of adaptive intelligent
systems includes a set of
motion prediction systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of object type recognition systems. In embodiments, the unified
set of adaptive
intelligent systems includes a set of point cloud processing systems. In
embodiments, the unified
set of adaptive intelligent systems includes a set of analog signal processing
systems.
[0528] In embodiments, the unified set of adaptive intelligent systems
includes a set of signal
multiplexing systems. In embodiments, the unified set of adaptive intelligent
systems includes a
set of data fusion systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of sensor fusion systems. In embodiments, the unified set of
adaptive intelligent
systems includes a set of data filtering systems. In embodiments, the unified
set of adaptive
intelligent systems includes a set of statistical signal processing systems.
[0529] In embodiments, the unified set of adaptive intelligent systems
includes a set of signal
filtering systems. In embodiments, the unified set of adaptive intelligent
systems includes a set of
signal processing systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of protocol selection systems. In embodiments, the unified set
of adaptive
intelligent systems includes a set of storage configuration systems. In
embodiments, the unified
set of adaptive intelligent systems includes a set of power management
systems.
[0530] In embodiments, the unified set of adaptive intelligent systems
includes a set of
clustering systems. In embodiments, the unified set of adaptive intelligent
systems includes a set
of variation systems. In embodiments, the unified set of adaptive intelligent
systems includes a
set of machine learning systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of event prediction systems. In embodiments, the unified set of
adaptive intelligent
systems includes a set of autonomous control systems.
[0531] In embodiments, the unified set of adaptive intelligent systems
includes a set of robotic
control systems. In embodiments, the unified set of adaptive intelligent
systems includes a set of
robotic process automation systems. In embodiments, the unified set of
adaptive intelligent
systems includes a set of data visualization systems. In embodiments, the
unified set of adaptive
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intelligent systems includes a set of data normalization systems. In
embodiments, the unified set
of adaptive intelligent systems includes a set of data cleansing systems.
[0532] In embodiments, the unified set of adaptive intelligent systems
includes a set of data
deduplication systems. In embodiments, the unified set of adaptive intelligent
systems includes a
set of graph-based data storage systems. In embodiments, the unified set of
adaptive intelligent
systems includes a set of intelligent agent systems. In embodiments, the
unified set of adaptive
intelligent systems includes a set of object-oriented data storage systems.
[0533] In embodiments, the unified set of adaptive intelligent systems
includes a set of self-
configuration systems. In embodiments, the unified set of adaptive intelligent
systems includes a
set of self-healing systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of self-organizing systems. In embodiments, the unified set of
adaptive intelligent
systems includes a set of self-organizing map systems.
[0534] In embodiments, the unified set of adaptive intelligent systems
includes a set of cost-
based routing systems. In embodiments, the unified set of adaptive intelligent
systems includes a
set of handshake negotiation systems. In embodiments, the unified set of
adaptive intelligent
systems includes a set of entity discovery systems. In embodiments, the
unified set of adaptive
intelligent systems includes a set of cybersecurity systems.
[0535] In embodiments, the unified set of adaptive intelligent systems
includes a set of
biometric systems. In embodiments, the unified set of adaptive intelligent
systems includes a set
of natural language processing systems. In embodiments, the unified set of
adaptive intelligent
systems includes a set of speech processing systems. In embodiments, the
unified set of adaptive
intelligent systems includes a set of voice recognition systems.
[0536] In embodiments, the unified set of adaptive intelligent systems
includes a set of sound
processing systems. In embodiments, the unified set of adaptive intelligent
systems includes a set
of ultrasound processing systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of artificial intelligence systems. In embodiments, the unified
set of adaptive
intelligent systems includes a set of rules engine systems.
[0537] In embodiments, the unified set of adaptive intelligent systems
includes a set of
workflow automation systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of opportunity discovery systems. In embodiments, the unified
set of adaptive
intelligent systems includes a set of physical modeling systems. In
embodiments, the unified set
of adaptive intelligent systems includes a set of testing systems.
[0538] In embodiments, the unified set of adaptive intelligent systems
includes a set of
diagnostic systems. In embodiments, the unified set of adaptive intelligent
systems includes a set
of software image propagation systems. In embodiments, the unified set of
adaptive intelligent
systems includes a set of peer-to-peer network configuration systems. In
embodiments, the
unified set of adaptive intelligent systems includes a set of RF spectrum
management systems.
[0539] In embodiments, the unified set of adaptive intelligent systems
includes a set of network
resource management systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of storage management systems. In embodiments, the unified set
of adaptive
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intelligent systems includes a set of data management systems. In embodiments,
the unified set of
adaptive intelligent systems includes a set of intrusion detection systems.
[0540] In embodiments, the unified set of adaptive intelligent systems
includes a set of firewall
systems. In embodiments, the unified set of adaptive intelligent systems
includes a set of
virtualization systems. In embodiments, the unified set of adaptive
intelligent systems includes a
set of digital twin systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of Internet of Things monitoring systems.
[0541] In embodiments, the unified set of adaptive intelligent systems
includes a set of routing
systems. In embodiments, the unified set of adaptive intelligent systems
includes a set of
switching systems. In embodiments, the unified set of adaptive intelligent
systems includes a set
of indoor location systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of geolocation systems.
[0542] In embodiments, the unified set of adaptive intelligent systems
includes a set of parsing
systems. In embodiments, the unified set of adaptive intelligent systems
includes a set of
semantic filtering systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of machine vision systems. In embodiments, the unified set of
adaptive intelligent
systems includes a set of fuzzy logic systems.
[0543] In embodiments, the unified set of adaptive intelligent systems
includes a set of
recommendation systems. In embodiments, the unified set of adaptive
intelligent systems
includes a set of dialog management systems. In embodiments, the set of
interfaces includes a
demand management interface and a supply chain management interface. In
embodiments, the
interface is a user interface for a command center dashboard by which an
enterprise orchestrates
a set of value chain entities related to a type of product.
[0544] In embodiments, the interface is a user interface of a local management
system located
in an environment that hosts a set of value chain entities. In embodiments,
the local management
system user interface facilitates configuration of a set of network
connections for the adaptive
intelligence systems. In embodiments, the local management system user
interface facilitates
configuration of a set of data storage resources for the adaptive intelligence
systems. In
embodiments, the local management system user interface facilitates
configuration of a set of
data integration capabilities for the adaptive intelligence systems.
[0545] In embodiments, the local management system user interface facilitates
configuration of
a set of machine learning input resources for the adaptive intelligence
systems. In embodiments,
the local management system user interface facilitates configuration of a set
of power resources
that support the adaptive intelligence systems. In embodiments, the local
management system
user interface facilitates configuration of a set of workflows that are
managed by the adaptive
intelligence systems.
[0546] In embodiments, the interface is a user interface of a mobile computing
device that has a
network connection to the adaptive intelligence systems.
[0547] In embodiments, the interface is an application programming interface.
In embodiments,
the application programming interface facilitates exchange of data between the
adaptive
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intelligence systems and a cloud-based artificial intelligence system. In
embodiments, the
application programming interface facilitates exchange of data between the
adaptive intelligence
systems and a real-time operating system of a cloud data management platform.
[0548] In embodiments, the application programming interface facilitates
exchange of data
between the adaptive intelligence systems and a computational facility of a
cloud data
management platform.
[0549] In embodiments, the application programming interface facilitates
exchange of data
between the adaptive intelligence systems and a set of environmental sensors
that collect data
about an environment that hosts a set of value chain network entities. In
embodiments, the
.. application programming interface facilitates exchange of data between the
adaptive intelligence
systems and a set of sensors that collect data about a product.
[0550] In embodiments, the application programming interface facilitates
exchange of data
between the adaptive intelligence systems and a set of sensors that collect
data published by an
intelligent product.
[0551] In embodiments, the application programming interface facilitates
exchange of data
between the adaptive intelligence systems and a set of sensors that collect
data published by a set
of Internet of Things systems that are disposed in an environment that hosts a
set of value chain
network entities.
[0552] In embodiments, the set of demand management applications, supply chain
applications,
intelligent product applications and enterprise resource management
applications may include,
any of the applications mentioned throughout this disclosure or the documents
incorporated
herein by reference.
[0553] In embodiments, the adaptive intelligent systems layer 614 is
configured to train and
deploy artificial intelligence systems to perform value-chain related tasks.
For example, the
adaptive intelligent systems layer 614 may be leveraged to manage a container
fleet, design a
logistics system, control one or more aspects of a logistics system, select
packaging attributes of
packages in the value chain, design a process to meet regulatory requirements,
automate
processes to mitigate waste production (e.g., solid waste or waste water),
and/or other suitable
tasks related to the value-chain.
[0554] In some of these embodiments, one or more digital twins may be
leveraged by the
adaptive intelligent systems layer 614. A digital twin may refer to a digital
representation of a
physical object (e.g., an asset, a device, a product, a package, a container,
a vehicle, a ship, or the
like), an environment (e.g., a facility), an individual (e.g., a customer or
worker), or other entity
(including any of the value chain network entities 652 described herein), or
combination thereof.
Further examples of physical assets include containers (e.g., boxes, shipping
containers, boxes,
palates, barrels, and the like), goods/products (e.g., widgets, food,
household products, toys,
clothing, water, gas, oil, equipment, and the like), components (e.g., chips,
boards, screens,
chipsets, wires, cables, cards, memory, software components, firmware, parts,
connectors,
housings, and the like), furniture (e.g., tables, counters, workstations,
shelving, etc.), and the like.
Examples of devices include computers, sensors, vehicles (e.g., cars, trucks,
tankers, trains,
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forklifts, cranes, and the like), equipment, conveyer belts, and the like.
Examples of
environments may include facilities (e.g., factories, refineries, warehouses,
retail locations,
storage buildings, parking lots, airports, commercial buildings, residential
buildings, and the
like), roads, water ways, cities, countries, land masses, and the like.
Examples of different types
of physical assets, devices, and environments are referenced throughout the
disclosure.
[0555] In embodiments, a digital twin may be comprised of (e.g., via
reference, or by partial or
complete integration) other digital twins. For example, a digital twin of a
package may include a
digital twin of a container and one or more digital twins of one or more
respective goods
enclosed within the container. Taking this example one step further, one or
more digital twins of
the packages may be contained in a digital twin of a vehicle traversing a
digital twin of a road or
may be positioned on a digital twin of a shelf within a digital twin of a
warehouse, which would
include digital twins of other physical assets and devices.
[0556] In embodiments, the digital representation for a digital twin may
include a set of data
structures (e.g., classes of objects) that collectively define a set of
properties, attributes, and/or
parameters of a represented physical asset, device, or environment, possible
behaviors or
activities thereof and/or possible states or conditions thereof, among other
things. For example, a
set of properties of a physical asset may include a type of the physical
asset, the shape and/or
dimensions of the asset, the mass of the asset, the density of the asset, the
material(s) of the asset,
the physical properties of the material(s), the chemical properties of the
asset, the expected
lifetime of the asset, the surface of the physical asset, a price of the
physical asset, the status of
the physical asset, a location of the physical asset, and/or other properties,
as well as identifiers
of other digital twins contained within or linked to the digital twin and/or
other relevant data
sources that may be used to populate the digital twin (such as data sources
within the
management platform described herein or external data sources, such as
environmental data
sources that may impact properties represented in the digital twin (e.g.,
where ambient air
pressure or temperature affects the physical dimensions of an asset that
inflates or deflates).
Examples of a behavior of a physical asset may include a state of matter of
the physical asset
(e.g., a solid, liquid, plasma or gas), a melting point of the physical asset,
a density of the
physical asset when in a liquid state, a viscosity of the physical asset when
in a liquid state, a
freezing point of the physical asset, a density of the physical asset when in
a solid state, a
hardness of the physical asset when in a solid state, the malleability of the
physical asset, the
buoyancy of the physical asset, the conductivity of the physical asset,
electromagnetic properties
of the physical asset, radiation properties, optical properties (e.g.,
reflectivity, transparency,
opacity, albedo, and the like), wave interaction properties (e.g.,
transparency or opacity to radio
waves, reflection properties, shielding properties, or the like), a burning
point of the physical
asset, the manner by which humidity affects the physical asset, the manner by
which water or
other liquids affect the physical asset, and the like. In another example, the
set of properties of a
device may include a type of the device, the dimensions of the device, the
mass of the device, the
density of the density of the device, the material(s) of the device, the
physical properties of the
material(s), the surface of the device, the output of the device, the status
of the device, a location
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of the device, a trajectory of the device, identifiers of other digital twins
that the device is
connected to and/or contains, and the like. Examples of the behaviors of a
device may include a
maximum acceleration of a device, a maximum speed of a device, possible
motions of a device,
possible configurations of the device, operating modes of the device, a
heating profile of a
device, a cooling profile of a device, processes that are performed by the
device, operations that
are performed by the device, and the like. Example properties of an
environment may include the
dimensions of the environment, environmental air pressure, the temperature of
the environment,
the humidity of the environment, the airflow of the environment, the physical
objects in the
environment, currents of the environment (if a body of water), and the like.
Examples of
behaviors of an environment may include scientific laws that govern the
environment, processes
that are performed in the environment, rules or regulations that must be
adhered to in the
environment, and the like.
[0557] In embodiments, the properties of a digital twin may be adjusted. For
example, the
temperature of a digital twin, a humidity of a digital twin, the shape of a
digital twin, the material
of a digital twin, the dimensions of a digital twin, or any other suitable
parameters may be
adjusted to conform to current status data and/or to a predicted status of a
corresponding entity.
[0558] In embodiments, a digital twin may be rendered by a computing device,
such that a
human user can view a digital representation of a set of physical assets,
devices, or other entities,
and/or an environment thereof. For example, the digital twin may be rendered
and provided as an
output, or may provide an output, to a display device. In some embodiments,
the digital twin may
be rendered and output in an augmented reality and/or virtual reality display.
For example, a user
may view a 3D rendering of an environment (e.g., using monitor or a virtual
reality headset).
While doing so, the user may inspect digital twins of physical assets or
devices in the
environment. In embodiments, a user may view processes being performed with
respect to one or
more digital twins (e.g., inventorying, loading, packing, shipping, and the
like). In embodiments,
a user may provide input that controls one or more properties of a digital
twin via a graphical
user interface.
[0559] In some embodiments, the adaptive intelligent systems layer 614 is
configured to
execute simulations using the digital twin. For example, the adaptive
intelligent systems layer
614 may iteratively adjust one or more parameters of a digital twin and/or one
or more embedded
digital twins. In embodiments, the adaptive intelligent systems layer 614 may,
for each set of
parameters, execute a simulation based on the set of parameters and may
collect the simulation
outcome data resulting from the simulation. Put another way, the adaptive
intelligent systems
layer 614 may collect the properties of the digital twin and the digital twins
within or containing
the digital twin used during the simulation as well as any outcomes stemming
from the
simulation. For example, in running a simulation on a digital twin of a
shipping container, the
adaptive intelligent systems layer 614 can vary the materials of the shipping
container and can
execute simulations that outcomes resulting from different combinations. In
this example, an
outcome can be whether the goods contained in the shipping container arrive to
a destination
undamaged. During the simulation, the adaptive intelligent systems layer 614
may vary the
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external temperatures of the container (e.g., a temperature property of the
digital twin of an
environment of the container may be adjusted between simulations or during a
simulation), the
dimensions of the container, the products inside (represented by digital twins
of the products) the
container, the motion of the container, the humidity inside the container,
and/or any other
properties of the container, the environment, and/or the contents in the
container. For each
simulation instance, the adaptive intelligent systems layer 614 may record the
parameters used to
perform the simulation instance and the outcome of the simulation instance. In
embodiments,
each digital twin may include, reference, or be linked to a set of physical
limitations that define
the boundary conditions for a simulation. For example, the physical
limitations of a digital twin
of an outdoor environment may include a gravity constant (e.g., 9.8 m/s2), a
maximum
temperature (e.g., 60 degrees Celsius), a minimum temperature (e.g., -80
degrees Celsius), a
maximum humidity (e.g., 110% humidity), friction coefficients of surfaces,
maximum velocities
of objects, maximum salinity of water, maximum acidity of water, minimum
acidity of water.
Additionally or alternatively, the simulations may adhere to scientific
formulas, such as ones
reflecting principles or laws of physics, chemistry, materials science,
biology, geometry, or the
like. For example, a simulation of the physical behavior of an object may
adhere to the laws of
thermodynamics, laws of motion, laws of fluid dynamics, laws of buoyancy, laws
of heat
transfer, laws of cooling, and the like. Thus, when the adaptive intelligent
systems layer 614
performs a simulation, the simulation may conform to the physical limitations
and scientific
laws, such that the outcomes of the simulations mimic real world outcomes. The
outcome from a
simulation can be presented to a human user, compared against real world data
(e.g., measured
properties of a container, the environment of the container, the contents of
the container, and
resultant outcomes) to ensure convergence of the digital twin with the real
world, and/or used to
train machine learning models.
[0560] Fig. 38 illustrates example embodiments of a system for controlling
and/or making
decisions, predictions, and/or classification on behalf of a value chain
system 2030. In
embodiments, an artificial intelligence system 2010 leverages one or more
machine-learned
models 2004 to perform value chain-related tasks on behalf of the value chain
system 2030
and/or to make decisions, classifications, and/or predictions on behalf of the
value chain system
2030. In some embodiments, a machine learning system 2002 trains the machine
learned models
2004 based on training data 2062, outcome data 2060, and/or simulation data
2022. As used
herein, the term machine-learned model may refer to any suitable type of model
that is learned in
a supervised, unsupervised, or hybrid manner. Examples of machine-learned
models include
neural networks (e.g., deep neural networks, convolution neural networks, and
many others),
regression based models, decision trees, hidden forests, Hidden Markov models,
Bayesian
models, and the like. In embodiments, the artificial intelligence system 2010
and/or the value
chain system 2030 may provide outcome data 2060 to the machine-learning system
2002 that
relates to a determination (e.g., decision, classification, prediction) made
by the artificial
intelligence system 2010 based in part on the one or more machine-learned
models and the input
to those models. The machine learning system may in-turn reinforce/retrain the
machine-learned
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models 2004 based on the feedback. Furthermore, in embodiments, the machine-
learning system
2002 may train the machine-learning models based on simulation data 2022
generated by the
digital twin simulation system 2020. In these embodiments, the digital twin
simulation system
2020 may be instructed to run specific simulations using one or more digital
twins that represent
objects and/or environments that are managed, maintained, and/or monitored by
the value chain
system. In this way, the digital twin simulation system 2020 may provide
richer data sets that the
machine-learning system 2002 may use to train/reinforce the machine-learned
models.
Additionally or alternatively, the digital twin simulation system 2020 may be
leveraged by the
artificial intelligence system 2010 to test a decision made by the artificial
intelligence system
2010 before providing the decision to the value chain entity.
[0561] In the illustrated example, a machine learning system 2002 may receive
training data
2062, outcome data 2060, and/or simulation data 2022. In embodiments, the
training data may be
data that is used to initially train a model. The training data may be
provided by a domain expert,
collected from various data sources, and/or obtained from historical records
and/or scientific
experimentation. The training data 2062 may include quantified properties of
an item or
environment and outcomes relating from the quantified properties. In some
embodiments, the
training data may be structured in n-tuples, whereby each tuple includes an
outcome and a
respective set of properties relating to the outcome. In embodiments, the
outcome data 2060
includes real world data (e.g., data measured or captured from one or more of
IoT sensors, value
chain entities, and/or other sources). The outcome data may include an outcome
and properties
relating to the outcome. Outcome data may be provided by the value chain
system 2030
leveraging the artificial intelligence system 2010 and/or other data sources
during operation of
the value chain entity system 2010. Each time an outcome is realized (whether
negative or
positive), the value chain entity system 2010, the artificial intelligence
system 2010, as well as
any other data source 2050, may output data relating to the outcome to the
machine learning
system 2002. In embodiments, this data may be provided to the machine-learning
system via an
API of the adaptive intelligent systems layer 614. Furthermore, in
embodiments, the adaptive
intelligent systems layer 614 may obtain data from other types of external
data sources that are
not necessarily a value chain entity but may provide insightful data. For
example, weather data,
stock market data, news events, and the like may be collected, crawled,
subscribed to, or the like
to supplement the outcome data (and/or training data and/or simulation data).
[0562] In some embodiments, the machine learning system 2002 may receive
simulation data
2022 from the digital twin simulation system 2020. Simulation data 2022 may be
any data
relating to a simulation using a digital twin. Simulation data 2022 may be
similar to outcome data
2060, but the results are simulated results from an executed simulation rather
than real-world
data. In embodiments, simulation data 2022 may include the properties of the
digital twin and
any other digital twins that were used to perform the simulation and the
outcomes stemming
therefrom. In embodiments, the digital twin simulation system 2020 may
iteratively adjust the
properties of a digital twin, as well as other digital twins that are
contained or contain the digital
twin. During each iteration, the digital twin simulation system 2020 may
provide the properties
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of the simulation (e.g., the properties of all the digital twins involved in
the simulation) to the
artificial intelligence system 2010, which then outputs predictions,
classifications, or any other
decisions to the digital twin simulation system 2020. The digital twin
simulation system 2020
may use the decisions from the artificial intelligence system 2010 to execute
the simulation
(which may result in a series of decisions stemming from a state change in the
simulation). At
each iteration, the digital twin simulation system 2020 may output the
properties used to run the
simulation to the machine learning system 2002, any decisions from the
artificial intelligence
system 2010 used by the digital twin simulation system 2020, and outcomes from
the simulation
to the machine learning system 2002, such that the properties, decisions, and
outcomes of the
simulation are used to further train the model(s) used by the artificial
intelligence system during
the simulation.
[0563] In some embodiments, training data, outcome data 2060, and/or
simulation data 2022
may be fed into a data lake (e.g., a Hadoop data lake). The machine learning
system 2002 may
structure the data from the data lake. In embodiments, the machine learning
system 2002 may
train/reinforce the models using the collected data to improve the accuracy of
the models (e.g.,
minimize the error value of the model). The machine learning system may
execute machine-
learning algorithms on the collected data (e.g., training data, outcome data,
and/or simulation
data) to obtain the model. Depending on the type of model, the machine-
learning algorithm will
vary. Examples of learning algorithms/models include (e.g., deep neural
networks, convolution
neural networks, and many others as described throughout this disclosure),
statistical models
(e.g., regression-based models and many others), decision trees and other
decision models,
random/hidden forests, Hidden Markov models, Bayesian models, and the like. In
collecting data
from the digital twin simulation system 2020, the machine-learning system 2002
may train the
model on scenarios not yet encountered by the value chain system 2030. In this
way, the resultant
models will have less "unexplored" feature spaces, which may lead to improved
decisions by the
artificial intelligence system 2010. Furthermore, as digital twins are based
partly on assumptions,
the properties of a digital twin may be updated/corrected when a real-world
behavior differs from
that of the digital twin. Examples are provided below.
[0564] Fig. 39 illustrates an example of a container fleet management system
2070 that
interfaces with the adaptive intelligent systems layer 614. In example
embodiments, a container
fleet management system 2070 may be configured to automate one or more aspects
of the value
chain as it applies to containers and shipping. In embodiments, the container
fleet management
system 2070 may be include one or more software modules that are executed by
one or more
server devices. These software modules may be configured to select containers
to use (e.g., a size
of container, the type of the container, the provider of the container, etc.)
for a set of one or more
shipments, schedule delivery/pickup of container, selection of shipping
routes, determining the
type of storage for a container (e.g., outdoor or indoor), select a location
of each container while
awaiting shipping, manage bills of lading and/or other suitable container
fleet management tasks.
In embodiments, the machine-learning system 2002 trains one or more models
that are leveraged
by the artificial intelligence system 2010 to make classifications,
predictions, and/or other
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decisions relating to container fleet management. In example embodiments, a
model 2004 is
trained to select types of containers given one or more task-related features
to maximize the
likelihood of a desired outcome (e.g., that the contents of the container
arrive in a timely manner
with minimal loss at the lowest possible cost). As such, the machine-learning
system 2002 may
train the models using n-tuples that include the task-related features
pertaining to a particular
event and one or more outcomes associated with the particular event. In this
example, task-
related features for a particular event (e.g., a shipment) may include, but
are not limited to, the
type of container used, the contents of the container, properties of the
container contents (e.g.,
cost, perishability, temperature restrictions, and the like), the source and
destination of the
container, whether the container is being shipped via truck, rail, or ship,
the time of year, the cost
of each container, and/or other relevant features. In this example, outcomes
relating to the
particular event may include whether the contents arrived safely, replacement
costs (if any)
associated with any damage or loss, total shipping time, and/or total cost of
shipment (e.g., how
much it cost to ship container). Furthermore, as international and/or
interstate logistics may
include many different sources, destinations, contents, weather conditions,
and the like,
simulations that simulate different shipping events may be run to richen the
data used to train the
model. For instance, simulations may be run for different combinations of
ports and/or train
depots for different combinations of sources, destinations, products, and
times of year. In this
example, different digital twins may be generated to represent the different
combinations (e.g.,
digital twins of products, containers, and shipping-related environments),
whereby one or more
properties of the digital twins are varied for different simulations and the
outcomes of each
simulation may be recorded in a tuple with the proprieties. In this way, the
model may be trained
on certain combinations of routes, contents, time of year, container type,
and/or cost that may not
have been previously encountered in the real-world outcome data. Other
examples of training a
container fleet management model may include a model that is trained to
determine where a
container should be stored in a storage facility (e.g., where in a stack,
indoors or outdoors, and/or
the like) given the contents of the container, when the container needs to be
moved, the type of
container, the location, the time of year, and the like.
