Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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METHOD AND SYSTEM FOR MANAGTNG COMMODITY
INFORMATION IN A SUPPZY CHAIN OF PRODUCTION
TECHNICAL FIELD'
The invention pertains to a method and system for managing
commodity information as a commodity flows through a supply
chain of production.
BACKGROUND OF THE INVENTION
Many supply chains of production deal with commodities such as
raw or partially processed materials or other articles which
are bought and sold. In such chains of production, the
commodity is sourced from at least one entity, processed in
one or more steps and, typically, transferred between one or
more entities in the supply chain. A discrete quantity of a
commodity (e. g. a lot) may be acquired, blended with other
lots, refined, transported, or combined with one or more other
lots of other commodities. Increasingly, to meet a variety of
producer and consumer interests, there is a need to determine
and track commodity characteristics through the supply chain,
particularly as a commodity moves between entities in such a
chain.
By way of example, agricultural commodities derived from
cultivating the soil or rearing animals and including crops
such as grain, fruit and vegetables as well as other
commodities derived therefrom,. such as meat,. flour, and
prepared foods for humans or animals, etc., are classified
according to certain characteristics. Often, there is a need
to determine one or more inherent characteristics of a
particular commodity in order to further determine a quality
characteristic or other standard measure for the commodity.
Rudimentary methods for determining commodity characteristics
include the visual inspection of the commodity and, typically,
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a subjective comparison to a defined standard. However, more
sophisticated computerized detection and comparison methods
are also known.
By way of example, the Canadian Grain Commission (CGC)
regulates the quality of all grains in Canada. One aspect of
grain analysis in Canada is the determination of the Kernel
Visual Distinctiveness (KVD) of wheat varieties. This measure
helps to track varieties that have specific baking
characteristics. CGC monitors customers' needs and adjusts the
CGC grading structure according to market demands. CGC also
offers an inspection service that is used by grain elevator
operators and the Canadian Wheat Board (CWB). A CGC grain
inspector evaluates samples of a grain shipment visually to
determine grain characteristics and compares the
characteristics to the CGC standard. Elevator operators
purchase grain from farmers on behalf of the CWB. The elevator
operators may blend grain from several farmers in order to
produce an amount of grain that meets a predefined quality
grade level. The price for such grain paid to the farmer by
~0 the elevator operator and to the elevator operator by the CWB
is determined, in part, by the grade of the grain.
Grain shipments are analyzed numerous times between field and
market. For example, grain is analyzed at the farmer's local
elevator before it is loaded for transporting and is evaluated
again when received on behalf of the CGC. Grain elevator
operators risk that the grade evaluation may not be the same
at the receiving end as it was at its origin. When a grain
shipment is evaluated to a lower grade, the elevator operator
receives less money than expected from the CWB; however,
compensation cannot be sought from the farmer.
Although the CGC's grading system is very precise, it is
difficult to implement. This can be attributed to sampling
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bias and the subjectivity of the visual inspection by
different inspectors on different days.
Computerized analysis systems to determine one or more
characteristics of a commodity are well known. For example,
United States Patent No. 6,324,531 issued Nov. 27, 2001 of
Anderson et al. discloses a system for identifying the
geographic origin of a fresh commodity. The system analyzes
samples of the commodity for elemental concentrations. It also
employs a neural network model and a bootstrap aggregating
strategy to determine a classification of each sample
indicative