[0565] In operation, the artificial intelligence system 2010 may use the above-
discussed models
2004 to make container fleet management decisions on behalf of a container
fleet management
system 2070 given one or more features relating to a task or event. For
example, the artificial
intelligence system 2010 may select a type of container (e.g., materials of
the container, the
dimensions of the container, the brand of the container, and the like) to use
for a particular
shipment. In this example, the container fleet management system 2070 may
provide the features
of an upcoming shipment to the artificial intelligence system 2010. These
features may include
what is being shipped (e.g., the type(s) of goods in the shipment), the size
of the shipment, the
source and destination, the date when the shipment is to be sent off, and/or
the desired date or
range of dates for delivery. In embodiments, the artificial intelligence
system 2010 may feed
these features into one or more of the models discussed above to obtain one or
more decisions.
These decisions may include which type of container to use and/or which
shipping routes to use,
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whereby the decisions may be selected to minimize overall shipping costs
(e.g., costs for
container and transit + any replacement costs). The container fleet management
system 2070 may
then initiate the shipping event using the decision(s) made by the artificial
intelligence system
2010. Furthermore, after the shipping event, the outcomes of the event (e.g.,
total shipping time,
any reported damages or loss, replacement costs, total costs) may be reported
to the machine-
learning system 2002 to reinforce the models used to make the decisions.
Furthermore, in some
embodiments, the output of the container fleet management system 2070 and/or
the other value
chain entity data sources 2050 may be used to update one or more properties of
one or more
digital twins via the digital twin system 2020.
[0566] Fig. 40 illustrates an example of a logistics design system that
interfaces with the
adaptive intelligent systems layer 614. In embodiments, a logistics design
system may be
configured to design one or more aspects of a logistics solution. For example,
the logistics design
system may be configured to receive one or more logistics factors (e.g., from
a user via a GUI).
In embodiments, logistics factors may include one or more present conditions,
historical
conditions, or future conditions of an organization (or potential
organization) that are relevant to
forming a logistics solution. Examples of logistics factors may include, but
are not limited to the
type(s) of products being produced/farmed/shipped, features of those products
(e.g., dimensions,
weights, shipping requirements, shelf life, etc.), locations of manufacturing
sites, locations of
distribution facilities, locations of warehouses, locations of customer bases,
market penetration in
certain areas, expansion locations, supply chain features (e.g., required
parts/supplies/resources,
suppliers, supplier locations, buyers, buyer locations), and/or the like) and
may determine one or
more design recommendations based on the factors. Examples of design
recommendations may
include supply chain recommendations (e.g., proposed suppliers (e.g., resource
or parts
suppliers), implementations of a smart inventory systems that order on-demand
parts from
available suppliers, and the like), storage and transport recommendations
(e.g., proposed shipping
routes, proposed shipping types (e.g., air, freight, truck, ship), proposed
storage development
(e.g., locations and/or dimensions of new warehouses), infrastructure
recommendations (e.g.,
updates to machinery, adding cooled storage, adding heated storage, or the
like), and
combinations thereof. In embodiments, the logistics design system determines
the
recommendations to optimize an outcome. Examples of outcomes can include
manufacturing
times, manufacturing costs, shipping times, shipping costs, loss rate,
environmental impact,
compliance to a set of rules/regulations, and the like. Examples of
optimizations include
increased production throughput, reduced production costs, reduced shipping
costs, decreased
shipping times, reduced carbon footprint, and combinations thereof.
[0567] In embodiments, the logistics design system may interface with the
artificial intelligence
system 2010 to provide the logistics factors and to receive design
recommendations that are
based thereon. In embodiments, the artificial intelligence system 2010 may
leverage one or more
machine-learned models 2004 (e.g., logistics design recommendations models) to
determine a
recommendation. As will be discussed, a logistics design recommendation model
may be trained
to optimize one or more outcomes given a set of logistics factors. For
example, a logistics design
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recommendation model trained to design supply chains may identify a set of
suppliers that can
supply a given manufacturer, the location of the manufacturer, the supplies
needed, and/or other
factors. The set of suppliers may then be used to implement an on-demand
supply side inventory.
In another example, the logistics design recommendation may take the same
features of another
manufacturer and recommend the purchase and use of one or more 3D printers.
[0568] In embodiments, the artificial intelligence system 2010 may leverage
the digital twin
system 2020 to generate a digital twin of a logistics system that implements
the logistics design
recommendation (and, in some embodiments, alternative systems that implement
other design
recommendations). In these embodiments, the digital twin system 2010 may
receive the design
recommendations and may generate a digital twin of a logistics environment
that mirrors the
recommendations. In embodiments, the artificial intelligence system 2010 may
leverage the
digital twin of the logistics environment to run simulations on the proposed
solution. In
embodiments, the digital twin system 2010 may display the digital twin of the
logistics
environment to a user via a display device (e.g., a monitor or a VR headset).
In embodiments, the
.. user may view the simulations in the digital twin. Furthermore, in
embodiments, the digital twin
system 2010 may provide a graphical user interface that the user may interact
with to adjust the
design of the logistics environment to adjust the design. The design provided
(at least in part) by
a user may also be represented in a digital twin of a logistics environment,
whereby the digital
twin system 2020 may perform simulations using the digital twin.
[0569] In some embodiments, the simulations run by the digital twin system
2010 may be used
to train the recommendation models. Furthermore, when the design
recommendations are
implemented by an organization, the logistics system of the organization may
be configured to
report (e.g., via sensors, computing devices, manual human input) outcome data
corresponding to
the design recommendations to the machine learning system 2002, which may use
the outcome
data to reinforce the logistics design recommendation models.
[0570] Fig. 41 illustrates an example of a packaging design system that
interfaces with the
adaptive intelligent systems layer 614. In embodiments, the packaging design
system may be
configured to design one or more aspects of packaging for a physical object
being conveyed in
the value chain network. In some embodiments, the packaging design system may
select one or
more packaging attributes (e.g., size, material, padding, etc.) of the
packaging to optimize one or
more outcomes associated with the transport of the physical object. For
example, the packaging
attributes may be selected to reduce costs, decrease loss/damage, decrease
weight, decrease
plastic or other non-biodegradable waste, or the like. In embodiments, the
packaging design
system leverages the artificial intelligence system 2010 to obtain packaging
attribute
recommendations. In embodiments, the packaging design system may provide one
or more
features of the physical object. In embodiments, the features of the physical
object may include
the dimensions of the physical object, the mass of the physical object, the
source of the physical
object, one or more potential destinations of the physical object, the manner
by which the
physical object is shipped, and the like. In embodiments, the packaging design
system may
further provide one or more optimization goals for the package design (e.g.,
reduce cost, reduce
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damage, reduce environmental impact). In response, the artificial intelligence
system 2010 may
determine one or more recommended packaging attributes based on the physical
asset features
and the given objective. In embodiments, the packaging design system receives
the packaging
attributes and generates a package design based thereon. The package design
may include a
material to be used, the external dimensions of the packaging, the internal
dimensions of the
packaging, the shape of the packaging, the padding/stuffing for the packaging,
and the like.
[0571] In some embodiments, the packaging design system may provide a
packaging design to
the digital twin system 2020, which generates a digital twin of the packaging
and physical asset
based on the packaging design. The digital twin of the packaging and physical
asset may be used
to run simulations that test the packaging (e.g., whether the packaging holds
up in shipping,
whether the packaging provides adequate insulation/padding, and the like). In
embodiments, the
results of the simulation may be returned to the packaging design system,
which may output the
results to a user. In embodiments, the user may accept the packaging design,
may adjust the
packaging design, or may reject the design. In some embodiments, the digital
twin system may
run simulations on one or more digital twins to test different conditions that
the package may be
subjected to (e.g., outside in the snow, rocking in a boat, being moved by a
forklift, or the like).
In some embodiments, the digital twin system may output the results of a
simulation to the
machine-learning system 2002, which can train/reinforce the packaging design
models based on
the properties used to run the simulation and the outcomes stemming therefrom.
[0572] In embodiments, the machine-learning system 2002 may receive outcome
data from the
packaging design system and/or other value chain entity data sources (e.g.,
smart warehouses,
user feedback, and the like). The machine-learning system 2002 may use this
outcome data to
train/reinforce the packaging design models. Furthermore, in some embodiments,
the outcome
data may be used by the digital twin system 2020 to update/correct any
incorrect assumptions
used by the digital twin system (e.g., the flexibility of a packaging
material, the water resistance
of a packaging material, and the like).
[0573] Fig. 42 illustrates examples of a waste mitigation system that
interfaces with the
adaptive intelligent systems layer 614. In embodiments, the waste mitigation
system is
configured to analyze a process within the value chain (e.g., manufacturing of
a product, oil
refining, fertilization, water treatment, or the like) to mitigate waste
(e.g., solid waste,
wastewater, discarded packaging, wasted energy, wasted time, wasted resources,
or other waste).
In embodiments, the waste mitigation system may interface with the artificial
intelligence system
2010 to automate one or more processes to mitigate waste.
[0574] In embodiments, the artificial intelligence system 2010 may provide
control decisions to
the waste mitigation system to mitigate solid waste production. Examples of
waste production
may include excess plastic or other non-biodegradable waste, hazardous or
toxic waste (e.g.,
nuclear waste, petroleum coke, or the like), and the like. In some of these
embodiments, the
artificial intelligence system 2010 may receive one or more features of the
process (or "process
features"). Examples of process features may include, but are not limited to,
the steps in the
process, the materials being used, the properties of the materials being used,
and the like. The
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artificial intelligence system 2010 may leverage one or more machine-learned
models to control
the process. In embodiments, the machine-learned models may be trained to
classify a waste
condition and/or the cause of the waste condition. In some of these
embodiments, the artificial
intelligence system 2010 may determine or select a waste mitigation solution
based on the
classified waste condition. For example, in some embodiments, the artificial
intelligence system
2010 may apply rules-based logic to determine an adjustment to make to the
process to reduce or
resolve the waste condition. Additionally, or alternatively, the artificial
intelligence may leverage
a model that recommends an adjustment to make to the process to reduce or
resolve the waste
condition.
.. [0575] In embodiments, the artificial intelligence system 2010 may leverage
the digital twin
system 2020 to mitigate the waste produced by a process. In embodiments, the
digital twin
system 2020 may execute iterative simulations of the process in a digital twin
of the environment
in which the process is performed. When the simulation is executed, the
artificial intelligence
system 2010 may monitor the results of the simulation to determine a waste
condition and/or the
.. cause of the waste condition. During the simulations, the artificial
intelligence system 2010 may
adjust one or more aspects of the process to determine whether the adjustments
mitigated the
waste condition, worsened the waste condition, or had no effect. When an
adjustment is found to
mitigate the waste condition, the artificial intelligence system 2010 may
adjust other aspects of
the process to determine if an improvement can be realized. In embodiments,
the artificial
intelligence system 2010 may perform a genetic algorithm when iteratively
adjusting the aspects
of the process in the digital twin simulations. In these embodiments, the
artificial intelligence
system 2010 may identify aspects of the process that can be adjusted to
mitigate the waste
production.
[0576] Smart Project Management Facilities
[0577] Referring to Fig. 43, an embodiment of the platform 604 is provided. As
with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 624, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 614 (including artificial
intelligence 1160), a set of data
storage facilities or systems 624, and a set of monitoring facilities or
systems 614. The platform
604 may support a set of applications 630 (including processes, workflows,
activities, events, use
cases and applications) for enabling an enterprise to manage a set of value
chain network entities
652, such as from a point of origin to a point of customer use of a product
650, which may be an
intelligent product.
[0578] In embodiments, the adaptive intelligence systems layer 614 may further
include a set of
automated project management facilities MPVC1102 that provide automated
recommendations
for a set of value chain project management tasks based on processing current
status information,
a set of application outputs and/or a set of outcomes 1040 for a set of demand
management
applications 1502, a set of supply chain applications 1500, a set of
intelligent product
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applications 1510, a set of asset management applications 1530 and a set of
enterprise resource
management applications 1520 for a category of goods.
[0579] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a set of project management facilities that provide
automated
recommendations for a set of value chain project management tasks based on
processing current
status information and a set of outcomes for a set of demand management
applications, a set of
supply chain applications, a set of intelligent product applications and a set
of enterprise resource
management applications for a category of goods.
[0580] In embodiments, the set of project management facilities are configured
to manage a
wide variety of types of projects, such as procurement projects, logistics
projects, reverse
logistics projects, fulfillment projects, distribution projects, warehousing
projects, inventory
management projects, product design projects, product management projects,
shipping projects,
maritime projects, loading or unloading projects, packing projects, purchasing
projects,
marketing projects, sales projects, analytics projects, demand management
projects, demand
planning projects, resource planning projects and many others.
[0581] In embodiments, the project management facilities are configured to
manage a set of
procurement projects. In embodiments, the project management facilities are
configured to
manage a set of logistics projects. In embodiments, the project management
facilities are
configured to manage a set of reverse logistics projects. In embodiments, the
project management
facilities are configured to manage a set of fulfillment projects.
[0582] In embodiments, the project management facilities are configured to
manage a set of
distribution projects. In embodiments, the project management facilities are
configured to
manage a set of warehousing projects. In embodiments, the project management
facilities are
configured to manage a set of inventory management projects. In embodiments,
the project
management facilities are configured to manage a set of product design
projects.
[0583] In embodiments, the project management facilities are configured to
manage a set of
product management projects. In embodiments, the project management facilities
are configured
to manage a set of shipping projects. In embodiments, the project management
facilities are
configured to manage a set of maritime projects. In embodiments, the project
management
facilities are configured to manage a set of loading or unloading projects.
[0584] In embodiments, the project management facilities are configured to
manage a set of
packing projects. In embodiments, the project management facilities are
configured to manage a
set of purchasing projects. In embodiments, the project management facilities
are configured to
manage a set of marketing projects. In embodiments, the project management
facilities are
configured to manage a set of sales projects.
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[0585] In embodiments, the project management facilities are configured to
manage a set of
analytics projects. In embodiments, the project management facilities are
configured to manage a
set of demand management projects. In embodiments, the project management
facilities are
configured to manage a set of demand planning projects. In embodiments, the
project
management facilities are configured to manage a set of resource planning
projects.
[0586] Smart Task Recommendations
[0587] Referring to Fig. 282, an embodiment of the platform 604 is provided.
As with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 624, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 614 (including artificial
intelligence 1160), a set of data
storage facilities or systems 624, and a set of monitoring facilities or
systems 614.
[0588] The platform 604 may support a set of applications 630 (including
processes,
workflows, activities, events, use cases and applications) for enabling an
enterprise to manage a
set of value chain network entities 652, such as from a point of origin to a
point of customer use
of a product 650, which may be an intelligent product.
[0589] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a set of project management facilities that provide
automated
recommendations for a set of value chain project management tasks based on
processing current
status information and a set of outcomes for a set of demand management
applications, a set of
supply chain applications, a set of intelligent product applications and a set
of enterprise resource
management applications for a category of goods.
[0590] In embodiments, the adaptive intelligent systems layer 614 may further
include a set of
process automation facilities 14402 that provide automated recommendations for
a set of value
chain process tasks MPVC1102 that provide automated recommendations for a set
of value chain
processes based on processing current status information, a set of application
outputs and/or a set
of outcomes 1040 for a set of demand management applications 1502, a set of
supply chain
applications 1500, a set of intelligent product applications 1510, a set of
asset management
applications 1530 and a set of enterprise resource management applications
1520 for a category
of goods. In some examples, the process automation facilities 14402 may be
used with basic rule-
based training and recommendations. This may relate to following a set of
rules that an expert
has articulated such as when a trigger occurs, undertake a task. In another
example, the process
automation facilities 14402 may utilize deep learning to observe interactions
such as deep
learning on outcomes to learn to recommend decisions or tasks that produce a
highest return on
investment (ROI) or other outcome-based yield. The process automation
facilities 14402 may be
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used to provide collaborative filtering such as look at a set of experts that
are most similar in
terms of work done and tasks completed being most similar. For example, the
underlying
software may be used to find customers similar to another set of customers to
sell to, make a
different offering to, or change price accordingly. In general, given a set of
underlying pattern
data, contextually, about a customer segment, purchasing patterns may be
determined for that
customer segment such as knowledge of cost and pricing patterns for that
customer. This
information may be used to learn to focus a next set of activities around
pricing, promotion,
demand management towards an ideal that may be based on deep learning or rules
or
collaborative filtering type work trying to leverage off of similar decisions
made by similarly
situated people (e.g., recommending movies to a similar cohort of people).
[0591] In embodiments, the set of facilities that provide automated
recommendations for a set
of value chain process tasks provide recommendations involving a wide range of
types of
activities, such as, without limitation, product configuration activities,
product selection activities
for a customer, supplier selection activities, shipper selection activities,
route selection activities,
factory selection activities, product assortment activities, product
management activities, logistics
activities, reverse logistics activities, artificial intelligence
configuration activities, maintenance
activities, product support activities, product recommendation activities and
many others.
[0592] In embodiments, the automated recommendations relate to a set of
product
configuration activities. In embodiments, the automated recommendations relate
to a set of
product selection activities for a customer. In embodiments, the automated
recommendations
relate to a set of supplier selection activities. In embodiments, the
automated recommendations
relate to a set of shipper selection activities.
[0593] In embodiments, the automated recommendations relate to a set of route
selection
activities. In embodiments, the automated recommendations relate to a set of
factory selection
activities. In embodiments, the automated recommendations relate to a set of
product assortment
activities. In embodiments, the automated recommendations relate to a set of
product
management activities. In embodiments, the automated recommendations relate to
a set of
logistics activities.
[0594] In embodiments, the automated recommendations relate to a set of
reverse logistics
activities. In embodiments, the automated recommendations relate to a set of
artificial
intelligence configuration activities. In embodiments, the automated
recommendations relate to a
set of maintenance activities. In embodiments, the automated recommendations
relate to a set of
product support activities. In embodiments, the automated recommendations
relate to a set of
product recommendation activities.
[0595] Optimized routing among nodes
[0596] Referring to Fig. 44, an embodiment of the platform 604 is provided. As
with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 624, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 614 (including artificial
intelligence 1160), a set of data
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storage facilities or systems 624, and a set of monitoring facilities or
systems 614. The platform
604 may support a set of applications 630 (including processes, workflows,
activities, events, use
cases and applications) for enabling an enterprise to manage a set of value
chain network entities
652, such as from a point of origin to a point of customer use of a product
650, which may be an
intelligent product.
[0597] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform for a value
chain network with a micro-services architecture, a set of interfaces, network
connectivity
facilities, adaptive intelligence facilities, data storage facilities, and
monitoring facilities that are
coordinated for monitoring and management of a set of value chain network
entities; and a set of
applications for enabling an enterprise to manage a set of value chain network
entities from a
point of origin to a point of customer use; wherein a set of routing
facilities generate a set of
routing instructions for routing information among a set of nodes in the value
chain network
based on current status information for the value chain network.
[0598] In embodiments, the adaptive intelligent systems layer 614 may further
include a set of
routing facilities 1720 that generate a set of routing instructions for
routing information among a
set of nodes in the value chain network, such as based on processing current
status information
1730, a set of application outputs and/or a set of outcomes 1040, or other
information collected
by or used in the VCNP 102. Routing may include routing for the benefit of a
set of demand
management applications 1502, a set of supply chain applications 1500, a set
of intelligent
product applications 1510, a set of asset management applications 1530 and a
set of enterprise
resource management applications 1520 for a category of goods.
[0599] In embodiments, the set of routing facilities that generate a set of
routing instructions for
routing information among a set of nodes in the value chain network use a wide
variety of
routing systems or configurations, such as involving, without limitation,
priority-based routing,
master controller routing, least cost routing, rule-based routing, genetically
programmed routing,
random linear network coding routing, traffic-based routing, spectrum-based
routing, RF
condition-based routing, energy-based routing, latency-sensitive routing,
protocol compatibility
based routing, dynamic spectrum access routing, peer-to-peer negotiated
routing, queue-based
routing, and others.
[0600] In embodiments, the routing includes priority-based routing. In
embodiments, the
routing includes master controller routing. In embodiments, the routing
includes least cost
routing. In embodiments, the routing includes rule-based routing. In
embodiments, the routing
includes genetically programmed routing.
[0601] In embodiments, the routing includes random linear network coding
routing. In
embodiments, the routing includes traffic-based routing. In embodiments, the
routing includes
spectrum-based routing.
[0602] In embodiments, the routing includes RF condition-based routing. In
embodiments, the
routing includes energy-based routing. In embodiments, the routing includes
latency-sensitive
routing.
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[0603] In embodiments, the routing includes protocol compatibility-based
routing.
[0604] In embodiments, the routing includes dynamic spectrum access routing.
In
embodiments, the routing includes peer-to-peer negotiated routing. In
embodiments, the routing
includes queue-based routing.
[0605] In embodiments, the status information for the value chain network
involves a wide
range of states, events, workflows, activities, occurrences, or the like, such
as, without limitation,
traffic status, congestion status, bandwidth status, operating status,
workflow progress status,
incident status, damage status, safety status, power availability status,
worker status, data
availability status, predicted system status, shipment location status,
shipment timing status,
delivery status, anticipated delivery status, environmental condition status,
system diagnostic
status, system fault status, cybersecurity status, compliance status, demand
status, supply status,
price status, volatility status, need status, interest status, aggregate
status for a group or
population, individual status, and many others.
[0606] In embodiments, the status information involves traffic status. In
embodiments, the
status information involves congestion status. In embodiments, the status
information involves
bandwidth status. In embodiments, the status information involves operating
status. In
embodiments, the status information involves workflow progress status.
[0607] In embodiments, the status information involves incident status. In
embodiments, the
status information involves damage status. In embodiments, the status
information involves
safety status.
[0608] In embodiments, the status information involves power availability
status. In
embodiments, the status information involves worker status. In embodiments,
the status
information involves data availability status.
[0609] In embodiments, the status information involves predicted system
status. In
embodiments, the status information involves shipment location status. In
embodiments, the
status information involves shipment timing status. In embodiments, the status
information
involves delivery status.
[0610] In embodiments, the status information involves anticipated delivery
status. In
embodiments, the status information involves environmental condition status.
[0611] In embodiments, the status information involves system diagnostic
status. In
embodiments, the status information involves system fault status. In
embodiments, the status
information involves cybersecurity status. In embodiments, the status
information involves
compliance status.
DASHBOARD FOR MANAGING DIGITAL TWINS
.. [0612] Referring to Fig. 14, an embodiment of the platform 604 is provided.
As with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 624, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 614 (including artificial
intelligence 1160), a set of data
storage facilities or systems 624, and a set of monitoring facilities or
systems 614. The platform
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604 may support a set of applications 630 (including processes, workflows,
activities, events, use
cases and applications) for enabling an enterprise to manage a set of value
chain network entities
652, such as from a point of origin to a point of customer use of a product
650, which may be an
intelligent product.
[0613] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a dashboard for managing a set of digital twins,
wherein at least one
digital twin represents a set of supply chain entities, workflows and assets
and at least one other
digital twin represents a set of demand management entities and workflows.
[0614] In embodiments, the VCNP 604 may further include a dashboard 1740 for
managing a
set of digital twins 1700. In embodiments, this may include different twins,
such as where one
digital twin 1700 represents a set of supply chain entities, workflows and
assets and another
digital twin 1700 represents a set of demand management entities and
workflows. In some
example embodiments, managing a set of digital twins 1700 may refer to
configuration (e.g., via
the dashboard 1740) as described in the disclosure. For example, the digital
twin 1700 may be
configured through use of a digital twin configuration system to set up and
manage the enterprise
digital twins and associated metadata of an enterprise, to configure the data
structures and data
listening threads that power the enterprise digital twins, and to configure
features of the
enterprise digital twins, including access features, processing features,
automation features,
reporting features, and the like, each of which may be affected by the type of
enterprise digital
twin (e.g., based on the role(s) that it serves, the entities it depicts, the
workflows that it supports
or enables and the like). In example embodiments, the digital twin
configuration system may
receive the types of digital twins that may be supported for the enterprise,
as well as the different
objects, entities, and/or states that are to be depicted in each type of
digital twin. For each type of
digital twin, the digital twin configuration system may determine one or more
data sources and
types of data that feed or otherwise support each objectõ entity, or state
that is depicted in the
respective type of digital twin and may determine any internal or external
software requests (e.g.,
API calls) that obtain the identified data types or other suitable data
acquisitions mechanisms,
such as webhooks, that may configured to automatically receive data from an
internal or external
data source In some embodiments, the digital twin configuration system may
determine internal
and/or external software requests that support the identified data types by
analyzing the
relationships between the different types of data that correspond to a
particular state/entity/object
and the granularity thereof. Additionally or alternatively, a user may define
(e.g., via a GUI) the
data sources and/or software requests and/or other data acquisition mechanisms
that support the
respective data types that are depicted in a respective digital twin. In these
example
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embodiments, the user may indicate the data source that may be accessed and
the types of data to
be obtained from the respective data source.
[0615] The dashboard may be used to configure the digital twins 1700 for use
in collection,
processing, and/or representation of information collected in the platform
604, such as status
information 1730, such as for the benefit of a set of demand management
applications 1502, a set
of supply chain applications 1500, a set of intelligent product applications
1510, a set of asset
management applications 1530 and a set of enterprise resource management
applications 1520
for a category of goods.
[0616] In embodiments, the dashboard for managing a set of digital twins,
wherein at least one
digital twin represents a set of supply chain entities and workflows and at
least one other digital
twin represents a set of demand management entities and workflows.
[0617] In embodiments, the entities and workflows relate to a set of products
of an enterprise.
In embodiments, the entities and workflows relate to a set of suppliers of an
enterprise. In
embodiments, the entities and workflows relate to a set of producers of a set
of products. In
embodiments, the entities and workflows relate to a set of manufacturers of a
set of products.
[0618] In embodiments, the entities and workflows relate to a set of retailers
of a line of
products. In embodiments, the entities and workflows relate to a set of
businesses involved in an
ecosystem for a category of products. In embodiments, the entities and
workflows relate to a set
of owners of a set of assets involved in a value chain for a set of products.
In embodiments, the
entities and workflows relate to a set of operators of a set of assets
involved in a value chain for a
set of products.
[0619] In embodiments, the entities and workflows relate to a set of operating
facilities. In
embodiments, the entities and workflows relate to a set of customers. In
embodiments, the
entities and workflows relate to a set of consumers. In embodiments, the
entities and workflows
relate to a set of workers.
[0620] In embodiments, the entities and workflows relate to a set of mobile
devices. In
embodiments, the entities and workflows relate to a set of wearable devices.
In embodiments, the
entities and workflows relate to a set of distributors. In embodiments, the
entities and workflows
relate to a set of resellers.
[0621] In embodiments, the entities and workflows relate to a set of supply
chain infrastructure
facilities. In embodiments, the entities and workflows relate to a set of
supply chain processes. In
embodiments, the entities and workflows relate to a set of logistics
processes. In embodiments,
the entities and workflows relate to a set of reverse logistics processes.
[0622] In embodiments, the entities and workflows relate to a set of demand
prediction
processes. In embodiments, the entities and workflows relate to a set of
demand management
processes. In embodiments, the entities and workflows relate to a set of
demand aggregation
processes. In embodiments, the entities and workflows relate to a set of
machines.
[0623] In embodiments, the entities and workflows relate to a set of ships. In
embodiments, the
entities and workflows relate to a set of barges. In embodiments, the entities
and workflows
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relate to a set of warehouses. In embodiments, the entities and workflows
relate to a set of
maritime ports.
[0624] In embodiments, the entities and workflows relate to a set of airports.
In embodiments,
the entities and workflows relate to a set of airways. In embodiments, the
entities and workflows
relate to a set of waterways. In embodiments, the entities and workflows
relate to a set of
roadways.
[0625] In embodiments, the entities and workflows relate to a set of railways.
In embodiments,
the entities and workflows relate to a set of bridges. In embodiments, the
entities and workflows
relate to a set of tunnels. In embodiments, the entities and workflows relate
to a set of online
.. retailers.
[0626] In embodiments, the entities and workflows relate to a set of ecommerce
sites. In
embodiments, the entities and workflows relate to a set of demand factors. In
embodiments, the
entities and workflows relate to a set of supply factors. In embodiments, the
entities and
workflows relate to a set of delivery systems.
[0627] In embodiments, the entities and workflows relate to a set of floating
assets. In
embodiments, the entities and workflows relate to a set of points of origin.
In embodiments, the
entities and workflows relate to a set of points of destination. In
embodiments, the entities and
workflows relate to a set of points of storage.
[0628] In embodiments, the entities and workflows relate to a set of points of
product usage. In
embodiments, the entities and workflows relate to a set of networks. In
embodiments, the entities
and workflows relate to a set of information technology systems. In
embodiments, the entities
and workflows relate to a set of software platforms.
[0629] In embodiments, the entities and workflows relate to a set of
distribution centers. In
embodiments, the entities and workflows relate to a set of fulfillment
centers. In embodiments,
the entities and workflows relate to a set of containers. In embodiments, the
entities and
workflows relate to a set of container handling facilities.
[0630] In embodiments, the entities and workflows relate to a set of customs.
In embodiments,
the entities and workflows relate to a set of export control. In embodiments,
the entities and
workflows relate to a set of border control. In embodiments, the entities and
workflows relate to a
set of drones.
[0631] In embodiments, the entities and workflows relate to a set of robots.
In embodiments,
the entities and workflows relate to a set of autonomous vehicles. In
embodiments, the entities
and workflows relate to a set of hauling facilities. In embodiments, the
entities and workflows
relate to a set of drones, robots and autonomous vehicles. In embodiments, the
entities and
.. workflows relate to a set of waterways. In embodiments, the entities and
workflows relate to a set
of port infrastructure facilities.
[0632] In embodiments, the set of digital twins may include, for example and
without
limitation, distribution twins, warehousing twins, port infrastructure twins,
shipping facility
twins, operating facility twins, customer twins, worker twins, wearable device
twins, portable
device twins, mobile device twins, process twins, machine twins, asset twins,
product twins,
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point of origin twins, point of destination twins, supply factor twins,
maritime facility twins,
floating asset twins, shipyard twins, fulfillment twins, delivery system
twins, demand factors
twins, retailer twins, ecommerce twins, online twins, waterway twins, roadway
twins, roadway
twins, railway twins, air facility twins, aircraft twins, ship twins, vehicle
twins, train twins,
autonomous vehicle twins, robotic system twins, drone twins, logistics factor
twins and many
others.
MICROSERVICES ARCHITECTURE
[0633] Referring to Fig. 15, an embodiment of the platform 604 is provided. As
with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 624, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 614, a set of data storage
facilities or systems 624, and a
set of monitoring facilities or systems 614. The platform 604 may support a
set of applications
630 (including processes, workflows, activities, events, use cases and
applications) for enabling
an enterprise to manage a set of value chain network entities 652, such as
from a point of origin
to a point of customer use of a product 650, which may be an intelligent
product.
[0634] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
.. intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a set of microservices layers including an
application layer supporting
at least one supply chain application and at least one demand management
application, wherein
the applications of the application layer use a common set of services among a
set of data
processing services, data collection services, and data storage services.
[0635] In embodiments, the VCNP 604 may further include a set of microservices
layers
including an application layer supporting at least two applications among a
set of demand
management applications 1502, a set of supply chain applications 1500, a set
of intelligent
product applications 1510, a set of asset management applications 1530 and a
set of enterprise
resource management applications 1520 for a category of goods.
[0636] A microservices architecture provides several advantages to the
platform 604. For
example, one advantage may be the ability to leverage creation of improved
microservices
created by others such that developer may only need to define inputs and
outputs such that the
platform may use readily adapted services created by others. Also, use of the
microservices
architecture may provide ability to modularize microservices into collections
that may be used to
achieve tasks. For example, a goal to determine what is happening in a
warehouse may be
achieved with a variety of microservices with minimal cost such as vision-
based service, series of
regular prompts that may ask and receive, reading off of event logs or feeds,
and the like. Each
one of these microservices may be a distinct microservice that may be easily
plugged in and
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used. If a particular microservice does not work effectively, the microservice
may be replaced
easily with another service with minimal impact to other components in the
platform. Other
microservices that may be used include recommendation service, collaborative
filtering service,
deep learning with semi-supervised learning service, etc. The microservice
architecture may
provide modularity at each stage in building a full workflow. In an example
embodiment, a
microservice may be built for multiple applications that may be consumed
including shared data
steam and anything else enabled by the microservices architecture.
[0637] IoT Data Collection Architecture Recommendation of other Sensors and
Cameras
[0638] Referring to Fig. 16, an embodiment of the platform 604 is provided. As
with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 614, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 1160, a set of data storage
facilities or systems 624, and
a set of monitoring facilities or systems 614. The platform 604 may support a
set of applications
630 (including processes, workflows, activities, events, use cases and
applications) for enabling
an enterprise to manage a set of value chain network entities 652, such as
from a point of origin
to a point of customer use of a product 650, which may be an intelligent
product.
[0639] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a set of microservices layers including an
application layer supporting
at least one supply chain application and at least one demand management
application, wherein
the microservice layers include a data collection layer that collects
information from a set of
Internet of Things resources that collect information with respect to supply
chain entities and
demand management entities.
[0640] Also provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a machine learning/artificial intelligence system
configured to
generate recommendations for placing an additional sensor/and or camera on
and/or in proximity
to a value chain entity and wherein data from the additional sensor and/or
camera feeds into a
digital twin that represents a set of value chain entities.
[0641] In embodiments, the VCNP 604 may further include a set of
microservices, wherein the
microservice layers include a monitoring systems and data collections systems
layer 614 having
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data collection and management systems 640 that collect information from a set
of Internet of
Things resources 1172 that collect information with respect to supply chain
entities and demand
management entities 652. The microservices may support various applications
among a set of
demand management applications 1502, a set of supply chain applications 1500,
a set of
intelligent product applications 1510, a set of asset management applications
1530 and a set of
enterprise resource management applications 1520 for a category of goods.
[0642] In embodiments, the platform 604 may further include a machine
learning/artificial
intelligence system 1160 that includes a sensor recommendation system 1750
that is configured
to generate recommendations for placing an additional sensor 1462 and/or
camera on and/or in
proximity to a value chain network entity 652. For example, in some
embodiments, the sensor
recommendation system 1750 may generate recommendations by using load, array
of signals,
emergent situations, frequency response, maintenance, diagnosis, etc. Data
from the additional
sensor 1462 and/or camera may feed into a digital twin 1700 that represents a
set of value chain
entities 652.In embodiments, the set of Internet of Things resources that
collect information with
respect to supply chain entities and demand management entities collects
information from
entities of any of the types described throughout this disclosure and in the
documents
incorporated by reference herein.
[0643] In embodiments, the set of Internet of Things resources may be of a
wide variety of
types such as, without limitation, camera systems, lighting systems, motion
sensing systems,
weighing systems, inspection systems, machine vision systems, environmental
sensor systems,
onboard sensor systems, onboard diagnostic systems, environmental control
systems, sensor-
enabled network switching and routing systems, RF sensing systems, magnetic
sensing systems,
pressure monitoring systems, vibration monitoring systems, temperature
monitoring systems,
heat flow monitoring systems, biological measurement systems, chemical
measurement systems,
ultrasonic monitoring systems, radiography systems, LIDAR-based monitoring
systems, access
control systems, penetrating wave sensing systems, SONAR-based monitoring
systems, radar-
based monitoring systems, computed tomography systems, magnetic resonance
imaging systems,
network monitoring systems, or others.
[0644] In embodiments, the set of Internet of Things resources includes a set
of camera
systems. In embodiments, the set of Internet of Things resources includes a
set of lighting
systems. In embodiments, the set of Internet of Things resources includes a
set of machine vision
systems. In embodiments, the set of Internet of Things resources includes a
set of motion sensing
systems.
[0645] In embodiments, the set of Internet of Things resources includes a set
of weighing
systems. In embodiments, the set of Internet of Things resources includes a
set of inspection
systems. In embodiments, the set of Internet of Things resources includes a
set of environmental
sensor systems. In embodiments, the set of Internet of Things resources
includes a set of onboard
sensor systems.
[0646] In embodiments, the set of Internet of Things resources includes a set
of onboard
diagnostic systems. In embodiments, the set of Internet of Things resources
includes a set of
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environmental control systems. In embodiments, the set of Internet of Things
resources includes
a set of sensor-enabled network switching and routing systems. In embodiments,
the set of
Internet of Things resources includes a set of RF sensing systems. In
embodiments, the set of
Internet of Things resources includes a set of magnetic sensing systems.
[0647] In embodiments, the set of Internet of Things resources includes a set
of pressure
monitoring systems. In embodiments, the set of Internet of Things resources
includes a set of
vibration monitoring systems. In embodiments, the set of Internet of Things
resources includes a
set of temperature monitoring systems. In embodiments, the set of Internet of
Things resources
includes a set of heat flow monitoring systems. In embodiments, the set of
Internet of Things
resources includes a set of biological measurement systems.
[0648] In embodiments, the set of Internet of Things resources includes a set
of chemical
measurement systems. In embodiments, the set of Internet of Things resources
includes a set of
ultrasonic monitoring systems. In embodiments, the set of Internet of Things
resources includes a
set of radiography systems. In embodiments, the set of Internet of Things
resources includes a set
of LIDAR-based monitoring systems. In embodiments, the set of Internet of
Things resources
includes a set of access control systems.
[0649] In embodiments, the set of Internet of Things resources includes a set
of penetrating
wave sensing systems. In embodiments, the set of Internet of Things resources
includes a set of
SONAR-based monitoring systems. In embodiments, the set of Internet of Things
resources
includes a set of radar-based monitoring systems. In embodiments, the set of
Internet of Things
resources includes a set of computed tomography systems. In embodiments, the
set of Internet of
Things resources includes a set of magnetic resonance imaging systems. In
embodiments, the set
of Internet of Things resources includes a set of network monitoring systems.
SOCIAL DATA COLLECTION ARCHITECTURE
[0650] Referring to Fig. 17, an embodiment of the platform 604 is provided. As
with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 614, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 1160, a set of data storage
facilities or systems 624, and
a set of monitoring facilities or systems 614. The platform 604 may support a
set of applications
630 (including processes, workflows, activities, events, use cases and
applications) for enabling
an enterprise to manage a set of value chain network entities 652, such as
from a point of origin
to a point of customer use of a product 650, which may be an intelligent
product.
[0651] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a set of microservices layers including an
application layer supporting
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at least one supply chain application and at least one demand management
application, wherein
the microservice layers include a data collection layer that collects
information from a set of
social network sources that provide information with respect to supply chain
entities and demand
management entities.
[0652] In embodiments, the VCNP 604 may further include a set of microservices
layers that
include a data collection layer (e.g., monitoring systems and data collection
systems layer 614)
with a social data collection facility 1760 that collects information from a
set of social network
resources MPVC1708 that provide information with respect to supply chain
entities and demand
management entities. The social network data collection facilities 1760 may
support various
applications among a set of demand management applications 1502, a set of
supply chain
applications 1500, a set of intelligent product applications 1510, a set of
asset management
applications 1530 and a set of enterprise resource management applications
1520 for a category
of goods. Social network data collection (using social network data collection
facilities 1760)
may be facilitated by a social data collection configuration interface, such
as for configuring
queries, identifying social data sources of relevance, configuring APIs for
data collection, routing
data to appropriate applications 630, and the like.
CROWDSOURCING DATA COLLECTION ARCHITECTURE
[0653] Referring to Fig. 18, an embodiment of the platform 604 is provided. As
with other
embodiments, the platform 604 may employ a micro-services architecture with
the various data
handling layers 614, a set of network connectivity facilities 642 (which may
include or connect to
a set of interfaces 702 of various layers of the platform 604), a set of
adaptive intelligence
facilities or adaptive intelligent systems 1160, a set of data storage
facilities or systems 624, and
a set of monitoring facilities or systems 614. The platform 604 may support a
set of applications
630 (including processes, workflows, activities, events, use cases and
applications) for enabling
an enterprise to manage a set of value chain network entities 652, such as
from a point of origin
to a point of customer use of a product 650, which may be an intelligent
product.
[0654] Thus, provided herein are methods, systems, components and other
elements for an
information technology system that may include: a cloud-based management
platform with a
micro-services architecture, a set of interfaces, network connectivity
facilities, adaptive
intelligence facilities, data storage facilities, and monitoring facilities
that are coordinated for
monitoring and management of a set of value chain network entities; a set of
applications for
enabling an enterprise to manage a set of value chain network entities from a
point of origin to a
point of customer use; and a set of microservices layers including an
application layer supporting
at least one supply chain application and at least one demand management
application, wherein
the microservice layers include a data collection layer that collects
information from a set of
crowdsourcing resources that provide information with respect to supply chain
entities and
demand management entities.
[0655] In embodiments, the VCNP 604 may further include a set of microservices
layers that
include a monitoring systems and data collection systems layer 614 with a
crowdsourcing facility
1770 that collects information from a set of crowdsourcing resources that
provide information
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with respect to supply chain entities and demand management entities. The
crowdsourcing
services 1770 may support various applications among a set of demand
management applications
1502, a set of supply chain applications 1500, a set of intelligent product
applications 1510, a set
of asset management applications 1530 and a set of enterprise resource
management applications
1520 for a category of goods. Crowdsourcing may be facilitated by a
crowdsourcing interface
1770, such as for configuring queries, setting rewards for information,
configuring workflows,
determining eligibility for participation, and other elements of
crowdsourcing.
VALUE CHAIN DIGITAL TWIN PROCESSING (DTPT)
[0656] Referring now to Fig. 52 a set of value chain network digital twins
1700 representing a
set of value chain network entities 652 is depicted. The digital twins 1700
are configured to
simulate properties, states, operations, behaviors and other aspects of the
value chain network
entities 652. The digital twins 1700 may have a visual user interface, e.g.,
in the form of 3D
models, or may consist of system specifications or ontologies describing the
architecture,
including components and their interfaces, of the value chain network entities
652. The digital
twins 1700 may include configuration or condition of the value chain network
entities 652,
including data records of the past and current state of the value chain
network entities 652, such
as captured through sensors, through user input, and/or determined by outputs
of behavioral
models that describe the behavior of the value chain network entities 652. The
digital twins 1700
may be updated continuously to reflect the current condition of the value
chain network entities
652, based on sensor data, test and inspection results, conducted maintenance,
modifications, etc.
The digital twins 1700 may also be configured to communicate with a user via
multiple
communication channels, such as speech, text, gestures, and the like. For
example, a digital twin
1700 may receive queries from a user about the value chain network entities
652, generate
responses for the queries, and communicate such responses to the user.
Additionally or
alternatively, digital twins 1700 may communicate with one another to learn
from and identify
similar operating patterns and issues in other value chain network entities
652, as well as steps
taken to resolve those issues. The digital twins 1700 may be used for
monitoring, diagnostics,
simulation, management, remote control, and prognostics, such as to optimize
the individual and
collective performance and utilization of value chain network entities 652.
[0657] For example, machine twins 1770 may continuously capture the key
operational metrics
of the machines 724 and may be used to monitor and optimize machine
performance in real time.
Machine twins 1770 may combine sensor, performance, and environmental data,
including
insights from similar machines 724, enabling prediction of life span of
various machine
components and informed maintenance decisions. In embodiments, machine twins
1770 may
generate an alert or other warning based on a change in operating
characteristics of the machine
724. The alert may be due to an issue with a component of the machine 724.
Additionally,
machine twins 1770 may determine similar issues that have previously occurred
with the
machine or similar machines, provide a description of what caused the issues,
what was done to
address the issues, and explain differences between the present issue and the
previous issues and
what actions to take to resolve the issue, etc.
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[0658] Similarly, warehousing twins 1712 may combine a 3D model of the
warehouse with
inventory and operational data including the size, quantity, location, and
demand characteristics
of different products. The warehousing twins 1712 may also collect sensor data
in a connected
warehouse, as well as data on the movement of inventory and personnel within
the warehouse.
Warehousing twins 1712 may help in optimizing space utilization and aid in
identification and
elimination of waste in warehouse operations. The simulation using warehousing
twins 1712 of
the movement of products, personnel, and material handling equipment may
enable warehouse
managers to test and evaluate the potential impact of layout changes or the
introduction of new
equipment and new processes.
[0659] In embodiments, multiple digital twins of the value chain network
entities 652 may be
integrated, thereby aggregating data across the value chain network to drive
not only entity-level
insights but also system-level insights. For example, consider a simple value
chain network with
an operating facility 712 comprising different machines 724 including
conveyors, robots, and
inspection devices. The operating facility digital twin 1172 may need to
integrate the data from
digital twins 1770 of different machines to get a holistic picture of the
complete conveyor line in
the operating facility 712 (e.g., a warehouse, distribution center, or
fulfillment center where
packages are moved along a conveyor and inspected before being sent out for
delivery. While the
digital twin of conveyor line may provide insights about only its performance,
the composite
digital twin may aggregate data across the different machines in the operating
facility 712. Thus,
it may provide an integrated view of individual machines and their
interactions with
environmental factors in the operating facility leading to insights about the
overall health of the
conveyor line within the operating facility 712. As another example, the
supply factor twins 1650
and demand factor twins 1640 may be integrated to create a holistic picture of
demand-supply
equilibrium for a product 650. The integration of digital twins also enables
the querying of
multiple value chain network entities 652 and create a 360-degree view of the
value chain
network 668 and its various systems and subsystems.
[0660] It will be apparent that the ability to integrate digital twins of the
value chain network
entities 652 may be used to generate a value chain network digital twin system
from a plurality of
digital twin subsystems representing entities selected from among supply chain
entities, demand
management entities and value chain network entities. For example, a machine
digital twin 1770
is comprised of multiple digital twins of sub-systems and individual
components constituting the
machine 724. The machine's digital twin may integrate all such component twins
and their inputs
and outputs to build the model of the machine. Also, for example, a
distribution facility twins
system 1714 may be comprised of subsystems, such as warehousing twins 1712,
fulfilment twins
1600 and delivery system twins 1610.
[0661] Similarly, the process digital twin may be seen as comprised of digital
twins of multiple
sub-processes representing entities selected from among supply chain entities,
demand
management entities and value chain network entities. For example, the digital
twin of a
packaging process is comprised of digital twins of sub-processes for picking,
moving, inspecting
and packing the product. As another example, the digital twin of warehousing
process may be
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seen as comprised of digital twins of multiple sub-processes including
receiving, storing, picking
and shipping of stored inventories.
[0662] It will be apparent that a value chain network digital twin system may
be generated from
a plurality of digital twin subsystems or conversely a digital twin subsystem
may be generated
from a digital twin system, wherein at least one of the digital twin subsystem
and the digital twin
system represents entities selected from among supply chain entities, demand
management
entities and value chain network entities.
[0663] Similarly, a value chain network digital twin process may be generated
from a plurality
of digital twin sub-processes or conversely digital twin sub-process generated
from a digital twin
process wherein at least one of the digital twin sub-process and the digital
twin process
represents entities selected from among supply chain entities, demand
management entities and
value chain network entities.
[0664] The analytics obtained from digital twins 1700 of the value chain
network entities 652
and their interactions with one another provide a systemic view of the value
chain network as
well as its systems, sub-systems, processes and sub-processes. This may help
in generating new
insights into ways the various systems and processes may be evolved to improve
their
performance and efficiency.
[0665] In embodiments, the platform 604 and applications 630 may have a system
for
generating and updating a self-expanding digital twin that represents a set of
value chain entities.
The self-expanding digital twin continuously keeps learning and expanding in
scope, with more
and more data it collects and scenarios it encounters. As a result, the self-
expanding twin can
evolve with time and take on more complex tasks and answer more complex
questions posed by
a user of the self-expanding digital twin.
[0666] In embodiments, the platform 604 and applications 630 may have a system
for
scheduling the synchronization of a physical value chain entity's changing
condition to a digital
twin that represents a set of value chain entities. In embodiments, the
synchronization between
the physical value chain entity and its digital twin is on a near real-time
basis.
[0667] In embodiments, the platform 604 and applications 630 may have an
application
programming interface for extracting, sharing, and/or harmonizing data from
information
technology systems associated with multiple value chain network entities that
contribute to a
single digital twin representing a set of value chain entities.
[0668] In embodiments, value chain network management platform 604 may include
various
subsystems that may be implemented as micro services, such that other
subsystems of the system
access the functionality of a subsystem providing a micro service via
application programming
interface API. In some embodiments, the various services that are provided by
the subsystems
may be deployed in bundles that are integrated, such as by a set of APIs.
[0669] In embodiments, value chain network management platform 604 may include
a set of
microservices for managing a set of value chain network entities for an
enterprise and having a
set of processing capabilities for at least one of creating, modifying, and
managing the
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parameters of a digital twin that is used in the platform to represent a set
of value chain network
entities.
[0670] Value Chain Digital Twin Kit (DTIB)
[0671] The value chain network management platform may provide a digital twin
sub-system
in the form of an out-of-the-box kit system with self-configuring
capabilities. The kit may
provide a data-rich and interactive overview of a set of value chain network
entities constituting
the sub-system. For example, a supply chain out-of-the-box digital twin kit
system may represent
a set of supply chain entities that are linked to the identity of an owner or
operator of the supply
chain entities. The owner or operator of the supply chain entity may then use
the kit to get a
holistic picture of its complete portfolio. The owner may investigate for
information related to
various supply chain entities and ask interactive questions from the digital
twin kit system.
[0672] In embodiments, a demand management out-of-the-box digital twin kit
system may
represent a set of demand management entities that are linked to the identity
of an owner or
operator of the demand management entities.
[0673] In embodiments, a value chain network digital twin kit system for
providing out-of-the-
box, self-configuring capabilities may represent a set of demand management
entities and a set of
supply chain entities that are linked to the identity of an owner or operator
of the demand
management entities and the supply chain entities.
[0674] In embodiments, a warehouse digital twin kit system for providing out-
of-the-box, self-
configuring capabilities may represent a set of warehouse entities that are
linked to the identity of
an owner or operator of the warehouse.
[0675] Referring now to Fig. 53, an example warehouse digital twin kit system
5000 is
depicted. The warehouse digital twin kit system 5000 includes warehousing
twins in the virtual
space 5002 representing models of warehouses 654 in the real space 5004.
[0676] The warehouse digital twin kit system 5000 allows an owner or operator
5008 of the one
or more warehouse entities 654 to get complete portfolio overview of all these
entities- existing
or in design or construction. The owner 5008 may navigate a wealth of
information including
warehouse photographs 5010, 3D images 5012, live video feeds 5014 of real-time
construction
progress and AR or VR renderings 5018 of the warehousing entities 654. The
owner 5008 may
investigate about the health of one or more entities 654 and ask interactive
questions and search
for detailed information about one or more warehouse entities 654. The
warehouse digital twin
kit system 5000 has access to real time dynamic data captured by IoT devices
and sensors at
warehouse entities 654 and may be supported with natural language capabilities
enabling it to
interact with the owner 5008 and answer any questions about the condition of
the warehouse
entities 654.
[0677] In embodiments, warehouse digital twin kit system 5000 may provide the
portfolio
overview of warehouse entities 654 to owner 5008 in the form of a 3D
information map
containing all the warehouse entities 654. Owner 5008 may select a specific
entity on the map
and get information about inventory, operational and health data from the
warehousing twin
1710. Alternatively, the owner 5008 may ask for information about the overall
portfolio of
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warehouse entities 654 owned. The warehouse digital twin kit system 5000
consolidates
information from the multiple warehousing twins 1710 and provides a holistic
view. The
consolidated view may help owner 5008 to optimize operations across warehouse
entities 654 by
adjusting stock locations and staffing levels to match current or forecasted
demand. The owner
5008 may also display the information from warehouse digital twin kit system
5000 on a website
or marketing material to be accessed by any customers, suppliers, vendors and
other partners.
[0678] In embodiments, a container ship digital twin kit system for providing
out-of-the-box,
self-configuring capabilities may represent a set of container ship entities
that are linked to the
identity of an owner or operator of the container ship.
[0679] In embodiments, a port infrastructure digital twin kit system for
providing out-of-the-
box, self-configuring capabilities may represent a set of port infrastructure
entities that are linked
to the identity of an owner or operator of the port infrastructure.
[0680] Value Chain Compatibility Testing (VCCT)
[0681] The platform 604 may deploy digital twins 1700 of value chain network
entities 652 for
testing the compatibility between different value chain network entities 652
interacting with one
another and forming various systems and subsystems of the value chain network.
[0682] This brings visibility to the compatibility and performance of various
systems and
subsystems within the value chain network before there are any physical
impacts. Any
incompatibilities or performance deficiencies of different value chain network
entities 652 may
be highlighted through digital models and simulations rather than having to
rely on physical
systems to perform such tests which is both expensive and impractical.
[0683] The digital twin 1700 may make use of artificial intelligence 1160
(including any of the
various expert systems, artificial intelligence systems, neural networks,
supervised learning
systems, machine learning systems, deep learning systems, and other systems
described
throughout this disclosure and in the documents incorporated by reference) for
carrying out the
compatibility testing in the value chain network.
[0684] In embodiments, the platform may provide a system for testing
compatibility or
configuration of a set of vendor components for a container ship using a set
of digital twins
representing the container ship and the vendor components.
[0685] In embodiments, the platform may provide a system for testing
compatibility or
configuration of a set of vendor components for a warehouse using a set of
digital twins
representing the warehouse and the vendor components.
[0686] In embodiments, the platform may provide a system for testing
compatibility or
configuration of a set of vendor components for a port infrastructure facility
using a set of digital
twins representing the port infrastructure facility and the vendor components.
[0687] In embodiments, the platform may provide a system for testing
compatibility or
configuration of a set of vendor components for a shipyard facility using a
set of digital twins
representing the shipyard facility and the vendor components.
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[0688] In embodiments, the platform may provide a system for testing
compatibility or
configuration of a container ship and a set of port infrastructure facilities
using a set of digital
twins representing the container ship and the port infrastructure facility.
[0689] In embodiments, the platform may provide a system for testing
compatibility or
configuration of a barge and a set of waterways for a navigation route using a
set of digital twins
representing the barge and the set of waterways.
[0690] In embodiments, the platform may provide a system for testing
compatibility or
configuration of a container ship and a set of cargo for an identified
shipment using a set of
digital twins representing the container ship and the cargo.
[0691] In embodiments, the platform may provide a system for testing
compatibility or
configuration of a barge and a set of cargo for an identified shipment using a
set of digital twins
representing the barge and the cargo.
[0692] In embodiments, the platform may provide a system for testing
compatibility or
configuration of a set of cargo handling infrastructure facilities and a set
of cargo for an
identified shipment using a set of digital twins representing the cargo
handling infrastructure
facilities and the cargo.
[0693] Value Chain Infrastructure Testing (VCIT)
[0694] The platform 604 may deploy digital twins 1700 of value chain network
entities 652 to
perform stress tests on a set of value chain network entities. The digital
twins may help simulate
behavior of value chain network systems and sub-systems in a wide variety of
environments. The
stress tests may help run any "what-if' scenarios to understand the impact of
change in relevant
parameters beyond normal operating values and evaluate the resilience of the
infrastructure of
value chain network.
[0695] The platform 604 may include a system for learning on a training set of
outcomes,
parameters, and data collected from data sources relating to a set of value
chain network
activities to train artificial intelligence system 1160 (including any of the
various expert systems,
artificial intelligence systems, neural networks, supervised learning systems,
machine learning
systems, deep learning systems, and other systems described throughout this
disclosure and in the
documents incorporated) for performing such stress tests on the value chain
network.
[0696] In embodiments, the platform may include a system for learning on a
training set of
machine outcomes, parameters, and data collected from data sources relating to
a set of value
chain network activities to train an artificial intelligence/machine learning
system to perform
stress tests on the machine using a digital twin that represents a set of
value chain entities.
[0697] As described, the value chain network comprises a plurality of
interrelated sub-systems
and sub-processes that manage and control all aspects associated with the
production and
delivery of a finished product to an end-user¨from the acquisition and
distribution of raw
materials between a supplier and a manufacturer, through the delivery,
distribution, and storage
of materials for a retailer or wholesaler, and, finally, to the sale of the
product to an end-user. The
complex interconnected nature of the value chain network means that an adverse
event within
one subsystem or one or more value chain entities reflect through the entire
value chain network.
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[0698] Fig. 54 is an example method for performing a stress test on the value
chain network.
The stress test may comprise a simulation exercise to test the resilience of
the value chain
network (including its subsystems) and determine its ability to deal with an
adverse scenario, say
a natural calamity, a congested route, a change in law, or a deep economic
recession. Such
adverse or stress scenarios may affect one or more entities or subsystems
within the value chain
network depending on the nature of the scenario. Hence, any stress tests would
require simulating
scenarios and analyzing the impact of different scenarios across different
subsystems and on the
overall value chain network.
[0699] At 5102, all historical and current data related to the value chain
network are received.
The data may include information related to various operating parameters of
the value chain
network over a particular historical time period, say last 12 months. The data
may also provide
information on the typical values of various operating parameters under normal
conditions. Some
examples of operating parameters include: product demand, procurement lead
time, productivity,
inventory level at one or more warehouses, inventory turnover rates,
warehousing costs, average
time to transport product from warehouse to shipping terminals, overall cost
of product delivery,
service levels, etc. At 5104, one or more simulation models of value chain
network are created
based on the data. The simulation models help in visualizing the value chain
network as a whole
and in predicting how changes in operating parameters affect the operation and
performance of
the value chain network. In embodiments, the simulation model may be a sum of
multiple models
of different subsystems of the value chain network.
[0700] At 5106, one or more stress scenarios may be simulated by changing one
or more
parameters beyond the normal operating values. The simulating of stress
scenarios overcome the
limitation of any analysis based only on historical data and helps analyze the
network
performance across a range of hypothetical yet plausible stress conditions.
The simulation
involves varying (shocking) one or more parameters while keeping the other
parameters as fixed
to analyze the impact of such variations on value chain network. In
embodiments, a single
parameter may be varied while keeping remaining parameters as fixed. In other
embodiments,
multiple parameters may be varied simultaneously. At 5108, the outcomes of
stress scenario
simulations are determined, and the performance of value chain network and its
different
subsystems is estimated across various scenarios. At 5110, the data,
parameters and outcomes are
fed into a machine learning process in the artificial intelligence system 1160
for further analysis.
[0701] An advantage of generating data through simulations and then training
machine learning
algorithms on this data is the control this approach provides on the features
in the data as well as
volume and frequency of data.
[0702] In embodiments, the platform may include a system for learning on a
training set of
outcomes, parameters, and data collected from data sources relating to a set
of value chain
network activities to train an artificial intelligence/machine learning system
to perform stress
tests on a physical object using a digital twin that represents a set of value
chain entities.
[0703] In embodiments, the platform may include a system for learning on a
training set of
outcomes, parameters, and data collected from data sources relating to a set
of value chain
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network activities to train an artificial intelligence/machine learning system
to perform stress
tests on a telecommunications network using a digital twin that represents a
set of value chain
entities in a connected network of entities and the telecommunications
network.
[0704] For example, the telecommunications network may be stress tested for
resiliency by
deliberately increasing network traffic by generating and sending data packets
to a specific target
node within the telecommunications network. Further, the amount of traffic may
be varied to
create varying load conditions on the target node by manipulating the number,
rate or amount of
data in the data packets. The response from the target node may be determined
to evaluate how
the node performed in the stress test. The target node may be selected at
different parts of the
telecommunications network for stress testing so as to test robustness of any
portion of the
network in any topology. The simulated stress tests on the telecommunications
network may be
utilized to identify vulnerabilities in any portion of a network so that the
vulnerability can be
rectified before users experience network outages in a deployed network.
[0705] In embodiments, the platform may include a system for using a digital
twin that
represents a set of value chain entities in a demand management environment to
perform a set of
stress tests on a set of workflows in the demand management environment using
the digital twin,
wherein the stress tests represent impacts in the digital twin of varying a
set of demand-relevant
parameters to levels that exceed normal operating levels. For example, the
demand of a product
in the value chain network may be affected by factors like changes in consumer
confidence,
recessions, excessive inventory levels, substitute product pricing, overall
market indices,
currency exchange changes, etc. The demand factors twin 1640 may simulate such
scenarios by
varying supply parameters and evaluate the impact of such stresses on the
demand environments
672. The stress tests performed using the digital twins may help in testing
and evaluating the
resiliency of the value chain network both in cases of over-demand and under-
demand.
[0706] In embodiments, the platform may include a system for using a digital
twin that
represents a set of value chain entities in the supply chain to perform a set
of stress tests on a set
of workflows in the supply chain using the digital twin, wherein the stress
tests represent impacts
in the digital twin of varying a set of supply chain-relevant parameters to
levels that exceed
normal operating levels. For example, the supply of a product in the value
chain network may be
affected by factors like weather, natural calamities, traffic congestion,
regulatory changes
including taxes and subsidies and border restrictions, etc. The supply factors
twin 1650 may
simulate such scenarios by varying supply parameters and evaluate the impact
of such stresses on
the supply environments 670. The stress tests performed using the digital
twins may help in
testing and evaluating the resiliency of the value chain network both in cases
of over-supply and
under-supply.
[0707] Value Chain Incident Management (VCIM)
[0708] The platform 604 may deploy digital twins 1700 of value chain network
entities 652 for
automatically managing a set of incidents relating to a set of value chain
network entities and
activities. The incidents may include any events causing disruption to the
value chain network
like accidents, fires, explosions, labor strikes, increases in tariffs,
changes in law, changes in
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market prices (e.g., of fuel, components, materials, or end products), changes
in demand,
activities of cartels, closures of borders or routes, and/or natural events
and/or disasters
(including storms, heat waves, winds, earthquakes, floods, hurricanes,
tsunamis, etc.), among
many others.
[0709] Also, the platform 604 may provide real-time visualization and analysis
of mobility
flows in the value chain network. This may help in quantifying risks,
improving visibility and
reacting to the disruptions in the value chain network. For example, real-time
visualization of a
utility flow for shipping activities using a digital twin may help in
detecting the occurrence and
location of an emergency involving a shipping system and deploying emergency
services to the
detected location.
[0710] In embodiments, the platform may deploy digital twins 1700 of value
chain network
entities 652 for more accurate determination of accident fault. The platform
may learn on a
training set of accident outcomes, parameters, and data collected from the
monitoring layer 614
and data sources of the data storage layer 624 to train artificial
intelligence system 1160 using a
set of digital twins 1700 of involved value chain network entities 652 to
determine accident fault.
For example, data from digital twins of two colliding vehicles may be compared
with each other
in addition to data from the drivers, witnesses and police reports to
determine accident fault.
[0711] In embodiments, the platform may include a system for learning on a
training set of
vehicular event outcomes, parameters, and data collected from data sources
related to a set of
value chain network entities 652 to train artificial intelligence system 1160
to use a digital twins
1700 of a selected set of value chain network entities 652 to detect an
incidence of fraud. For
example, comparing vehicular event data from digital twins of vehicles to any
insurance claims,
contract claims, maritime claims on such vehicles may help in detecting any
mismatch in the
two.
[0712] In embodiments, the platform may include a system for learning on a
training set of
vehicle outcomes, parameters, and data collected from data sources related to
a set of value chain
network entities 652 to train artificial intelligence system 1160 to use a
digital twin 1700 of a
selected set of value chain network entities 652 to detect unreported abnormal
events with respect
to selected set of value chain network entities 652. Consider an example where
the digital twin of
a vehicle shows an abnormal event like an accident but this event has not been
reported by the
driver of the vehicle. The unreported event may be added to the record of the
vehicle and the
driver by a lessor of the vehicle. Also, the lessor of the vehicle may charge
the lessee for repairs
or diminished value of the vehicle at lease-end and adjust residual value
forecast for the same.
Similarly, an insurer may add the unreported event to the record of the
vehicle and the driver.
The reporting may be as detailed as the exact nature, timing, location, fault,
etc. of the accident or
just the fact there was unreported accident. This information may then be used
for calculating the
insurance premium.
[0713] Finally, in case there are multiple entities involved in the accident,
the data may be
triangulated with the digital twin of another entity for validation.
[0714] Value Chain Predictive Maintenance (PMVC)
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[0715] The platform 604 may deploy digital twins 1700 of value chain network
entities 652 to
predict when a set of value chain network entities should receive maintenance.
[0716] The digital twin may predict the anticipated wear and failure of
components of a system
by reviewing historical and current operational data thereby reducing the risk
of unplanned
downtime and the need for scheduled maintenance. Instead of over-servicing or
over-maintaining
products to avoid costly downtime, repairs or replacement, any product
performance issues
predicted by the digital twin may be addressed in a proactive or just-in-time
manner.
[0717] The digital twins 1700 may collect events or state data about value
chain entities 652
from the monitoring layer 614 and historical or other data from selected data
sources of the data
storage layer 624. Predictive analytics powered by artificial intelligence
system 1160 dissect the
data, search for correlations, and formulate predictions about maintenance
need and remaining
useful life of a set of value chain entities 652.
[0718] The platform 604 may include a system for learning on a training set of
outcomes,
parameters, and data collected from data sources relating to a set of value
chain network
activities to train artificial intelligence 1160 (including any of the various
expert systems,
artificial intelligence systems, neural networks, supervised learning systems,
machine learning
systems, deep learning systems, and other systems described throughout this
disclosure and in the
documents incorporated) for performing condition monitoring, anomaly
detection, failure
forecasting and predictive maintenance of a set of value chain entities 652.
[0719] In embodiments, the platform may include a system for learning on a
training set of
machine maintenance outcomes, parameters, and data collected from data sources
relating to a
set of machine activities to train an artificial intelligence/machine learning
system to perform
predictive maintenance on a machine using a digital twin of the machine.
[0720] In embodiments, artificial intelligence system 1160 may train models,
such as predictive
models (e.g., various types of neural networks, classification-based models,
regression based
models, and other machine-learned models). In embodiments, training can be
supervised, semi-
supervised, or unsupervised. In embodiments, training can be done using
training data, which
may be collected or generated for training purposes.
[0721] An example artificial intelligence system 1160 trains a machine
predictive maintenance
model. A predictive maintenance model may be a model that receives machine
related data and
outputs one or more predictions or answers regarding the remaining life of the
machine. The
training data can be gathered from multiple sources including machine
specifications,
environmental data, sensor data, run information, outcome data and notes
maintained by machine
operators. The artificial intelligence system 1160 takes in the raw data, pre-
processes it and
applies machine learning algorithms to generate the predictive maintenance
model. In
embodiments, the artificial intelligence system 1160 may store the predictive
model in a model
datastore within data storage layer 624.
[0722] Some examples of questions that the predictive model may answer are:
when will the
machine fail, what type of failure it will be, what is the probability that a
failure will occur within
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the next X hours, what is the remaining useful life of the machine, is the
machine behaving in an
uncharacteristic manner, which machine requires maintenance most urgently and
the like.
[0723] The artificial intelligence system 1160 may train multiple predictive
models to answer
different questions. For example, a classification model may be trained to
predict failure within a
given time window, while a regression model may be trained to predict the
remaining useful life
of the machine.
[0724] In embodiments, training may be done based on feedback received by the
system, which
is also referred to as "reinforcement learning." In embodiments, the
artificial intelligence system
1160 may receive a set of circumstances that led to a prediction (e.g.,
attributes of a machine,
attributes of a model, and the like) and an outcome related to the machine and
may update the
model according to the feedback.
[0725] In embodiments, artificial intelligence system 1160 may use a
clustering algorithm to
identify the failure pattern hidden in the failure data to train a model for
detecting
uncharacteristic or anomalous behavior. The failure data across multiple
machines and their
historical records may be clustered to understand how different patterns
correlate to certain wear-
down behavior and develop a maintenance plan resonant with the failure.
[0726] In embodiments, artificial intelligence system 1160 may output scores
for each possible
prediction, where each prediction corresponds to a possible outcome. For
example, in using a
predictive model used to determine a likelihood that a machine will fail in
the next one week, the
predictive model may output a score for a "will fail" outcome and a score for
a "will not fail"
outcome. The artificial intelligence system 1160 may then select the outcome
with the greater
score as the prediction. Alternatively, the system 1160 may output the
respective scores to a
requesting system. In embodiments, the output from system 1160 includes a
probability of the
prediction's accuracy.
[0727] Fig. 55 is an example method used by machine twin 1770 for detecting
faults and
predicting any future failures of machine 724.
[0728] At 5202, a plurality of streams of machine related data from multiple
data sources are
received at the machine twin 1770. This includes machine specifications like
mechanical
properties, data from maintenance records, operating data collected from the
sensors, historical
data including failure data from multiple machines running at different times
and under different
operating conditions and so on. At 5205, the raw data is cleaned by removing
any missing or
noisy data, which may occur due to any technical problems in the machine at
the time of
collection of data. At 5208, one or more models are selected for training by
machine twin 1770.
The selection of model is based on the kind of data available at the machine
twin 1770 and the
desired outcome of the model. For example, there may be cases where failure
data from
machines is not available, or only a limited number of failure datasets exist
because of regular
maintenance being performed. Classification or regression models may not work
well for such
cases and clustering models may be most suitable. As another example, if the
desired outcome of
the model is determining current condition of the machine and detecting any
faults, then fault
detection models may be selected, whereas if the desired outcome is predicting
future failures
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then remaining useful life prediction model may be selected. At 5210, the one
or more models
are trained using training dataset and tested for performance using testing
dataset. At 5212, the
trained model is used for detecting faults and predicting future failure of
the machine on
production data.
[0729] Fig.56 is an example embodiment depicting the deployment of machine
twins 1770
perform predictive maintenance on machines 724. Machine twin 1770 receives
data from data
storage systems 624 on a real-time or near real-time basis. The data storage
systems 624 may
store different types of data in different datastores. For example, machine
datastore 5202 may
store data related to machine identification and attributes, machine state and
event data, data from
maintenance records, historical operating data, notes from machine operator,
etc. Sensor
datastore 5204 may store sensor data from operation such as temperature,
pressure, and vibration
that may be stored as signal or time series data. Failure datastore 5310 may
store failure data
from machine 724 or similar machines running at different times and under
different operating
conditions. Model datastore 5312 may store data related to different
predictive models including
fault detection and remaining life prediction models.
[0730] Machine twin 1770 then coordinates with artificial intelligence system
to select one or
more of models based on the kind and quality of available data and the desired
answers or
outcomes. For example, physical models 5320 may be selected if the intended
use of machine
twin 1770 is to simulate what-if scenarios and predict how the machine will
behave under such
scenarios. Fault Detection and Diagnostics Models 5322 may be selected to
determine the current
health of the machine and any fault conditions. A simple fault detection model
may use one or
more condition indicators to distinguish between regular and faulty behaviors
and may have a
threshold value for the condition indicator that is indicative of a fault
condition when exceeded.
A more complex model may train a classifier to compare the value of one or
more condition
indicators to values associated with fault states and returns the probability
of presence of one or
more fault states.
[0731] Remaining Useful Life (RUL) Prediction models 5324 are used for
predicting future
failures and may include degradation models 5326, survival models 5328 and
similarity models
5330. An example RUL prediction model may fit the time evolution of a
condition indicator and
predicts how long it will be before the condition indicator crosses some
threshold value
indicative of a failure. Another model may compare the time evolution of the
condition indicator
to measured or simulated time series from similar systems that ran to failure.
[0732] In embodiments, a combination of one or more of these models may be
selected by the
machine twin 1770.
[0733] Artificial Intelligence system 1160 may include machine learning
processes 5340,
clustering processes 5342, analytics processes 5344 and natural language
processes 5348.
Machine learning processes 5340 work with machine twin 1770 to train one or
more models as
identified above. An example of such machine learned model is the RUL
prediction model 5324.
The model 5324 may be trained using training dataset pmvc 230 from the Data
Storage Systems
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624. The performance of the model 5324 and classifier may then be tested using
testing dataset
5350.
[0734] Clustering processes 5342 may be implemented to identify the failure
pattern hidden in
the failure data to train a model for detecting uncharacteristic or anomalous
behavior. The failure
data across multiple machines and their historical records may be clustered to
understand how
different patterns correlate to certain wear-down behavior. Analytics
processes 5344 perform
data analytics on various data to identify insights and predict outcomes.
Natural language
processes 4348 coordinate with machine twin 1770 to communicate the outcomes
and results to
the user of machine twin 1770.
[0735] The outcomes 5360 may be in the form of modeling results 5362, alerts
and warnings
5364 or remaining useful life (RUL) predictions 5368. Machine twin 1770 may
communicate
with a user via multiple communication channels such as speech, text, gestures
to convey
outcomes 5360.
[0736] In embodiments, models may then be updated or reinforced based on the
model
outcomes 5360. For example, the artificial intelligence system may receive a
set of circumstances
that led to a prediction of failure and the outcome and may update the model
based on the
feedback.
[0737] In embodiments, the platform may include a system for learning on a
training set of ship
maintenance outcomes, parameters, and data collected from data sources
relating to a set of ship
activities to train an artificial intelligence/machine learning system to
perform predictive
maintenance on a ship using a digital twin of the ship.
[0738] In embodiments, the platform may include a system for learning on a
training set of
barge maintenance outcomes, parameters, and data collected from data sources
relating to a set of
barge activities to train an artificial intelligence/machine learning system
to perform predictive
maintenance on a barge using a digital twin of the barge.
[0739] In embodiments, the platform may include a system for learning on a
training set of port
maintenance outcomes, parameters, and data collected from data sources
relating to a set of port
activities to train an artificial intelligence/machine learning system to
perform predictive
maintenance on a port infrastructure facility using a digital twin of the port
infrastructure facility.
[0740] In embodiments, the platform may include a system for learning on a
training set of
repair outcomes, parameters, and data collected from data sources related to a
set of value chain
entities to train an artificial intelligence/machine learning system to use a
digital twin of a
selected set of value chain entities to estimate the cost of repair of a
damaged object.
[0741] In embodiments, the platform may include a system for learning on a
training set of
infrastructure outcomes, parameters, and data collected from data sources to
train an artificial
intelligence/machine learning system to predict deterioration of
infrastructure using a digital twin
of the infrastructure.
[0742] In embodiments, the platform may include a system for learning on a
training set of
natural hazard outcomes, parameters, and data collected from data sources
relating to a set of
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shipping activities to train an artificial intelligence/machine learning
system to model natural
hazard risks for a set of shipping infrastructure facilities using a digital
twin of a city.
[0743] In embodiments, the platform may include a system for learning on a
training set of
maintenance outcomes, parameters, and data collected from data sources
relating to a set of
shipping activities to train an artificial intelligence/machine learning
system to monitor shipping
infrastructure maintenance activities for a set of shipping infrastructure
facilities using a digital
twin of the set of facilities
[0744] In embodiments, the platform may include a system for learning on a
training set of
maintenance outcomes, parameters, and data collected from data sources
relating to a set of
shipping activities to train an artificial intelligence/machine learning
system to detect the
occurrence and location of a maintenance issue using a digital twin of a set
of shipping
infrastructure facilities and having a system for automatically deploying
maintenance services to
the detected location.
[0745] Referring to Fig. 57, the platform 604 may include, integrate,
integrate with, manage,
.. control, coordinate with, or otherwise handle customer digital twins 5502
and/or customer profile
digital twins 1730.
[0746] Customer digital twins 5502 may represent evolving, continuously
updated digital
representations of value chain network customers 662. In embodiments, value
chain network
customers 662 include consumers, licensees, businesses, enterprises, value-
added resellers and
other resellers, distributors, retailers (including online retailers, mobile
retailers, conventional
brick and mortar retailers, pop-up shops and the like), end users, and others
who may purchase,
license, or otherwise use a category of goods and/or related services.
[0747] Customer profile digital twins 1730, on the other hand, may represent
one or more
demographic (age, gender, race, marital status, number of children,
occupation, annual income,
education level, living status (homeowner, renter, and the like)
psychographic, behavioral,
economic, geographic, physical (e.g., size, weight, health status,
physiological state or condition,
or the like) or other attributes of a set of customers. In embodiments,
customer profile digital
twins 1730 may be enterprise customer profile digital twins that represent
attributes of a set of
enterprise customers. In embodiments, a customer profiling application may be
used to manage
customer profiles 5504 based on historical purchasing data, loyalty program
data, behavioral
tracking data (including data captured in interactions by a customer with an
intelligent product
650), online clickstream data, interactions with intelligent agents, and other
data sources.
[0748] Customers 662 can be depicted in a set of one or more customer digital
twins 5502, such
as by populating the customer digital twin 1730 with value chain network data
objects 1004, such
as event data 1034, state data 1140, or other data with respect to value chain
network customers
662. Likewise, customer profiles 5504 can be depicted in a set of one or more
customer profile
digital twins 1730, such as by populating the customer profile digital twins
1730 with value chain
network data objects 1004, such as described throughout this disclosure.
[0749] Customer digital twins 5502 and customer profile digital twins 1730 may
allow for
modeling, simulation, prediction, decision-making, classification, and the
like.
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[0750] Where customers 662 are consumers, for example, the respective customer
digital twins
1730 may be populated with identity data, account data, payment data, contact
data, age data,
gender data, race data, location data, demographic data, living status data,
mood data, stress data,
behavior data, personality data, interest data, preference data, style data,
medical data,
physiological data, phycological data, physical attribute data, education
data, employment data,
salary data, net worth data, family data, household data, relationship data,
pet data,
contact/connection data (such as mobile phone contacts, social media
connections, and the like),
transaction history data, political data, travel data, product interaction
data, product feedback
data, customer service interaction data (such as a communication with a
chatbot, or a telephone
communication with a customer service agent at a call center), fitness data,
sleep data, nutrition
data, software program interaction observation data 1500 (e.g., by customers
interacting with
various software interfaces of applications 630 involving value chain entities
652) and physical
process interaction observation data 1510 (e.g., by watching customers
interacting with products
or other value chain entities 652), and the like.
[0751] In another example, where customers 662 are enterprises or businesses,
the customer
digital twin 1730 may be populated with identity data, account data, payment
data, transaction
data, product feedback data, location data, revenue data, enterprise type
data, product and/or
service offering data, worker data (such as identity data, role data, and the
like), and other
enterprise-related attributes.
[0752] Customer digital twins and customer profile digital twins 1730 may
include a set of
components, processes, services, interfaces, and other elements for
development and deployment
of digital twin capabilities for visualization of value chain network
customers 662 and customer
profiles 5504 as well as for coordinated intelligence (including artificial
intelligence 1160, edge
intelligence, analytics and other capabilities) and other value-added services
and capabilities that
are enabled or facilitated with digital twins.
[0753] In embodiments, the customer digital twins 5502 and customer profile
digital twins
1730 may take advantage of the presence of multiple applications 630 within
the value chain
management platform layer 604, such that a pair of applications may share data
sources (such as
in the data storage layer 624) and other inputs (such as from the monitoring
layer 614) that are
collected with respect to value chain entities 652, as well as sharing events,
state information and
outputs, which collectively may provide a much richer environment for
enriching content in the
digital twins, including through use of artificial intelligence 1160
(including any of the various
expert systems, artificial intelligence systems, neural networks, supervised
learning systems,
machine learning systems, deep learning systems, and other systems described
throughout this
disclosure and in the documents incorporated by reference) and through use of
content collected
by the monitoring layer 614 and data collection systems 640.
[0754] An environment for development of a customer digital twin 5502 may
include a set of
interfaces for developers in which a developer may configure an artificial
intelligence system
1160 to take inputs from selected data sources of the data storage layer 624
and events or other
data from the monitoring systems layer 614 and supply them for inclusion in a
customer digital
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twin 5502. A customer digital twin development environment may be configured
to take outputs
and outcomes from various applications 630. In embodiments, a customer digital
twin 1730 may
be provided for the wide range of value chain network applications 630
mentioned throughout
this disclosure and the documents incorporated herein by reference.
[0755] In embodiments, the customer digital twin 5502 may be rendered by a
computing
device, such that a user can view a digital representation of the customer
714. For example, a
customer digital twin 5502 may be rendered and output to a display device. In
another example, a
5502 may be rendered in a three-dimensional environment and viewed using a
virtual reality
headset.
[0756] An environment for development of a customer profile digital twin 1730
may include a
set of interfaces for developers in which a developer may configure an
artificial intelligence
system 1160 to take inputs from selected data sources of the data storage
layer 624 and events or
other data from the monitoring systems layer 614 and supply them for inclusion
in a customer
profile digital twin 1730. A customer profile digital twin development
environment may be
configured to take outputs and outcomes from various applications 630. In
embodiments, a
customer profile digital twin 1730 may be provided for the wide range of value
chain network
applications 630 mentioned throughout this disclosure and the documents
incorporated herein by
reference.
[0757] In embodiments, the adaptive intelligent systems layer 614 is
configured to train and
implement artificial intelligence systems to perform tasks related to the
value chain network 668
and/or value chain network entities 652. For example, the adaptive intelligent
systems layer 614
may be leveraged to recommend products, enhance customer experience, select
advertising
attributes for advertisements relating to value chain products and/or
services, and/or other
appropriate value-chain tasks.
[0758] In embodiments, a customer profile digital twin 1730 or other customer
digital twin may
be created interactively and cooperatively with a customer, such as by
allowing a customer to
request, select, modify, delete, or otherwise influence a set of properties,
states, behaviors, or
other aspects represented in the digital twin 1730. For example, a customer
could refine sizes
(e.g., shoe size, dress size, shirt size, pant size, and the like), indicate
interests and needs (e.g.,
what the customer is interested in buying), indicate behaviors (e.g., projects
planned by an
enterprise), update current states (e.g., to reflect changes), and the like. A
version of the digital
twin 1730 may thus be made available to a customer, such as in a graphical
user interface, where
the customer may manipulate one or more aspects of the digital twin 1730,
request changes, and
the like. In embodiments, multiple versions of a digital twin 1730 may be
maintained for a given
customer, such as a version for customer review, an internal version for an
enterprise or host, a
version for each of a specific set of brands (e.g., where a customer's
appropriate clothing sizes
vary by brand), a public version (such as one shared with a customer's social
network for
feedback, such as from friends), a private version (such as one where a
customer is provided
complete control over features and properties), a simulation version, a real-
time version, and the
like. In embodiments, the adaptive intelligent systems layer 614 is configured
to leverage the
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customer digital twins 5502, customer profile digital twins 1730, and/or other
digital twins 1700
of other value chain network entities 652. In embodiments, the adaptive
intelligent systems layer
614 is configured to perform simulations using the customer digital twins
5502, customer profile
digital twins 1730, and/or digital twins of other value chain network entities
652. For example,
the adaptive intelligent systems layer 614 may vary one or more features of a
product digital twin
1780 as its use is simulated by a customer digital twin 1730.
[0759] In embodiments, a simulation management system 5704 may set up,
provision,
configure, and otherwise manage interactions and simulations between and among
digital twins
1700 representing value chain entities 652.
[0760] In embodiments, the adaptive intelligent systems layer 614 may, for
each set of features,
execute a simulation based on the set of features and may collect the
simulation outcome data
resulting from the simulation. For example, in executing a simulation
involving the interactions
of an intelligent product digital twin 1780 representing an intelligent
product 650 and a customer
digital twin 1730, the adaptive intelligent systems layer 614 can vary the
dimensions of the
.. intelligent product digital twin 1780 and can execute simulations that
generate outcomes in a
simulation management system 5704. In this example, an outcome can be an
amount of time
taken by a customer digital twin 5502 to complete a task using the intelligent
product digital twin
1780. During the simulations, the adaptive intelligent systems layer 614 may
vary the intelligent
product digital twin 1780 display screen size, available capabilities
(processing, speech
.. recognition, voice recognition, touch interfaces, remote control, self-
organization, self-healing,
process automation, computation, artificial intelligence, data storage, and
the like), materials,
and/or any other properties of the intelligent product digital twin 1780.
Simulation data 5710 may
be created for each simulation and may include feature data used to perform
the simulations, as
well as outcome data. In the example described above, the simulation data 5710
may be the
properties of the customer digital twin 5502 and the intelligent product
digital twin 1780 that
were used to perform the simulation and the outcomes resulting therefrom. In
embodiments, a
machine learning system 5720 may receive training data 5730, outcome data
5740, simulation
data 5710, and/or data from other types of external data sources 5702 (weather
data, stock market
data, sports event data, news event data, and the like). In embodiments, this
data may be provided
to the machine-learning system 5720 via an API of the adaptive intelligent
systems layer 614.
The machine learning system 5720 may train, retrain, or reinforce machine
leaning models 5750
using the received data (training data, outcome data, simulation data, and the
like).
[0761] Fig. 58 illustrates an example of an advertising application that
interfaces with the
adaptive intelligent systems layer 614. In example embodiments, the
advertising application may
be configured automate advertising-related tasks for a value chain product or
service.
[0762] In embodiments, the machine-learning system 5720 trains one or more
models 5750 that
are leveraged by the artificial intelligence system 1160 to make
classifications, predictions,
and/or other decisions relating to advertisements for a set of value chain
products and/or services.
[0763] In example embodiments, a model 5750 is trained to select advertisement
features to
optimize one or more outcomes (e.g., maximize product sales for a product 650
in the value
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chain network 668). The machine-learning system 5720 may train the models 5750
using n-
tuples that include the features pertaining to advertisements and one or more
outcomes associated
with the advertisements. In this example, features for an advertisement may
include, but are not
limited to, product and/or service category advertised, advertised product
features (price, product
vendor, and the like), advertised service features, advertisement type
(television, radio, podcast,
social media, e-mail or the like), advertisement length (10 seconds, 30
seconds, or the like),
advertisement timing (in the morning, before a holiday, and the like),
advertisement tone
(comedic, informational, emotional, or the like), and/or other relevant
advertisement features. In
this example, outcomes relating to the advertisement may include product
sales, total cost of the
advertisement, advertisement interaction measures, and the like. In this
example, one or more
digital twins 1700 may be used to simulate the different arrangements (e.g.,
digital twins of
advertisements, customers, customer profiles, and environments), whereby one
or more
properties of the digital twins are varied for different simulations and the
outcomes of each
simulation may be recorded in a tuple with the proprieties. Other examples of
training advertising
models may include a model that is trained to generate advertisements for
value chain products
650, a model that is trained to manage an advertising campaign for value chain
products 650, and
the like. In operation, the artificial intelligence system 1160 may use such
models 5750 to make
advertisement decisions on behalf of an advertising application 5602 given one
or more features
relating to an advertising-related task or event. For example, the artificial
intelligence system
1160 may select a type of advertisement (e.g., social media, podcast, and the
like) to use for a
value chain product 650. In this example, the advertising application 5602 may
provide the
features of the product to artificial intelligence system 1160. These features
may include product
vendor, the price of the product, and the like. In embodiments, the artificial
intelligence system
1160 may insert these features into one or more of the models 5750 to obtain
one or more
decisions, which may include which type of advertisement to use. In
embodiments, the artificial
intelligence system 1160 may leverage the customer digital twins 5502 and/or
customer profile
digital twins 1730 to run simulations on the one or more decisions and
generate simulation data
5710. The machine learning system 5720 may receive the simulation data 5710
and other data as
described throughout this disclosure to retrain or reinforce machine leaning
models. In
embodiments, the customer digital twins 5502, customer profile digital twins
1730, and other
digital twins 1700 may be leveraged by the artificial intelligence system 1160
to simulate a
decision made by the artificial intelligence system 1160 before providing the
decision to the
value chain entity 652. In the present example, the customer profile digital
twins 1730 may be
leveraged by the artificial intelligence system 1160 to simulate decisions
made by the artificial
intelligence system 1160 before providing the decision to the advertising
application 5602. In
embodiments, where simulation outcomes are unacceptable, simulation data 5710
may be
reported to the machine learning system 5720, which may use the received data
to re-train
machine learning models 5750, which may then be leveraged by the artificial
intelligence system
1160 to make a new decision. The advertising application 824 may initiate an
advertising event
using the decision(s) made by the artificial intelligence system 1160. In
embodiments, after the
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advertising event, the outcomes of the event (e.g., product sales) may be
reported to the machine-
learning system 5720 to reinforce the models 5750 used to make the decisions.
Furthermore, in
some embodiments, the output of the advertising application and/or the other
value chain entity
data sources may be used to update one or more properties of customer digital
twins 5502,
customer profile digital twins 1730 and/or other digital twins 1700.
[0764] Fig. 59 illustrates an example of an e-commerce application 5604
integrated with the
adaptive intelligent systems layer 614. In embodiments, an e-commerce
application 5604 may be
configured to generate product recommendations for value chain customers 662.
For example,
the ecommerce application 5604 may be configured to receive one or more
product features for a
value chain network product 650. Examples of product features may include, but
are not limited
to product types, product capabilities, product price, product materials,
product vendor, and the
like. In embodiments, the e-commerce application 5604 determines
recommendations to optimize
an outcome. Examples of outcomes can include software interaction observations
(such as mouse
movements, mouse clicks, cursor movements, navigation actions, menu
selections, and many
others), such as logged and/or tracked by software interaction observation
system 1500, purchase
of the product by a customer 714, and the like. In embodiments, the e-commerce
application
5604 may interface with the artificial intelligence system 1160 to provide
product features and to
receive product recommendations that are based thereon. In embodiments, the
artificial
intelligence system 1160 may utilize one or more machine-learned models 5750
to determine a
recommendation. In some embodiments, the simulations run by the customer
digital twin 1730
may be used to train the product recommendation machine-learning models.
[0765] Fig. 60 is a schematic illustrating an example of demand management
application 824
integrated with the adaptive intelligent systems layer 614. In embodiments,
the artificial
intelligence system 1160 may use machine-learning models 5750 trained to make
demand
management decisions for a demand environment 672 on behalf of a demand
management
application 824 given one or more demand factors 644. Demand factors 644 may
include product
type, product capabilities, product price, product materials, time of year,
location, and the like. In
embodiments, the artificial intelligence system 1160 may determine a demand
management
decision for a value chain product 650. For example, the artificial
intelligence system 1160 may
generate a demand management decision relating to how many printer ink
cartridges should be
supplied to a particular region for an upcoming month. In this example, the
demand management
system 824 may provide the demand factors 644 to artificial intelligence
system 1160. In
embodiments, the artificial intelligence system 1160 may insert these factors
644 into one or
more machine-learning models 5750 to obtain one or more demand management
decisions.
These decisions may include the volume of ink cartridges should be sent to the
select region
during the select month.
[0766] In embodiments, the artificial intelligence system 1160 may leverage
the customer
profile digital twins 1730 to run simulations on the proposed decisions
related to the demand
management. The demand management application 824 may then initiate an ink
resupply event
using the decision(s) made by the artificial intelligence system 1160.
Furthermore, after the ink
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resupply event, the outcomes of the event (e.g., ink cartridge sales) may be
reported to the
machine-learning system 5720 to reinforce the models used to make the
decisions. Furthermore,
in some embodiments, the output of the demand management system 824 and/or the
other value
chain entity data sources may be used to update one or more properties of
customer profile digital
twins 1730 and/or other digital twins 1700.
[0767] In embodiments, an API enables users to access the customer digital
twins 5502 and/or
customer profile digital twins 1730. In embodiments, an API enables users to
receive one or
more reports related to the digital twins.
[0768] The platform 604 may include, integrate, integrate with, manage,
control, coordinate
with, or otherwise handle household demand digital twins 5902. Household
demand digital twins
5902 may be a digital representation of a household demand for a product
category or for a set of
product categories.
[0769] An environment for development of a household demand digital twin 5902
may include
a set of interfaces for developers in which a developer may configure an
artificial intelligence
system 1160 to take inputs from selected data sources of the data storage
layer 624 and events or
other data from the monitoring systems layer 614 and supply them for inclusion
in a household
demand digital twin 5902. A household demand digital twin development
environment may be
configured to take outputs and outcomes from various applications 630. In
embodiments, a
household demand digital twin 5902 may be provided for the wide range of value
chain network
applications 630 mentioned throughout this disclosure and the documents
incorporated herein by
reference.
[0770] In embodiments, a digital twin 1700 may be generated from other digital
twins. For
example, a customer digital twin 5502 may be used to generate an anonymized
customer digital
twin 5902. The platform may include, integrate, integrate with, manage,
control, coordinate with,
or otherwise handle anonymized customer digital twins 5902. Anonymized
customer digital
twins 5902 may be an anonymized digital representation of a customer 714. In
embodiments,
anonymized customer digital twins 5902 are not populated with personally
identifiable
information but may otherwise be populated using the same data sources as its
corresponding
customer digital twin 5502.
[0771] In embodiments, an environment for development of an anonymized
customer digital
twin 1730 may include a set of interfaces for developers in which a developer
may configure an
artificial intelligence system 1160 to take inputs from selected data sources
of the data storage
layer 624 and events or other data from the monitoring systems layer 614 and
supply them for
inclusion in an anonymized customer digital twin 5902. An anonymized digital
twin
development environment may be configured to take outputs and outcomes from
various
applications 630. In embodiments, an anonymized customer digital twin 5902 may
be provided
for the wide range of value chain network applications 630 mentioned
throughout this disclosure
and the documents incorporated herein by reference.
[0772] In embodiments, the anonymized customer digital twin 5902 comprises an
API that can
receive an access request to the anonymized customer digital twin 5902. A
requesting entity can
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use the API of the anonymized customer digital twin 5902 to issue an access
request. The access
request may be routed from the API to an access logic of the anonymized
customer twin 5902,
which can determine if the requesting entity is entitled to access. In
embodiments, users may
monetize access to anonymized customer digital twins 5902, such as by
subscription or any other
suitable monetization method.
[0773] The platform 604 may include, integrate, integrate with, manage,
control, coordinate
with, or otherwise handle enterprise customer engagement digital twins.
Enterprise customer
engagement digital twins may be a digital representation of a set of
attributes of the enterprise
customer that are relevant to engagement by the customer with a set of
offerings of an enterprise.
[0774] An environment for development of an enterprise customer engagement
digital twin
may include a set of interfaces for developers in which a developer may
configure an artificial
intelligence system 1160 to take inputs from selected data sources of the data
storage layer 624
and events or other data from the monitoring systems layer 614 and supply them
for inclusion in
an enterprise customer engagement digital twin. An enterprise customer
engagement digital twin
development environment may be configured to take outputs and outcomes from
various
applications 630. In embodiments, an enterprise customer engagement digital
twin may be
provided for the wide range of value chain network applications 630 mentioned
throughout this
disclosure and the documents incorporated herein by reference.
[0775] Referring to Fig. 61, the platform 604 may include, integrate,
integrate with, manage,
control, coordinate with, or otherwise handle component digital twins 6002.
Component digital
twins 6002 may represent evolving, continuously updated digital profiles of
components 6002 of
value chain products 650. Component digital twins 6002 may allow for modeling,
simulation,
prediction, decision-making, classification, and the like.
[0776] Product components 6002 can be depicted in a set of one or component
digital twins
6002, such as by populating the component digital twins 6002 with value chain
network data
objects 1004, such as event data 1034, state data 1140, or other data with
respect to value chain
network product components 6002.
[0777] A product 650 may be any category of product, such as a finished good,
software
product, hardware product, component product, material, item of equipment,
consumer packaged
good, consumer product, food product, beverage product, home product, business
supply
product, consumable product, pharmaceutical product, medical device product,
technology
product, entertainment product, or any other type of product and/or set of
related services, and
which may, in embodiments, encompass an intelligent product 650 that is
enabled with a set of
capabilities such as, without limitation data processing, networking, sensing,
autonomous
operation, intelligent agent, natural language processing, speech recognition,
voice recognition,
touch interfaces, remote control, self-organization, self-healing, process
automation,
computation, artificial intelligence, analog or digital sensors, cameras,
sound processing systems,
data storage, data integration, and/or various Internet of Things
capabilities, among others. A
component 6002 may be any category of product component.
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[0778] As an example, a component digital twin 6002 may be populated with
supplier data,
dimension data, material data, thermal data, price data, and the like.
[0779] A component digital twin 6002 may include a set of components,
processes, services,
interfaces, and other elements for development and deployment of digital twin
capabilities for
visualization of value chain network components 714 as well as for coordinated
intelligence
(including artificial intelligence 1160, edge intelligence, analytics and
other capabilities) and
other value-added services and capabilities that are enabled or facilitated
with a component
digital twin 1730.
[0780] In embodiments, the component digital twin 6002 may take advantage of
the presence
of multiple applications 630 within the value chain management platform layer
604, such that a
pair of applications may share data sources (such as in the data storage layer
624) and other
inputs (such as from the monitoring layer 614) that are collected with respect
to value chain
entities 652, as well sharing outputs, events, state information and outputs,
which collectively
may provide a much richer environment for enriching content in a component
digital twin 6002,
including through use of artificial intelligence 1160 (including any of the
various expert systems,
artificial intelligence systems, neural networks, supervised learning systems,
machine learning
systems, deep learning systems, and other systems described throughout this
disclosure and in the
documents incorporated by reference) and through use of content collected by
the monitoring
layer 614 and data collection systems 640.
[0781] An environment for development of a component digital twin 6002 may
include a set of
interfaces for developers in which a developer may configure an artificial
intelligence system
1160 to take inputs from selected data sources of the data storage layer 624
and events or other
data from the monitoring systems layer 614 and supply them for inclusion in a
component digital
twin 6002. A component digital twin development environment may be configured
to take
outputs and outcomes from various applications 630. In embodiments, a
component digital twin
6002 may be provided for the wide range of value chain network applications
630 mentioned
throughout this disclosure and the documents incorporated herein by reference.
In embodiments,
a digital twin 650 may be generated from other digital twins 1700. For
example, a product digital
twin 1780 may be used to generate component digital twins 6002. In another
example,
component digital twins 6002 may be used to generate product digital twins
1780. In
embodiments, a digital twin 1700 may be embedded in another digital twin 1700.
For example, a
component digital twin 6002 may be embedded in a product digital twin 1780
which may be
embedded in an environment digital twin 6004.
[0782] In embodiments, a simulation management system 6110 may set up,
provision,
configure, and otherwise manage interactions and simulations between and among
digital twins
1700 representing value chain entities 652.
[0783] In embodiments, the adaptive intelligent systems layer 614 is
configured to execute
simulations in a simulation management system 6110 using the component digital
twins 6002
and/or digital twins 1700 of other value chain network entities 652. For
example, the adaptive
intelligent systems layer 614 may adjust one or more features of an
environment digital twin
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6004 as a set of component digital twins 6002 are subjected to an environment.
In embodiments,
the adaptive intelligent systems layer 614 may, for each set of features,
execute a simulation
based on the set of features and may collect the simulation outcome data
resulting from the
simulation.
[0784] For example, in executing a simulation on a set of component digital
twins 6002
representing components of value chain product 650 in an environment digital
twin 6004, the
adaptive intelligent systems layer 614 can vary the properties of the
environment digital twin
6110 and can execute simulations that generate outcomes. During the
simulation, the adaptive
intelligent systems layer 614 may vary the environment digital twin
temperature, pressure,
lighting, and/or any other properties of the environment digital twin 6004. In
this example, an
outcome can be a condition of the component digital twin 6002 after being
subjected to a high
temperature. The outcomes from simulations can be used to train machine
learning models 6120.
[0785] In embodiments, a machine learning system 6150 may receive training
data 6170,
outcome data 6160, simulation data 6140, and/or data from other types of
external data sources
6150 (weather data, stock market data, sports event data, news event data, and
the like). In
embodiments, this data may be provided to the machine-learning system 6150 via
an API of the
adaptive intelligent systems layer 614. In embodiments, the machine learning
system 6150 may
receive simulation data 6140 relating to a component digital twin 6002
simulation. In this
example, the simulation data 6140 may be the properties of the component
digital twins 6002 that
were used to perform the simulation and the outcomes resulting therefrom.
[0786] In embodiments, the machine learning system 6150 may train/reinforce
machine leaning
models 6120 using the received data to improve the models.
[0787] Fig. SCDT-2 illustrates an example of a risk management application 818
that interfaces
with the adaptive intelligent systems layer 614. In example embodiments, the
risk management
application 818 may be configured to manage risk or liability with respect to
a good or good
component.
[0788] In embodiments, the machine-learning system 6150 trains one or more
models 6120 that
are utilized by the artificial intelligence system 1160 to make
classifications, predictions, and/or
other decisions relating to risk management, including for products 650 and
product components
6002. In embodiments, may be equipment components. In example embodiments, a
model 6120
is trained to mitigate risk and liability by detecting the condition of a set
of components. The
machine-learning system 6150 may train the models using n-tuples that include
the features
pertaining to components and one or more outcomes associated with the
component condition. In
this example, features for a component 6002 may include, but are not limited
to, component
material (plastic, glass, metal, or the like), component history
(manufacturing dates, usage
history, repair history), component properties, component dimensions,
component thermal
properties, component price, component supplier, and/or other relevant
features. In this example,
outcomes may include whether the digital twin of the component 6002 is in
operating condition.
In this example, one or more properties of the digital twins are varied for
different simulations
and the outcomes of each simulation may be recorded in a tuple with the
proprieties. Other
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examples of training risk management models may include a model 6120 that is
trained to
optimize product safety, a model that is trained to identify components with a
high likelihood of
causing an undesired event, and the like.
[0789] In operation, the artificial intelligence system 1160 may use the above-
discussed models
6120 to make risk management decisions on behalf of a risk management
application 818 given
one or more features relating to a task or event. For example, the artificial
intelligence system
1160 may determine the condition of a component. In this example, the risk
management
application 818 may provide the features of the component to the artificial
intelligence system
1160. These features may include component material, component history,
component
dimensions, component cost, component thermal properties, component supplier,
and the like. In
embodiments, the artificial intelligence system 1160 may feed these features
into one or more of
the models discussed above to obtain one or more decisions. These decisions
may include
whether the component is in operating condition.
[0790] In embodiments, the artificial intelligence system 1160 may leverage
the component
digital twins 6002 to run simulations on the proposed decisions.
[0791] The risk management application 818 may then initiate a component
resupply event
using the decision(s) made by the artificial intelligence system 1160.
Furthermore, after the
component resupply event, the outcomes of the event (e.g., improved product
performance) may
be reported to the machine-learning system 6150 to reinforce the models used
to make the
decisions.
[0792] The platform 604 may include, integrate, integrate with, manage,
control, coordinate
with, or otherwise handle component attribute digital twins 6140. Component
attribute digital
twins 6140 may be a digital representation of a set of attributes of a set of
supply chain
components in a supply for a set of products of an enterprise.
[0793] An environment for development of a component attribute digital twin
6140 may
include a set of interfaces for developers in which a developer may configure
an artificial
intelligence system 1160 to take inputs from selected data sources of the data
storage layer 624
and events or other data from the monitoring systems layer 614 and supply them
for inclusion in
a component attribute digital twin 6140. A component attribute digital twin
development
environment may be configured to take outputs and outcomes from various
applications 630. In
embodiments, a component attribute digital twin 6140 may be provided for the
wide range of
value chain network applications 630 mentioned throughout this disclosure and
the documents
incorporated herein by reference.
[0794] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform with an
asset
management application associated with maritime assets and a data handling
layer of the
management platform including data sources containing information used to
populate a training
set based on a set of maritime activities of one or more of the maritime
assets and one of design
outcomes, parameters, and data associated with the one or more maritime
assets. The information
technology system also has an artificial intelligence system that is
configured to learn on the
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training set collected from the data sources, that simulates one or more
attributes of one or more
of the maritime assets, and that generates one or more sets of recommendations
for a change in
the one or more attributes based on the training set collected from the data
sources. The
information technology system also has a digital twin system included in the
value chain network
management platform that provides for visualization of a digital twin of one
or more of the
maritime assets including detail generated by the artificial intelligence
system of one or more of
the attributes in combination with the one or more sets of recommendations.
[0795] In embodiments, the maritime assets include one or more container
ships. In
embodiments, the digital twin system further provides for visualization of the
digital twin of one
or more of the container ships including one or more of the attributes in
combination with one or
more of the sets of recommendations associated with the container ships.
[0796] In embodiments, the maritime assets include one or more barges. In
embodiments, the
digital twin system further provides for visualization of the digital twin of
one or more of the
barges including one or more of the attributes in combination with one or more
of the sets of
recommendations associated with the barges.
[0797] In embodiments, the maritime assets include one or more components of
the port
infrastructure installed on or adjacent to land. In embodiments, the digital
twin system further
provides for visualization of the digital twin of one or more of the
components of port
infrastructure including one or more of the attributes in combination with one
or more of the sets
of recommendations associated with the components of port infrastructure.
[0798] In embodiments, the maritime assets also include a container ship
moored to a
component of the port infrastructure. In embodiments, the maritime assets
include one or more
moored navigation units deployed on water. In embodiments, the maritime assets
include one or
more ships each connected to a barge.
[0799] In embodiments, the maritime assets are associated with a real-world
maritime port. In
embodiments, the digital twin system further provides for visualization of the
digital twin of one
or more of the components of the real-world maritime port including one or
more of the attributes
in combination with one or more of the sets of recommendations associated with
the components
of the real-world maritime port.
[0800] In embodiments, the maritime assets are associated with a real-world
shipyard In
embodiments, the digital twin system further provides for visualization of the
digital twin of one
or more of the components of the real-world shipyard including one or more of
the attributes in
combination with one or more of the sets of recommendations associated with
the components of
the real-world shipyard.
[0801] In embodiments, the digital twin of one or more of the maritime assets
is a floating asset
twin associated with a ship. In embodiments, the floating asset twin is
configured to provide for
visualization of a navigation course of the ship relative to a planned course
of the ship and one or
more of the sets of recommendations from the artificial intelligence system
for a change in the
navigation course of the ship. In embodiments, the floating asset twin is
configured to provide for
visualization of an engine performance of the ship and one or more of the sets
of
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recommendations from the artificial intelligence system for a change in the
engine performance
of the ship. In embodiments, the visualization of an engine performance
includes an emissions
profile of the ship.
[0802] In embodiments, the floating asset twin is configured to provide for
visualization of a
hull integrity of the ship and one or more of the sets of recommendations from
the artificial
intelligence system for a change in maintenance of the hull of the ship. In
embodiments, the
floating asset twin is configured to provide for visualization of in-situ
hydrodynamic changes to a
portion of a hull disposed below a water line of the ship and one or more of
the sets of
recommendations from the artificial intelligence system for a change in a
hydrodynamic surface
to change performance of the ship. In embodiments, the floating asset twin is
configured to
determine a schedule for the change to the hydrodynamic surface of the hull
disposed below the
waterline of the ship to improve fuel efficiency based on known routes of
travel and weather
patterns. In embodiments, the floating asset twin is configured to provide
visualizations of in-situ
aerodynamic changes to a portion of a hull disposed above a water line of the
ship and one or
more of the sets of recommendations from the artificial intelligence system
for a change in an
aerodynamic surface to change performance of the ship. In embodiments, the
floating asset twin
is configured to determine a schedule for the change to the aerodynamic
surface disposed above
the waterline of the ship to improve fuel efficiency using known routes of
travel and historical
weather patterns.
[0803] In embodiments, the floating asset twin is configured to provide
visualizations of
extendable buoyant members from a hull of the ship to improve stability during
certain
maneuvers of the ship and one or more of the sets of recommendations from the
artificial
intelligence system for a change in the extendable buoyant members to change
performance of
the ship. In embodiments, the floating asset twin is configured to provide
visualizations of a
plurality of inspection points on the ship and maintenance histories
associated with those
inspection points. In embodiments, the floating asset twin is also configured
to provide one or
more of the sets of recommendations from the artificial intelligence system
for a change in
maintenance of the plurality of inspection points. In embodiments, the
floating asset twin is
configured to provide for visualizations of the plurality of inspection points
on the ship affected
by travel within a geofenced area and maintenance histories associated with
those inspection
points. In embodiments, the floating asset twin is also configured to provide
one or more of the
sets of recommendations from the artificial intelligence system for a change
in maintenance of
the plurality of inspection points. In embodiments, the floating asset twin is
configured to provide
details of a ledger of activity associated with the visualization of the
plurality of inspection points
on the ship affected by travel within a geofenced area and maintenance
histories associated with
those inspection points.
[0804] In embodiments, the floating asset twin is configured to provide for
visualization for a
first user of one of a navigation course of the ship and an engine performance
of the ship within a
first geofenced area and for visualization for a second user of one of the
navigation course of the
ship and the engine performance of the ship within a second different
geofenced area and where
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transit between the first and second geofenced areas motivates a handoff of
the floating asset
twin of the ship between the first user and the second user.
[0805] In embodiments, the digital twin is configured to at least partially
represent one or more
of the maritime assets associated with an event investigation and to at least
partially detail a
timeline of the event investigation and associated maritime assets. In
embodiments, the digital
twin is also configured to provide one or more of the sets of recommendations
from the artificial
intelligence system for a change of one of the attributes of the associated
maritime assets.
[0806] In embodiments, the digital twin is configured to at least partially
represent one or more
of the maritime assets associated with a legal proceeding and to at least
partially detail at least a
portion of a timeline pertinent to the legal proceeding and associated
maritime assets. In
embodiments, the digital twin is also configured to provide one or more of the
sets of
recommendations from the artificial intelligence system for a change of one of
the attributes of
the associated maritime assets. In embodiments, the digital twin is configured
to at least partially
represent one or more of the maritime assets associated with a casualty
forecast and to at least
partially detail at least a portion of a timeline pertinent to the casualty
report and associated
maritime assets. In embodiments, the digital twin is also configured to
provide one or more of the
sets of recommendations from the artificial intelligence system for a change
of one of the
attributes of the associated maritime assets to reduce exposure relative to a
set of previous
casualty forecasts.
[0807] In embodiments, the maritime assets include a port infrastructure
facility. In
embodiments, the data collected by a value chain network management platform
facilitates
identifying theft at or misuse of the port infrastructure facility by
correlating data between a set
of data collectors for one or more physical items in the port infrastructure
facility and the digital
twin detailing the one or more physical items of the port infrastructure
facility for the at least one
of the port infrastructure facility and the set of operators.
[0808] In embodiments, the digital twin details the one or more physical items
of the port
infrastructure facility for at least one operator that includes a view of
expected states of at least a
portion of the one or more physical items.
[0809] In embodiments, the maritime assets include a shipyard. In embodiments,
the data
collected by a value chain network management platform facilitates identifying
theft at or misuse
of one or more physical items in the shipyard by correlating data between a
set of data collectors
for the one or more physical items and the digital twin detailing the one or
more physical items of
the shipyard for the at least one of the shipyard and the set of operators. In
embodiments, the
digital twin details the one or more physical items of the shipyard for at
least one operator that
includes a view of expected states of at least a portion of the one or more
physical items.
[0810] In embodiments, the artificial intelligence system determines a set of
geofence
parameters. In embodiments, the digital twin provides further visualization of
at least one
geofence that integrates representation of a set of the maritime assets with a
representation of a
maritime environment adjacent to the geofence. In embodiments, the digital
twin is also
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configured to provide one or more of the sets of recommendations from the
artificial intelligence
system for a change of one of the attributes of the set of maritime assets.
[0811] In embodiments, the maritime assets are ships capable of carrying
cargo. In
embodiments, the artificial intelligence system determines a set of geofence
parameters. In
embodiments, the digital twin provides further visualization of at least one
geofence that
integrates representation of the ships capable of carrying cargo with a
representation of a
maritime environment. In embodiments, the digital twin is also configured to
provide one or
more of the sets of recommendations from the artificial intelligence system
for a change of one of
the attributes of the ships capable of carrying cargo.
[0812] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform including
an asset
management application associated with one or more ships and a data handling
layer of the
management platform including data sources containing information used to
populate a training
set based on a set of maritime activities of one or more of the ships and one
of design outcomes,
parameters, and data associated with the one or more of the ships. The
information technology
system also has an artificial intelligence system that is configured to learn
on the training set
collected from the data sources, that simulates one or more design attributes
of one or more of the
ships, and that generates one or more sets of design recommendations based on
the training set
collected from the data sources. The information technology system also has a
digital twin
system included in the value chain network management platform that provides
for visualization
of a digital twin of one or more of the ships including detail generated by
the artificial
intelligence system of one or more of the design attributes in combination
with the one or more
sets of design recommendations.
[0813] In embodiments, one or more of the ships include one or more container
ships. In
embodiments, the digital twin system further provides for visualization of the
digital twin of one
or more of the container ships including one or more of the attributes in
combination with one or
more of the sets of recommendations associated with the container ships. In
embodiments, one or
more of the container ships are moored to a component of port infrastructure.
In embodiments,
one or more of the ships are connected to a barge. In embodiments, the digital
twin is configured
to provide further visualization of a navigation course relative to a planned
course and one or
more of the sets of recommendations from the artificial intelligence system
for a change in the
navigation course associated with one or more of the ships. In embodiments,
the digital twin is
configured to provide further visualization of an engine performance of one or
more of the ships
and one or more of the sets of recommendations from the artificial
intelligence system for a
change in the engine performance. In embodiments, the visualization of the
engine performance
includes an emissions profile of one or more of the ships.
[0814] In embodiments, the digital twin is configured to provide further
visualization of a hull
integrity of one or more of the ships and one or more of the sets of
recommendations from the
artificial intelligence system for a change in maintenance of a hull of one or
more of the ships. In
embodiments, the digital twin is configured to provide further visualization
of in-situ
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hydrodynamic changes to a portion of a hull disposed below a water line of one
or more of the
ships and one or more of the sets of recommendations from the artificial
intelligence system for a
change in a hydrodynamic surface to change performance of one or more of the
ships. In
embodiments, the digital twin is configured to determine a schedule for the
change to the
hydrodynamic surface of the hull disposed below the waterline of one or more
of the ships to
improve fuel efficiency based on known routes of travel and weather patterns.
In embodiments,
the digital twin is configured to provide further visualization of in-situ
aerodynamic changes to a
portion of a hull disposed above a water line of one or more of the ships and
one or more of the
sets of recommendations from the artificial intelligence system for a change
in an aerodynamic
surface to change performance of one or more of the ships. In embodiments, the
digital twin is
configured to determine a schedule for the change to the aerodynamic surface
disposed above the
waterline of one or more of the ships to improve fuel efficiency using known
routes of travel and
historical weather patterns.
[0815] In embodiments, digital twin is configured to provide further
visualization of extendable
buoyant members from a hull of one or more of the ships to improve stability
during certain
maneuvers and one or more of the sets of recommendations from the artificial
intelligence system
for a change in the extendable buoyant members to change performance of one or
more of the
ships.
[0816] In embodiments, the digital twin is configured to provide further
visualization of a
plurality of inspection points on one or more of the ships and maintenance
histories associated
with those inspection points. In embodiments, the digital twin is also
configured to provide one
or more of the sets of recommendations from the artificial intelligence system
for a change in
maintenance of the plurality of inspection points. In embodiments, the digital
twin is configured
to provide further visualization of the plurality of inspection points on the
ship affected by travel
within a geofenced area and maintenance histories associated with those
inspection points. In
embodiments, the digital twin is also configured to provide one or more of the
sets of
recommendations from the artificial intelligence system for a change in
maintenance of the
plurality of inspection points. In embodiments, the digital twin is configured
to provide details of
a ledger of activity associated with the visualization of the plurality of
inspection points on one or
more of the ships affected by travel within a geofenced area and maintenance
histories associated
with those inspection points.
[0817] In embodiments, the digital twin is configured to provide for
visualization for a first
user of one of a navigation course and an engine performance of one more of
the ships within a
first geofenced area and for visualization for a second user of one of the
navigation course and
the engine performance of one or more the ships within a second different
geofenced area and
where transit between the first and second geofenced areas motivates a handoff
of one or more of
the ships visualized by the digital twin of one or more of the ships between
the first user and the
second user.
[0818] In embodiments, the digital twin is configured to at least partially
represent one or more
of the ships associated with an event investigation and to at least partially
detail a timeline of the
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event investigation and associated ships. In embodiments, the digital twin is
also configured to
provide one or more of the sets of recommendations from the artificial
intelligence system for a
change of one of the attributes of the associated ships. In embodiments, the
digital twin is
configured to at least partially represent one or more of the ships associated
with a legal
proceeding and to at least partially detail at least a portion of a timeline
pertinent to the legal
proceeding and associated ships. In embodiments, the digital twin is also
configured to provide
one or more of the sets of recommendations from the artificial intelligence
system for a change of
one of the attributes of the associated ships.
[0819] In embodiments, the digital twin is configured to at least partially
represent one or more
of the ships associated with a casualty forecast and to at least partially
detail at least a portion of
a timeline pertinent to the casualty report and associated ships. In
embodiments, the digital twin
is also configured to provide one or more of the sets of recommendations from
the artificial
intelligence system for a change of one of the attributes of the associated
ships to reduce
exposure relative to a set of previous casualty forecasts.
[0820] In embodiments, the data collected by a value chain network management
platform
facilitates identifying theft or misuse of physical items at one of the ships
by correlating data
between a set of data collectors for one or more physical items in one of the
ships and the digital
twin detailing one or more of the physical items associated with one of the
ships for the at least
one of the port infrastructure facility and the set of operators. In
embodiments, the digital twin
details the one or more physical items associated with one of the ships for at
least one operator
that includes a view of expected states of at least a portion of the one or
more physical items.
[0821] In embodiments, the artificial intelligence system determines a set of
geofence
parameters. In embodiments, the digital twin provides further visualization of
at least one
geofence that integrates representation of one or more of the ships with a
representation of a
maritime environment adjacent to the geofence. In embodiments, the digital
twin is also
configured to provide one or more of the sets of recommendations from the
artificial intelligence
system for a change of one of the attributes of one or more of the ships.
[0822] In embodiments, one or more of the ships are capable of carrying cargo.
In
embodiments, the artificial intelligence system determines a set of geofence
parameters. In
embodiments, the digital twin provides further visualization of at least one
geofence that
integrates representation of one or more of the ships capable of carrying
cargo with a
representation of a maritime environment. In embodiments, the digital twin is
also configured to
provide one or more of the sets of recommendations from the artificial
intelligence system for a
change of one of the attributes of one or more of the ships capable of
carrying cargo.
[0823] In embodiments, the maritime activities include the forward speed of
one or more of the
ships relative to water and weather conditions based on the parameters
associated with energy
consumption of the propulsion units on one or more of the ships.
[0824] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform for
learning on a training
set of design outcomes, parameters, and data collected from data sources
relating to a set of
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shipping activities to train an artificial intelligence system to simulate
attributes of a container
ship and generate a set of recommendations of changes to the attributes using
a digital twin of the
container ship.
[0825] In embodiments, the container ship is moored to port infrastructure
installed on or
.. adjacent to land. In embodiments, the shipping activities include the
forward speed of the
container ship relative to water and weather conditions based on the
parameters associated with
energy consumption of propulsion units on the container ship. In embodiments,
the information
technology system further includes an asset management application associated
with one or more
maritime facilities connected to the container ship. In embodiments, the asset
management
application is associated with one or more ships connected to barges.
[0826] In embodiments, the digital twin of the container ship provides for
visualization of a
navigation course of the container ship. In embodiments, the digital twin of
the container ship
provides for visualization of an engine performance of the container ship. In
embodiments, the
digital twin of the container ship provides for visualization of a hull
integrity of the container
ship. In embodiments, the digital twin of the container ship provides for
visualization of in-situ
hydrodynamic changes to a portion of a hull disposed below a water line of the
container ship. In
embodiments, the digital twin of the container ship determines a schedule of
the in-situ
hydrodynamic changes to the portion of the hull disposed below the waterline
of the container
ship to improve fuel efficiency using known routes of travel and historical
weather patterns. In
embodiments, the digital twin of the container ship provides for visualization
of in-situ
aerodynamic changes to a portion of a hull disposed above a water line of the
container ship. In
embodiments, the digital twin of the container ship determines a schedule of
in-situ aerodynamic
changes to the portion of the hull disposed above the waterline of the
container ship to improve
fuel efficiency using known routes of travel and historical weather patterns.
[0827] In embodiments, the digital twin of the container ship provides for
visualization of
extendable buoyant members from a hull of the container ship to improve
stability during certain
maneuvers of the container ship. In embodiments, the digital twin of the
container ship provides
for visualization of extendable buoyant members from a hull of the container
ship to improve
stability during certain maneuvers of the container ship.
[0828] In embodiments, the digital twin of the container ship provides for
visualization of a
plurality of inspection points on the container ship and maintenance histories
associated with
those inspection points. In embodiments, the digital twin of the container
ship provides for the
visualization of the plurality of inspection points on the container ship
affected by travel within a
geofenced area and maintenance histories associated with those inspection
points when
maintenance follows travel through the geofenced area. In embodiments, the
digital twin of the
container ship provides for details of a ledger of activity associated with
the visualization of the
plurality of inspection points on the container ship affected by travel within
a geofenced area and
maintenance histories associated with those inspection points when maintenance
follows travel
through the geofenced area.
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[0829] In embodiments, the digital twin of the container ship provides for
visualization for a
first user of one of a navigation course of the container ship and an engine
performance of the
container ship within a first geofenced area and for visualization for a
second user of one of the
navigation course of the container ship and the engine performance of the
container ship within a
second geofenced area and where transit between the first and second geofenced
areas motivates
a handoff of the digital twin of the container ship between the first user and
the second user.
[0830] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform including
an asset
management application associated with one or more barges and a data handling
layer of the
management platform including data sources containing information used to
populate a training
set based on a set of maritime activities of one or more of the barges and one
of design outcomes,
parameters, and data associated with the one or more of the barges. The
information technology
system also has an artificial intelligence system that is configured to learn
on the training set
collected from the data sources, that simulates one or more design attributes
of one or more of the
barges, and that generates one or more sets of design recommendations based on
the training set
collected from the data sources. The information technology system also has a
digital twin
system included in the value chain network management platform that provides
for visualization
of a digital twin of one or more of the barges including detail generated by
the artificial
intelligence system of one or more of the design attributes in combination
with the one or more
sets of design recommendations.
[0831] In embodiments, the digital twin system further provides for
visualization of the digital
twin of one or more of the barges including one or more of the attributes in
combination with one
or more of the sets of recommendations associated with the barges. In
embodiments, one of the
barges is connected to a ship. In embodiments, the digital twin is configured
to provide for
visualization of a navigation course of one of the barges relative to a
planned course of one of the
barges and one or more of the sets of recommendations from the artificial
intelligence system for
a change in the navigation course of one of the barges.
[0832] In embodiments, the digital twin is configured to provide for
visualization of a hull
integrity of one of the barges relative to a planned course of one of the
barges and one or more of
the sets of recommendations from the artificial intelligence system for a
change in maintenance
of the hull of one of the barges.
[0833] In embodiments, the digital twin is configured to provide for
visualization of in-situ
hydrodynamic changes to a portion of a hull disposed below a water line of one
or more of the
barges and one or more of the sets of recommendations from the artificial
intelligence system for
a change in a hydrodynamic surface to change performance of one or more of the
barges. In
embodiments, the digital twin is configured to determine a schedule for the
change to the
hydrodynamic surface of the hull disposed below the waterline of one or more
of the barges to
improve fuel efficiency based on known routes of travel and weather patterns.
[0834] In embodiments, the digital twin is configured to provide
visualizations of extendable
buoyant members from a hull of one or more of the barges to improve stability
during certain
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maneuvers of one or more of the barges and one or more of the sets of
recommendations from the
artificial intelligence system for a change in the extendable buoyant members
to change
performance of one or more of the barges. In embodiments, the digital twin is
configured to
provide visualizations of a plurality of inspection points on one or more of
the barges and
maintenance histories associated with those inspection points. In embodiments,
the digital twin is
also configured to provide one or more of the sets of recommendations from the
artificial
intelligence system for a change in maintenance of the plurality of inspection
points. In
embodiments, the digital twin is configured to provide for visualizations of
the plurality of
inspection points on one or more of the barges affected by travel within a
geofenced area and
maintenance histories associated with those inspection points. In embodiments,
the digital twin is
also configured to provide one or more of the sets of recommendations from the
artificial
intelligence system for a change in maintenance of the plurality of inspection
points. In
embodiments, the digital twin is configured to provide details of a ledger of
activity associated
with the visualization of the plurality of inspection points on one or more of
the barges affected
by travel within a geofenced area and maintenance histories associated with
those inspection
points.
[0835] In embodiments, the digital twin is configured to provide for
visualization for a first
user of one of a navigation course of one or more of the barges within a first
geofenced area and
for visualization for a second user of one of the navigation course of one or
more of the barges
within a second different geofenced area and where transit between the first
and second
geofenced areas motivates a handoff of the digital twin of one or more of the
barges between the
first user and the second user. In embodiments, the digital twin is configured
to at least partially
represent one or more of the barges associated with an event investigation and
to at least partially
detail a timeline of the event investigation and associated maritime assets.
In embodiments, the
digital twin is also configured to provide one or more of the sets of
recommendations from the
artificial intelligence system for a change of one of the attributes of the
associated barges.
[0836] In embodiments, the digital twin is configured to at least partially
represent one or more
of the barges associated with a legal proceeding and to at least partially
detail at least a portion of
a timeline pertinent to the legal proceeding and associated barges. In
embodiments, the digital
twin is also configured to provide one or more of the sets of recommendations
from the artificial
intelligence system for a change of one of the attributes of the associated
barges. In
embodiments, the digital twin is configured to at least partially represent
one or more of the
barges associated with a casualty forecast and to at least partially detail at
least a portion of a
timeline pertinent to the casualty report and associated barges. In
embodiments, the digital twin is
also configured to provide one or more of the sets of recommendations from the
artificial
intelligence system for a change of one of the attributes of the associated
barges to reduce
exposure relative to a set of previous casualty forecasts.
[0837] In embodiments, the data collected by a value chain network management
platform
facilitates identifying theft or misuse of physical items at on one of the
barges by correlating data
between a set of data collectors for one or more physical items on one of the
barges and the
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digital twin detailing the one or more physical items on one of the barges for
at least one of a port
infrastructure facility and a set of operators. In embodiments, the digital
twin details the one or
more physical items on of the barges for at least one operator that includes a
view of expected
states of at least a portion of the one or more physical items. In
embodiments, the artificial
intelligence system determines a set of geofence parameters. In embodiments,
the digital twin
provides further visualization of at least one geofence that integrates
representation of one or
more of the barges with a representation of a maritime environment adjacent to
the geofence. In
embodiments, digital twin is also configured to provide one or more of the
sets of
recommendations from the artificial intelligence system for a change of one of
the attributes of
the set of one or more of the barges.
[0838] In embodiments, the asset management application is associated with one
or more ships
connected to one of the barges. In embodiments, the data handling layer of the
management
platform includes data sources containing information used to populate the
training set based on a
set of maritime activities of one or more of the barges underway and each
connected to a ship and
one of design outcomes, parameters, and data associated with the one or more
of the barges and
its associated ship.
[0839] In embodiments, the artificial intelligence system is configured to
learn on the training
set collected from the data sources and to simulate one or more design
attributes of one or more
of the barges each connected to a ship. In embodiments, the digital twin
system provides for
visualization of a digital twin of one or more of the barges and each of the
ships to which they are
connected.
[0840] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform for
learning on a training
set of design outcomes, parameters, and data collected from data sources
relating to a set of
shipping activities to train an artificial intelligence system to simulate
attributes of a barge and
generate a set of recommendations of changes to the attributes using a digital
twin of the barge.
[0841] In embodiments, the digital twin system further provides for
visualization of the digital
twin of one or more of the barges including one or more of the attributes in
combination with one
or more of the sets of recommendations of changes to the attributes associated
with the barges. In
embodiments, one of the barges is connected to a ship. In embodiments, the
digital twin is
configured to provide for visualization of a navigation course of one of the
barges relative to a
planned course of one of the barges and one or more of the sets of
recommendations from the
artificial intelligence system for a change in the navigation course of one of
the barges.
[0842] In embodiments, the digital twin is configured to provide for
visualization of a hull
integrity of one of the barges relative to a planned course of one of the
barges and one or more of
the sets of recommendations from the artificial intelligence system for a
change in maintenance
of the hull of one of the barges. In embodiments, digital twin is configured
to provide for
visualization of in-situ hydrodynamic changes to a portion of a hull disposed
below a water line
of one or more of the barges and one or more of the sets of recommendations
from the artificial
intelligence system for a change in a hydrodynamic surface to change
performance of one or
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more of the barges. In embodiments, the digital twin is configured to
determine a schedule for the
change to the hydrodynamic surface of the hull disposed below the waterline of
one or more of
the barges to improve fuel efficiency based on known routes of travel and
weather patterns.
[0843] In embodiments, the digital twin is configured to provide
visualizations of extendable
buoyant members from a hull of one or more of the barges to improve stability
during certain
maneuvers of one or more of the barges and one or more of the sets of
recommendations from the
artificial intelligence system for a change in the extendable buoyant members
to change
performance of one or more of the barges. In embodiments, the digital twin is
configured to
provide visualizations of a plurality of inspection points on one or more of
the barges and
maintenance histories associated with those inspection points. In embodiments,
the digital twin is
also configured to provide one or more of the sets of recommendations from the
artificial
intelligence system for a change in maintenance of the plurality of inspection
points. In
embodiments, the digital twin is configured to provide for visualizations of
the plurality of
inspection points on one or more of the barges affected by travel within a
geofenced area and
maintenance histories associated with those inspection points. In embodiments,
the digital twin is
also configured to provide one or more of the sets of recommendations from the
artificial
intelligence system for a change in maintenance of the plurality of inspection
points. In
embodiments, the digital twin is configured to provide details of a ledger of
activity associated
with the visualization of the plurality of inspection points on one or more of
the barges affected
by travel within a geofenced area and maintenance histories associated with
those inspection
points.
[0844] In embodiments, the digital twin is configured to provide for
visualization for a first
user of one of a navigation course of one or more of the barges within a first
geofenced area and
for visualization for a second user of one of the navigation course of one or
more of the barges
within a second different geofenced area and where transit between the first
and second
geofenced areas motivates a handoff of the digital twin of one or more of the
barges between the
first user and the second user. In embodiments, the digital twin is configured
to at least partially
represent one or more of the barges associated with an event investigation and
to at least partially
detail a timeline of the event investigation and associated maritime assets.
In embodiments, the
digital twin is also configured to provide one or more of the sets of
recommendations from the
artificial intelligence system for a change of one of the attributes of the
associated barges.
[0845] In embodiments, the digital twin is configured to at least partially
represent one or more
of the barges associated with a legal proceeding and to at least partially
detail at least a portion of
a timeline pertinent to the legal proceeding and associated barges. In
embodiments, the digital
twin is also configured to provide one or more of the sets of recommendations
from the artificial
intelligence system for a change of one of the attributes of the associated
barges. In
embodiments, the digital twin is configured to at least partially represent
one or more of the
barges associated with a casualty forecast and to at least partially detail at
least a portion of a
timeline pertinent to the casualty report and associated barges. In
embodiments, the digital twin is
also configured to provide one or more of the sets of recommendations from the
artificial
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intelligence system for a change of one of the attributes of the associated
barges to reduce
exposure relative to a set of previous casualty forecasts.
[0846] In embodiments, the data collected by a value chain network management
platform
facilitates identifying theft or misuse of physical items on one of the barges
by correlating data
between a set of data collectors for one or more physical items on one of the
barges and the
digital twin detailing the one or more physical items on one of the barges for
at least one of a port
infrastructure facility and a set of operators. In embodiments, the digital
twin details the one or
more physical items on of the barges for at least one operator that includes a
view of expected
states of at least a portion of the one or more physical items.
[0847] In embodiments, the artificial intelligence system determines a set of
geofence
parameters. In embodiments, the digital twin provides further visualization of
at least one
geofence that integrates representation of one or more of the barges with a
representation of a
maritime environment adjacent to the geofence. In embodiments, the digital
twin is also
configured to provide one or more of the sets of recommendations from the
artificial intelligence
system for a change of one of the attributes of the set of one or more of the
barges.
[0848] In embodiments, the asset management application is associated with one
or more ships
connected to one of the barges. In embodiments, the data handling layer of the
management
platform includes data sources containing information used to populate the
training set based on a
set of maritime activities of one or more of the barges underway and each
connected to a ship and
one of design outcomes, parameters, and data associated with the one or more
of the barges and
its associated ship. In embodiments, the artificial intelligence system is
configured to learn on the
training set collected from the data sources and to simulate one or more
design attributes of one
or more of the barges each connected to a ship. In embodiments, the digital
twin system provides
for visualization of a digital twin of one or more of the barges and each of
the ships to which they
are connected.
[0849] In embodiments, the methods, systems and apparatuses includes an
information
technology system having a value chain network management platform including
an asset
management application associated with port infrastructure and a data handling
layer of the
management platform including data sources containing information used to
populate a training
set based on a set of maritime activities around the port infrastructure and
one of design
outcomes, parameters, and data associated with the port infrastructure. The
information
technology system also has an artificial intelligence system that is
configured to learn on the
training set collected from the data sources, that simulates one or more
attributes of the port
infrastructure, and that generates one or more sets of recommendations for a
change in the one or
more attributes based on the training set collected from the data sources. The
information
technology system also has a digital twin system included in the value chain
network
management platform that provides for visualization of a digital twin of the
port infrastructure
including detail generated by the artificial intelligence system of one or
more of the attributes in
combination with the one or more sets of recommendations.
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[0850] In embodiments, the digital twin system further provides for
visualization of the digital
twin of one or more of container ships in the port infrastructure including
one or more of the
attributes in combination with one or more of the sets of recommendations
associated with one or
more of the container ships.
[0851] In embodiments, the digital twin system further provides for
visualization of the digital
twin of one or more of barges in the port infrastructure including one or more
of the attributes in
combination with one or more of the sets of recommendations associated with
one or more of the
barges. In embodiments, the port infrastructure includes one or more moored
navigation units
deployed on water. In embodiments, the port infrastructure includes one or
more ships each
connected to a barge. In embodiments, the port infrastructure is associated
with a real-world
maritime port. In embodiments, the digital twin system further provides for
visualization of the
digital twin of one or more of the components of the real-world maritime port
including one or
more of the attributes in combination with one or more of the sets of
recommendations associated
with the components of the real-world maritime port.
[0852] In embodiments, the port infrastructure is associated with a real-world
shipyard. In
embodiments, the digital twin system further provides for visualization of the
digital twin of one
or more of the components of the real-world shipyard including one or more of
the attributes in
combination with one or more of the sets of recommendations associated with
the components of
the real-world shipyard.
[0853] In embodiments, the digital twin is configured to provide for
visualization of an engine
performance of the port infrastructure and one or more of the sets of
recommendations from the
artificial intelligence system for a change in the engine performance
installed in the port
infrastructure. In embodiments, the visualization of an engine performance
includes an emissions
profile. In embodiments, the digital twin is configured to provide
visualizations of a plurality of
inspection points on the port infrastructure and maintenance histories
associated with those
inspection points. In embodiments, the digital twin is also configured to
provide one or more of
the sets of recommendations from the artificial intelligence system for a
change in maintenance
of the plurality of inspection points. In embodiments, the digital twin is
configured to provide for
visualizations of the plurality of inspection points on the port
infrastructure includes within a
geofenced area and maintenance histories associated with those inspection
points. In
embodiments, the digital twin is also configured to provide one or more of the
sets of
recommendations from the artificial intelligence system for a change in
maintenance of the
plurality of inspection points. In embodiments, the digital twin is configured
to provide details of
a ledger of activity associated with the visualization of the plurality of
inspection points on the
port infrastructure includes within a geofenced area and maintenance histories
associated with
those inspection points.
[0854] In embodiments, the digital twin is configured to at least partially
represent the port
infrastructure associated with an event investigation and to at least
partially detail a timeline of
the event investigation. In embodiments, the digital twin is also configured
to provide one or
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more of the sets of recommendations from the artificial intelligence system
for a change of one of
the attributes of the associated port infrastructure.
[0855] In embodiments, the digital twin is configured to at least partially
represent the port
infrastructure associated with a legal proceeding and to at least partially
detail at least a portion
of a timeline pertinent to the legal proceeding. In embodiments, the digital
twin is also
configured to provide one or more of the sets of recommendations from the
artificial intelligence
system for a change of one of the attributes of the associated port
infrastructure.
[0856] In embodiments, the digital twin is configured to at least partially
represent the port
infrastructure associated with a casualty forecast and to at least partially
detail at least a portion
of a timeline pertinent to the casualty report. In embodiments, the digital
twin is also configured
to provide one or more of the sets of recommendations from the artificial
intelligence system for
a change of one of the attributes of the associated port infrastructure to
reduce exposure relative
to a set of previous casualty forecasts.
[0857] In embodiments, the data collected by a value chain network management
platform
facilitates identifying theft at or misuse at the port infrastructure by
correlating data between a set
of data collectors for one or more physical items at the port infrastructure
and the digital twin
detailing the one or more physical items of the port infrastructure for the at
least one of a facility
at the port infrastructure and the set of operators. In embodiments, the
digital twin details the one
or more physical items at the port infrastructure for at least one operator
that includes a view of
expected states of at least a portion of the one or more physical items.
[0858] In embodiments, the data collected by a value chain network management
platform
facilitates identifying theft at or misuse of one or more physical items at
the port infrastructure by
correlating data between a set of data collectors for the one or more physical
items and the digital
twin detailing the one or more physical items at the port infrastructure
includes for the at least
.. one of a facility at the port infrastructure and the set of operators. In
embodiments, the digital
twin details the one or more physical items at the port infrastructure for at
least one operator that
includes a view of expected states of at least a portion of the one or more
physical items.
[0859] In embodiments, the artificial intelligence system determines a set of
geofence
parameters. In embodiments, the digital twin provides further visualization of
at least one
geofence that integrates representation of at least a portion of the port
infrastructure with a
representation of a maritime environment adjacent to the geofence. In
embodiments, the digital
twin is also configured to provide one or more of the sets of recommendations
from the artificial
intelligence system for a change of one of the attributes of the port
infrastructure.
[0860] In embodiments, one or more components of the port infrastructure are
installed on
land. In embodiments, the one or more components of the port infrastructure
include one or more
moored navigation units deployed on water. In embodiments, the methods,
systems and
apparatuses include an information technology system having a value chain
network
management platform for learning on a training set of design outcomes,
parameters, and data
collected from data sources relating to a set of shipping activities to train
an artificial intelligence
system to simulate design attributes of a port infrastructure facility and
generate a set of design
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recommendations using a digital twin of the port infrastructure facility. In
embodiments, the
digital twin system further provides for visualization of the digital twin of
the port infrastructure
facility including one or more of the attributes in combination with one or
more of the sets of
recommendations of changes to the attributes associated with the port
infrastructure facility.
[0861] In embodiments, the digital twin is configured to provide
visualizations of a plurality of
inspection points on the port infrastructure facility and maintenance
histories associated with
those inspection points. In embodiments, the digital twin is also configured
to provide one or
more of the sets of recommendations from the artificial intelligence system
for a change in
maintenance of the plurality of inspection points. In embodiments, the digital
twin is also
configured to provide one or more of the sets of recommendations from the
artificial intelligence
system for a change in maintenance of the plurality of inspection points. In
embodiments, the
digital twin is configured to provide details of a ledger of activity
associated with the
visualization of the plurality of inspection points on the port infrastructure
facility within a
geofenced area and maintenance histories associated with those inspection
points.
[0862] In embodiments, the digital twin is configured to at least partially
represent at least a
portion of the port infrastructure facility associated with an event
investigation and to at least
partially detail a timeline of the event investigation and associated with the
port infrastructure
facility. In embodiments, the digital twin is also configured to provide one
or more of the sets of
recommendations from the artificial intelligence system for a change of one of
the attributes of
the port infrastructure facility.
[0863] In embodiments, the digital twin is configured to at least partially
represent at least a
portion of the port infrastructure facility associated with a legal proceeding
and to at least
partially detail at least a portion of a timeline pertinent to the legal
proceeding and associated
with the port infrastructure facility. In embodiments, the digital twin is
also configured to provide
one or more of the sets of recommendations from the artificial intelligence
system for a change of
one of the attributes of the associated port infrastructure facility
[0864] In embodiments, the digital twin is configured to at least partially
represent at least a
portion of the port infrastructure facility associated with a casualty
forecast and to at least
partially detail at least a portion of a timeline pertinent to the casualty
report and associated port
infrastructure facility. In embodiments, the digital twin is also configured
to provide one or more
of the sets of recommendations from the artificial intelligence system for a
change of one of the
attributes of at least a portion of the port infrastructure facility to reduce
exposure relative to a set
of previous casualty forecasts.
[0865] In embodiments, the data collected by a value chain network management
platform
facilitates identifying theft or misuse of physical items in at least a
portion of the port
infrastructure facility by correlating data between a set of data collectors
for one or more physical
items in at least a portion of the port infrastructure facility and the
digital twin detailing the one
or more physical items in at least a portion of the port infrastructure
facility for at least one of the
port infrastructure facility and a set of operators. In embodiments, the
digital twin details the one
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or more physical items in the port infrastructure facility for at least one
operator that includes a
view of expected states of at least a portion of the one or more physical
items.
[0866] In embodiments, the artificial intelligence system determines a set of
geofence
parameters. In embodiments, the digital twin provides further visualization of
at least one
geofence that integrates representation of at least a portion of the port
infrastructure facility with
a representation of a maritime environment adjacent to the geofence. In
embodiments, the digital
twin is also configured to provide one or more of the sets of recommendations
from the artificial
intelligence system for a change of one of the attributes of at least a
portion of the port
infrastructure facility.
[0867] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform including
an asset
management application associated with maritime assets involved in a maritime
event and a data
handling layer of the management platform including data sources containing
information used to
populate a training set based on a set of maritime activities of the maritime
assets involved in the
maritime event and one of design outcomes, parameters, and data associated
with the maritime
assets involved in the maritime event. The information technology system also
has an artificial
intelligence system that is configured to learn on the training set collected
from the data sources,
that simulates one or more design attributes of the maritime assets involved
in a maritime event,
and that generates one or more sets of design recommendations based on the
training set
collected from the data sources. The information technology system also has a
digital twin
system included in the value chain network management platform that provides
for visualization
of a digital twin of the maritime assets involved in a maritime event
including detail generated by
the artificial intelligence system of one or more of the design attributes in
combination with the
one or more sets of design recommendations applicable to at least one of the
maritime assets
involved in the maritime event.
[0868] In embodiments, the maritime assets include one or more container ships
involved in the
maritime event. In embodiments, the digital twin system further provides for
visualization of the
digital twin of one or more of the container ships including one or more of
the attributes in
combination with one or more of the sets of recommendations associated with
the container
ships.
[0869] In embodiments, the maritime assets include one or more barges involved
in the
maritime event. In embodiments, the digital twin system further provides for
visualization of the
digital twin of one or more of the barges including one or more of the
attributes in combination
with one or more of the sets of recommendations associated with the barges.
[0870] In embodiments, the maritime assets include one or more components of
port
infrastructure involved in the maritime event. In embodiments, the digital
twin system further
provides for visualization of the digital twin of one or more of the
components of port
infrastructure including one or more of the attributes in combination with one
or more of the sets
of recommendations associated with the components of port infrastructure.
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[0871] In embodiments, the maritime assets are associated with a real-world
maritime port. In
embodiments, the digital twin system further provides for visualization of the
digital twin of one
or more of the components of the real-world maritime port involved in the
maritime event
including one or more of the attributes in combination with one or more of the
sets of
recommendations associated with the components of the real-world maritime
port.
[0872] In embodiments, the maritime assets are associated with a real-world
shipyard In
embodiments, the digital twin system further provides for visualization of the
digital twin of one
or more of the components of the real-world shipyard involved in the maritime
event including
one or more of the attributes in combination with one or more of the sets of
recommendations
associated with the components of the real-world shipyard.
[0873] In embodiments, the digital twin of one or more of the maritime assets
is a floating asset
twin associated with a ship. In embodiments, the floating asset twin is
configured to provide for
visualization of a navigation course of the ship involved in the maritime
event relative to a
planned course of the ship and one or more of the sets of recommendations from
the artificial
intelligence system for a change in the navigation course of the ship. In
embodiments, the
floating asset twin is configured to provide for visualization of an engine
performance of the ship
involved in the maritime event and one or more of the sets of recommendations
from the artificial
intelligence system for a change in the engine performance of the ship. In
embodiments, the
visualization of an engine performance includes an emissions profile of the
ship. In
embodiments, the floating asset twin is configured to provide for
visualization of a hull integrity
of the ship involved in the maritime event and one or more of the sets of
recommendations from
the artificial intelligence system for a change in maintenance of the hull of
the ship. In
embodiments, the floating asset twin is configured to provide visualizations
of a plurality of
inspection points on the ship involved in the maritime event and maintenance
histories associated
with those inspection points. In embodiments, the floating asset twin is also
configured to
provide one or more of the sets of recommendations from the artificial
intelligence system for a
change in maintenance of the plurality of inspection points associated with
the maritime event. In
embodiments, the floating asset twin is configured to provide for
visualizations of the plurality of
inspection points on the ship affected by travel within a geofenced area and
maintenance histories
associated with those inspection points. In embodiments, the floating asset
twin is also
configured to provide one or more of the sets of recommendations from the
artificial intelligence
system for a change in maintenance of the plurality of inspection points
associated with the
maritime event. In embodiments, the floating asset twin is configured to
provide details of a
ledger of activity associated with the visualization of the plurality of
inspection points on the ship
involved in the maritime event within a geofenced area and maintenance
histories associated with
those inspection points.
[0874] In embodiments, the artificial intelligence system determines a set of
geofence
parameters. In embodiments, the digital twin provides further visualization of
at least one
geofence that integrates representation of a set of the maritime assets
involved in the maritime
event with a representation of a maritime environment adjacent to the
geofence. In embodiments,
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the digital twin is also configured to provide one or more of the sets of
recommendations from
the artificial intelligence system for a change of one of the attributes of
the set of maritime assets
involved in the maritime event. In embodiments, the methods, systems and
apparatuses include
an information technology system having a value chain network management
platform for
learning on a training set of maritime event outcomes, parameters, and data
collected from data
sources to train an artificial intelligence system to use a digital twin to
facilitate investigation of a
maritime event.
[0875] In embodiments, the maritime event outcomes are associated with a real-
world shipyard.
In embodiments, the digital twin is configured to detail at least a portion of
the real-world
shipyard to facilitate investigation of the maritime event. In embodiments,
the maritime event
outcomes are associated with a real-world maritime port. In embodiments, the
digital twin is
configured to detail at least a portion of the real-world maritime port to
facilitate investigation of
the maritime event.
[0876] In embodiments, the maritime event outcomes are associated with one or
more container
ships. In embodiments, the digital twin is configured to detail one or more of
the container ships
to facilitate investigation of the maritime event. In embodiments, the
maritime event outcomes
are associated with one or more barges. In embodiments, the digital twin is
configured to detail
one or more of the barges to facilitate investigation of the maritime event.
[0877] In embodiments, the maritime event outcomes are associated with at
least a portion of
port infrastructure. In embodiments, the digital twin is configured to detail
at least a portion of
the port infrastructure to facilitate investigation of the maritime event. In
embodiments, the
digital twin is configured to at least partially represent activity of one or
more maritime value
chain network entities during a timeline associated with the maritime event.
In embodiments, the
one or more maritime value chain network entities are associated with a legal
proceeding. In
embodiments, the digital twin is further configured to at least partially
represent activity of one or
more maritime value chain network entities during a timeline associated with
the legal
proceeding. In embodiments, the one or more maritime value chain network
entities are
associated with a legal proceeding. In embodiments, the digital twin is
further configured to at
least partially represent activity of one or more maritime value chain network
entities during a
timeline associated with the legal proceeding.
[0878] In embodiments, the one or more maritime value chain network entities
are associated
with a casualty forecast. In embodiments, the digital twin is further
configured to at least partially
represent activity of one or more maritime value chain network entities during
a timeline
associated with the casualty forecast. In embodiments, one or more of the
maritime value chain
network entities is a port infrastructure facility. In embodiments, the data
collected by the value
chain network management platform facilitates identifying theft or misuse of
one or more
physical items of the port infrastructure facility by correlating data between
a set of data
collectors for one or more of the physical items in the port infrastructure
facility and the digital
twin detailing one or more of the physical items of the port infrastructure
facility for the at least
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one of the port infrastructure facility and the set of operators to further
facilitate investigation of
the maritime event.
[0879] In embodiments, the maritime event includes a container ship that is
moored to port
infrastructure installed on or adjacent to land. In embodiments, the maritime
event includes at
least a container ship having a forward speed relative to water and weather
conditions and
parameters associated with energy consumption of propulsion units on the
container ship.
[0880] In embodiments, the maritime event includes one or more ships connected
to barges. In
embodiments, the maritime event includes one or more ships. In embodiments,
the digital twin
provides for visualization of a navigation course of one or more of the ships
during the maritime
event. In embodiments, the maritime event includes one or more ships. In
embodiments, the
digital twin provides for visualization of an engine performance of one or
more of the ships
during the maritime event. In embodiments, the maritime event includes one or
more ships. In
embodiments, the digital twin provides for visualization of a hull integrity
of one or more of the
ships involved in the maritime event.
[0881] In embodiments, the maritime event includes one or more ships. In
embodiments, the
digital twin provides for visualization of a plurality of inspection points
associated with one or
more of the ships and maintenance histories associated with those inspection
points.
[0882] In embodiments, the digital twin further provides for the visualization
of the plurality of
inspection points associated with one or more of the ships within a geofenced
area related to the
maritime event and maintenance histories associated with those inspection
points. In
embodiments, the digital twin further provides for details of a ledger of
activity associated with
the visualization of the plurality of inspection points associated with one or
more of the ships
within a geofenced area related to the maritime event and maintenance
histories associated with
those inspection points.
[0883] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform including
an asset
management application associated with maritime assets involved in a maritime
legal proceeding
and a data handling layer of the management platform including data sources
containing
information used to populate a training set based on a set of maritime
activities of the maritime
assets involved in the maritime legal proceeding and one of parameters and
data associated with
the maritime assets involved in the maritime legal proceeding. The information
technology
system also has an artificial intelligence system that is configured to learn
on the training set
collected from the data sources, that simulates one or more attributes of one
or more of the
maritime assets involved in the maritime legal proceeding, and that generates
one or more sets of
recommendations for a change in the one or more attributes based on the
training set collected
from the data sources. The information technology system also has a digital
twin system included
in the value chain network management platform that provides for visualization
of a digital twin
of one or more of the maritime assets involved in the maritime legal
proceeding including detail
generated by the artificial intelligence system of one or more of the
attributes in combination
with the one or more sets of recommendations.
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[0884] In embodiments, the maritime assets include one or more container ships
involved in the
maritime legal proceeding. In embodiments, the digital twin system further
provides for
visualization of the digital twin of one or more of the container ships
including one or more of
the attributes in combination with one or more of the sets of recommendations
associated with
the container ships.
[0885] In embodiments, the maritime assets include one or more barges involved
in the
maritime legal proceeding. In embodiments, the digital twin system further
provides for
visualization of the digital twin of one or more of the barges including one
or more of the
attributes in combination with one or more of the sets of recommendations
associated with the
barges.
[0886] In embodiments, the maritime assets include one or more components of
port
infrastructure involved in the maritime legal proceeding. In embodiments, the
digital twin system
further provides for visualization of the digital twin of one or more of the
components of port
infrastructure including one or more of the attributes in combination with one
or more of the sets
of recommendations associated with the components of port infrastructure.
[0887] In embodiments, the maritime assets are associated with a real-world
maritime port. In
embodiments, the digital twin system further provides for visualization of the
digital twin of one
or more of the components of the real-world maritime port involved in the
maritime legal
proceeding including one or more of the attributes in combination with one or
more of the sets of
recommendations associated with the components of the real-world maritime
port.
[0888] In embodiments, the maritime assets are associated with a real-world
shipyard. In
embodiments, the digital twin system further provides for visualization of the
digital twin of one
or more of the components of the real-world shipyard involved in the maritime
legal proceeding
including one or more of the attributes in combination with one or more of the
sets of
recommendations associated with the components of the real-world shipyard.
[0889] In embodiments, the digital twin of one or more of the maritime assets
is a floating asset
twin associated with a ship. In embodiments, the floating asset twin is
configured to provide for
visualization of a navigation course of the ship involved in the maritime
legal proceeding relative
to a planned course of the ship and one or more of the sets of recommendations
from the artificial
intelligence system for a change in the navigation course of the ship. In
embodiments, the
floating asset twin is configured to provide for visualization of an engine
performance of the ship
involved in the maritime legal proceeding and one or more of the sets of
recommendations from
the artificial intelligence system for a change in the engine performance of
the ship.
[0890] In embodiments, the visualization of an engine performance includes an
emissions
profile of the ship. In embodiments, the floating asset twin is configured to
provide for
visualization of a hull integrity of the ship involved in the maritime legal
proceeding and one or
more of the sets of recommendations from the artificial intelligence system
for a change in
maintenance of the hull of the ship. In embodiments, the floating asset twin
is configured to
provide visualizations of a plurality of inspection points on the ship
involved in the maritime
legal proceeding and maintenance histories associated with those inspection
points. In
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embodiments, the floating asset twin is also configured to provide one or more
of the sets of
recommendations from the artificial intelligence system for a change in
maintenance of the
plurality of inspection points associated with the maritime event. In
embodiments, the floating
asset twin is configured to provide for visualizations of the plurality of
inspection points on the
ship affected by travel within a geofenced area and maintenance histories
associated with those
inspection points. In embodiments, the floating asset twin is also configured
to provide one or
more of the sets of recommendations from the artificial intelligence system
for a change in
maintenance of the plurality of inspection points associated with the maritime
event. In
embodiments, the floating asset twin is configured to provide details of a
ledger of activity
associated with the visualization of the plurality of inspection points on the
ship involved in the
maritime legal proceeding within a geofenced area and maintenance histories
associated with
those inspection points.
[0891] In embodiments, the artificial intelligence system determines a set of
geofence
parameters. In embodiments, the digital twin provides further visualization of
at least one
geofence that integrates representation of a set of the maritime assets
involved in the maritime
legal proceeding with a representation of a maritime environment adjacent to
the geofence. In
embodiments, the digital twin is also configured to provide one or more of the
sets of
recommendations from the artificial intelligence system for a change of one of
the attributes of
the set of maritime assets involved in the maritime legal proceeding.
[0892] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform for
learning on a training
set of maritime legal outcomes, parameters, and data collected from data
sources to train an
artificial intelligence system to use a digital twin to generate a
recommendation relating to a
maritime legal proceeding.
[0893] In embodiments, the maritime legal outcomes are associated with a real-
world shipyard.
In embodiments, the digital twin is configured to detail at least a portion of
the real-world
shipyard associated with the maritime legal proceeding. In embodiments, the
maritime legal
outcomes are associated with a real-world maritime port. In embodiments, the
digital twin is
configured to detail at least a portion of the real-world maritime port
associated with the
maritime legal proceeding.
[0894] In embodiments, the maritime legal outcomes are associated with one or
more container
ships. In embodiments, the digital twin is configured to detail at least a
portion of the one or more
container ships associated with the maritime legal proceeding. In embodiments,
the maritime
legal outcomes are associated with one or more barges. In embodiments, the
digital twin is
configured to detail at least a portion of the one or more barges associated
with the maritime
legal proceeding.
[0895] In embodiments, the maritime legal outcomes are associated with at
least a portion of
port infrastructure. In embodiments, the digital twin is configured to detail
at least a portion of
the port infrastructure associated with the maritime legal proceeding.
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[0896] In embodiments, the digital twin is configured to at least partially
represent activity of
one or more maritime value chain network entities during a timeline associated
with the maritime
legal proceeding. In embodiments, one or more of the maritime value chain
network entities is a
port infrastructure facility. In embodiments, the data collected by the value
chain network
management platform facilitates identifying theft or misuse of one or more
physical items of the
port infrastructure facility relating to the maritime legal proceeding by
correlating data between a
set of data collectors for one or more of the physical items in the port
infrastructure facility. In
embodiments, the digital twin is configured to further detail one or more of
the physical items of
the port infrastructure facility for the at least one of the port
infrastructure facility and the set of
operators.
[0897] In embodiments, the maritime legal proceeding includes a situation
involving a
container ship that is moored to port infrastructure installed on or adjacent
to land. In
embodiments, the maritime legal proceeding includes a situation involving a
container ship
having a forward speed relative to water and weather conditions and parameters
associated with
energy consumption of propulsion units on the container ship. In embodiments,
the maritime
legal proceeding includes a situation involving one or more ships connected to
barges. In
embodiments, the maritime legal proceeding includes a situation involving one
or more ships. In
embodiments, the digital twin provides for visualization of a navigation
course of one or more of
the ships relevant to the maritime legal proceeding. In embodiments, the
maritime legal
proceeding includes a situation involving one or more ships. In embodiments,
the digital twin
provides for visualization of an engine performance of one or more of the
ships relevant to the
maritime legal proceeding. In embodiments, the maritime legal proceeding
includes a situation
involving one or more ships. In embodiments, the digital twin provides for
visualization of a hull
integrity of one or more of the ships relevant to the maritime legal
proceeding.
[0898] In embodiments, the maritime legal proceeding includes a situation
involving one or
more ships. In embodiments, the digital twin provides for visualization of a
plurality of
inspection points associated with one or more of the ships and maintenance
histories associated
with those inspection points. In embodiments, the digital twin further
provides for the
visualization of the plurality of inspection points associated with one or
more of the ships within
a geofenced area relevant to the maritime legal proceeding and maintenance
histories associated
with those inspection points. In embodiments, the digital twin further
provides for details of a
ledger of activity associated with the visualization of the plurality of
inspection points associated
with one or more of the ships within a geofenced area relevant to the maritime
legal proceeding
and maintenance histories associated with those inspection points.
[0899] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform including
an asset
management application associated with maritime assets and a data handling
layer of the
management platform including data sources containing information used to
populate a training
set based on a set of maritime activities of one or more of the maritime
assets involved in a loss
event and one of outcomes, parameters, and data associated with the one or
more maritime assets
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experiencing the loss event. The information technology system also has an
artificial intelligence
system that is configured to learn on the training set collected from the data
sources, that
simulates one or more attributes of one or more of the maritime assets, and
that generates one or
more sets of casualty forecasts based on the training set collected from the
data sources. The
information technology system also has a digital twin system included in the
value chain network
management platform that provides for visualization of one or more digital
twins associated with
one or more of the maritime assets involved in the loss event including detail
generated by the
artificial intelligence system of at least a portion of one of the sets of
casualty forecasts.
[0900] In embodiments, the maritime assets include one or more container ships
associated
with at least a portion of one of the sets of casualty forecasts. In
embodiments, the digital twin
system further provides for visualization of the digital twin of one or more
of the container ships
including one or more of the attributes in combination with one or more of the
sets of
recommendations associated with the container ships.
[0901] In embodiments, the maritime assets include one or more barges with at
least a portion
of one of the sets of casualty forecasts. In embodiments, the digital twin
system further provides
for visualization of the digital twin of one or more of the barges including
one or more of the
attributes in combination with one or more of the sets of recommendations
associated with the
barges.
[0902] In embodiments, the maritime assets include one or more components of
port
infrastructure with at least a portion of one of the sets of casualty
forecasts. In embodiments, the
digital twin system further provides for visualization of the digital twin of
one or more of the
components of port infrastructure including one or more of the attributes in
combination with one
or more of the sets of recommendations associated with the components of port
infrastructure
associated with the sets of casualty forecasts.
[0903] In embodiments, the maritime assets are associated with a real-world
maritime port. In
embodiments, the digital twin system further provides for visualization of the
digital twin of one
or more of the components of the real-world maritime port associated at least
a portion of one of
the sets of casualty forecasts including one or more of the attributes in
combination with one or
more of the sets of recommendations associated with the components of the real-
world maritime
port.
[0904] In embodiments, the maritime assets are associated with a real-world
shipyard. In
embodiments, the digital twin system further provides for visualization of the
digital twin of one
or more of the components of the real-world shipyard associated at least a
portion of one of the
sets of casualty forecasts including one or more of the attributes in
combination with one or more
of the sets of recommendations associated with the components of the real-
world shipyard.
[0905] In embodiments, the digital twin of one or more of the maritime assets
is a floating asset
twin associated with a ship associated with at least a portion of one of the
sets of casualty
forecasts. In embodiments, the floating asset twin is configured to provide
for visualization of a
navigation course of the ship associated at least a portion of one of the sets
of casualty forecasts
relative to a planned course of the ship and one or more of the sets of
recommendations from the
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artificial intelligence system for a change in the navigation course of the
ship. In embodiments,
the floating asset twin is configured to provide for visualization of an
engine performance of the
ship associated at least a portion of one of the sets of casualty forecasts
and one or more of the
sets of recommendations from the artificial intelligence system for a change
in the engine
performance of the ship. In embodiments, the visualization of an engine
performance includes an
emissions profile of the ship. In embodiments, the floating asset twin is
configured to provide for
visualization of a hull integrity of the ship associated at least a portion of
one of the sets of
casualty forecasts and one or more of the sets of recommendations from the
artificial intelligence
system for a change in maintenance of the hull of the ship. In embodiments,
the floating asset
twin is configured to provide visualizations of a plurality of inspection
points on the ship
associated with at least a portion of one of the sets of casualty forecasts
and maintenance
histories associated with those inspection points. In embodiments, the
floating asset twin is also
configured to provide one or more of the sets of recommendations from the
artificial intelligence
system for a change in maintenance of the plurality of inspection points
associated with the
.. maritime event. In embodiments, the floating asset twin is configured to
provide for
visualizations of the plurality of inspection points on the ship affected by
travel within a
geofenced area and maintenance histories associated with those inspection
points. In
embodiments, the floating asset twin is also configured to provide one or more
of the sets of
recommendations from the artificial intelligence system for a change in
maintenance of the
plurality of inspection points associated with the maritime event. In
embodiments, the floating
asset twin is configured to provide details of a ledger of activity associated
with the visualization
of the plurality of inspection points on the ship associated at least a
portion of one of the sets of
casualty forecasts within a geofenced area and maintenance histories
associated with those
inspection points.
[0906] In embodiments, the artificial intelligence system determines a set of
geofence
parameters. In embodiments, the digital twin provides further visualization of
at least one
geofence that integrates representation of a set of the maritime assets
associated at least a portion
of one of the sets of casualty forecasts with a representation of a maritime
environment adjacent
to the geofence. In embodiments, the digital twin is also configured to
provide one or more of the
sets of recommendations from the artificial intelligence system for a change
of one of the
attributes of the set of maritime assets associated with at least a portion of
one of the sets of
casualty forecasts.
[0907] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform for
learning on a training
set of maritime outcomes, parameters, and data collected from data sources to
train an artificial
intelligence system to use a digital twin to predict and display a casualty
forecast for a set of
maritime assets.
[0908] In embodiments, the set of maritime assets includes a real-world
shipyard. In
embodiments, the digital twin is configured to detail at least a portion of
the real-world shipyard
associated with the casualty forecast.
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[0909] In embodiments, the set of maritime assets includes a real-world
maritime port. In
embodiments, the digital twin is configured to detail at least a portion of
the real-world maritime
port associated with the casualty forecast.
[0910] In embodiments, the set of maritime assets includes one or more
container ships. In
embodiments, the digital twin is configured to detail at least a portion of
the one or more
container ships associated with the casualty forecast.
[0911] In embodiments, the set of maritime assets includes one or more barges.
In
embodiments, the digital twin is configured to detail at least a portion of
the one or more barges
associated with the casualty forecast. In embodiments, the set of maritime
assets includes at least
a portion of port infrastructure. In embodiments, the digital twin is
configured to detail at least a
portion of the port infrastructure associated with the casualty forecast. In
embodiments, the
digital twin is configured to at least partially represent activity of the set
of maritime assets
during a timeline associated with the casualty forecast.
[0912] In embodiments, the set of maritime assets includes a port
infrastructure facility. In
embodiments, data collected by the value chain network management platform
facilitates
identifying theft or misuse of one or more physical items of the port
infrastructure facility
relating to the casualty forecast by correlating data between a set of data
collectors for one or
more of the physical items in the port infrastructure facility. In
embodiments, the digital twin is
configured to further detail one or more of the physical items of the port
infrastructure facility for
the at least one of the port infrastructure facility and the set of operators.
[0913] In embodiments, the set of maritime assets includes a container ship
that is moored to
port infrastructure installed on or adjacent to land. In embodiments, the set
of maritime assets
includes one or more ships connected to barges. In embodiments, the set of
maritime assets
includes one or more ships. In embodiments, the digital twin provides for
visualization of a
navigation course of one or more of the ships relevant to the casualty
forecast. In embodiments,
the set of maritime assets includes one or more ships. In embodiments, the
digital twin provides
for visualization of an engine performance of one or more of the ships
relevant to the casualty
forecast. In embodiments, the set of maritime assets includes one or more
ships. In embodiments,
the digital twin provides for visualization of a hull integrity of one or more
the ships relevant to
the casualty forecast.
[0914] In embodiments, the set of maritime assets includes one or more ships.
In embodiments,
the digital twin provides for visualization of a plurality of inspection
points associated with one
or more of the ships and maintenance histories associated with those
inspection points relevant to
the casualty forecast. In embodiments, the digital twin further provides for
the visualization of
the plurality of inspection points associated with one or more of the ships
within a geofenced area
relevant to the casualty forecast and maintenance histories associated with
those inspection
points. In embodiments, the digital twin further provides for details of a
ledger of activity
associated with the visualization of the plurality of inspection points
associated with one or more
of the ships within a geofenced area relevant to the casualty forecast and
maintenance histories
associated with those inspection points.
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[0915] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform for
identifying theft or
misuse of a port infrastructure facility by correlating data between a set of
data collectors for the
physical item and a set of digital twins for at least one of the port
infrastructure facility and a set
of operators.
[0916] In embodiments, the set of digital twins of the port infrastructure
facility includes one or
more of the attributes in combination with one or more of the sets of
recommendations of
changes to attributes associated with the port infrastructure facility. In
embodiments, the set of
digital twins is configured to provide visualizations of a plurality of
inspection points on the port
infrastructure facility and maintenance histories associated with those
inspection points. In
embodiments, the set of digital twins is configured to provide details of a
ledger of activity
associated with the visualization of the plurality of inspection points on the
port infrastructure
facility within a geofenced area and maintenance histories associated with
those inspection
points.
.. [0917] In embodiments, the set of digital twins is configured to at least
partially represent at
least a portion of the port infrastructure facility associated with an event
investigation and to at
least partially detail a timeline of the event investigation and associated
with the port
infrastructure facility. In embodiments, the set of digital twins is
configured to at least partially
represent at least a portion of the port infrastructure facility associated
with a legal proceeding
and to at least partially detail at least a portion of a timeline pertinent to
the legal proceeding and
associated with the port infrastructure facility. In embodiments, the set of
digital twins is
configured to at least partially represent at least a portion of the port
infrastructure facility
associated with a casualty forecast and to at least partially detail at least
a portion of a timeline
pertinent to the casualty report and associated port infrastructure facility.
[0918] In embodiments, the digital twin details the one or more physical items
in the port
infrastructure facility for at least one operator that includes a view of
expected states of at least a
portion of the one or more physical items. In embodiments, the set of digital
twins provides
further visualization of at least one geofence that integrates representation
of at least a portion of
the port infrastructure facility with a representation of a maritime
environment adjacent to the
geofence.
[0919] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform identifying
theft or
misuse of a shipyard facility by correlating data between a set of data
collectors for the physical
item and a set of digital twins for at least one of the shipyard facility and
a set of operators.
[0920] In embodiments, the set of digital twins for at least one of the
shipyard facility and a set
of operators includes one or more of the attributes in combination with one or
more of the sets of
recommendations of changes to attributes associated with the shipyard
facility.
[0921] In embodiments, the set of digital twins is configured to provide
visualizations of a
plurality of inspection points on in the shipyard facility and maintenance
histories associated with
those inspection points. In embodiments, the set of digital twins is
configured to provide details
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of a ledger of activity associated with the visualization of the plurality of
inspection points on the
shipyard facility within a geofenced area and maintenance histories associated
with those
inspection points.
[0922] In embodiments, the set of digital twins is configured to at least
partially represent at
least a portion of the shipyard facility associated with an event
investigation and to at least
partially detail a timeline of the event investigation and associated with the
port infrastructure
facility. In embodiments, the set of digital twins is configured to at least
partially represent at
least a portion of the shipyard facility associated with a legal proceeding
and to at least partially
detail at least a portion of a timeline pertinent to the legal proceeding and
associated with the
shipyard facility. In embodiments, the set of digital twins is configured to
at least partially
represent at least a portion of the shipyard facility associated with a
casualty forecast and to at
least partially detail at least a portion of a timeline pertinent to the
casualty report and associated
shipyard facility.
[0923] In embodiments, the digital twin details the one or more physical items
in the shipyard
facility for at least one operator that includes a view of expected states of
at least a portion of the
one or more physical items. In embodiments, the set of digital twins provides
further
visualization of at least one geofence that integrates representation of at
least a portion of the
shipyard facility with a representation of a maritime environment adjacent to
the geofence.
[0924] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform for
learning on a training
set of maritime outcomes, parameters, and data collected from data sources to
train an artificial
intelligence system to determine a set of geofence parameters and represent at
least one geofence
in a digital twin that integrates representation of a set of maritime entities
with a representation of
a maritime environment.
[0925] In embodiments, the set of maritime entities is associated with a real-
world shipyard. In
embodiments, the digital twin is configured to represent the real-world
shipyard and geofence
parameters include a location within the real-world shipyard.
[0926] In embodiments, the set of maritime entities is associated with a real-
world maritime
port. In embodiments, the digital twin is configured to represent the real-
world maritime port and
geofence parameters include a location within the real-world maritime port.
[0927] In embodiments, the set of maritime entities is associated with one or
more container
ships. In embodiments, the digital twin is configured to represent the one or
more container ships
relative to the geofence parameters. In embodiments, the set of maritime
entities is associated
with one or more container barges. In embodiments, the digital twin is
configured to represent
the one or more barges relative to the geofence parameters. In embodiments,
the set of maritime
entities is associated with an event investigation. In embodiments, the
digital twin is configured
to at least partially represent the set of maritime entities as it at least
one of interacted during a
timeline associated with the event investigation or is predicted to act based
on a suggestion
associated with the event investigation.
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[0928] In embodiments, the set of maritime entities is associated with a legal
proceeding. In
embodiments, the digital twin is configured to at least partially represent
the set of maritime
entities as it at least one of interacted during a timeline associated with
the legal proceeding or is
predicted to act based on a suggestion associated with the legal proceeding.
[0929] In embodiments, the data collected by the value chain network
management platform
relates to a casualty report. In embodiments, the digital twin of the set of
maritime entities is
configured to simulate possibilities of a loss relevant to the casualty report
based on the data
collected by the value chain network management platform.
[0930] In embodiments, the data collected by a value chain network management
platform
facilitates identifying theft or misuse of physical items contained on the set
of maritime entities
by correlating data between a set of data collectors for one or more physical
items on the set of
maritime entities and the digital twin detailing the one or more physical
items associated with the
set of maritime entities for the at least one of a port infrastructure
facility and a set of operators.
[0931] In embodiments, the set of maritime entities is a container ship that
is moored to port
infrastructure installed on or adjacent to land. In embodiments, data
collected by a value chain
network management platform is based on at least a ship having a forward speed
relative to water
and weather conditions and parameters associated with energy consumption of
propulsion units
on the ship.
[0932] The information technology system also includes an asset management
application
associated with the value chain network management platform and one or more
maritime entities
connected to a ship. In embodiments, the asset management application is
associated with one or
more ships connected to barges.
[0933] In embodiments, the set of maritime entities includes one or more
ships. In
embodiments, the digital twin provides for visualization of a navigation
course of one or more of
the ships. In embodiments, the set of maritime entities includes one or more
ships. In
embodiments, the digital twin provides for visualization of an engine
performance of one or more
of the ships. In embodiments, the set of maritime entities includes one or
more ships. In
embodiments, the digital twin provides for visualization of a hull integrity
of one or more of the
ships.
[0934] In embodiments, the digital twin provides for visualization of a
plurality of inspection
points on the set of the maritime entities and maintenance histories
associated with those
inspection points.
[0935] In embodiments, the digital twin further provides for the visualization
of the plurality of
inspection points on the set of the maritime entities within the geofenced
parameters and
maintenance histories associated with those inspection points. In embodiments,
the digital twin
further provides for details of a ledger of activity associated with the
visualization of the plurality
of inspection points on the maritime entities within the geofenced parameters
and maintenance
histories associated with those inspection points. In embodiments, the
training set of maritime
outcomes, parameters, and data collected from the data sources is related to a
set of shipping
activities.
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[0936] In embodiments, the methods, systems and apparatuses include an
information
technology system having a value chain network management platform for
learning on a training
set of maritime outcomes, parameters, and data collected from data sources
relating to a set of
shipping activities to train an artificial intelligence system to determine a
set of geofence
parameters and represent at least one geofence in a digital twin that
integrates representation of a
set of maritime entities with a representation of a maritime environment.
[0937] In embodiments, the set of maritime entities is associated with a real-
world shipyard. In
embodiments, the digital twin is configured to represent the real-world
shipyard, its associated
set of the shipping activities and geofence parameters include a location
within the real-world
shipyard. In embodiments, the set of maritime entities is associated with a
real-world maritime
port. In embodiments, the digital twin is configured to represent the real-
world maritime port, its
associated set of the shipping activities and geofence parameters include a
location within the
real-world maritime port. In embodiments, the set of maritime entities is
associated with one or
more container ships. In embodiments, the digital twin is configured to
represent the one or more
container ships and its associated set of the shipping activities relative to
the geofence
parameters.
[0938] In embodiments, the set of maritime entities is associated with one or
more container
barges. In embodiments, the digital twin is configured to represent the one or
more barges and its
associated set of the shipping activities relative to the geofence parameters.
In embodiments, the
set of maritime entities is associated with an event investigation. In
embodiments, the digital twin
is configured to at least partially represent the set of maritime entities and
its associated set of the
shipping activities at least partially detailed on a timeline associated with
the event investigation.
In embodiments, the set of maritime entities is associated with a legal
proceeding. In
embodiments, the digital twin is configured to at least partially represent
the set of maritime
entities as it at least one of interacted during a timeline associated with
the legal proceeding or is
predicted to act based on a suggestion associated with the legal proceeding.
[0939] In embodiments, the data collected by the value chain network
management platform
relates to a casualty report. In embodiments, the digital twin of the set of
maritime entities is
configured to simulate possibilities of a loss relevant to the casualty report
based on the data
collected by the value chain network management platform.
[0940] In embodiments, the data collected by a value chain network management
platform
facilitates identifying theft or misuse of physical items contained on the set
of maritime entities
by correlating data between a set of data collectors for one or more physical
items on the set of
maritime entities and the digital twin detailing the one or more physical
items associated with the
set of maritime entities for the at least one of a port infrastructure
facility and a set of operators.
[0941] In embodiments, the set of maritime entities is a container ship that
is moored to port
infrastructure installed on or adjacent to land. In embodiments, data
collected by a value chain
network management platform is based on at least a ship having a forward speed
relative to water
and weather conditions and parameters associated with energy consumption of
propulsion units
on the ship.
203
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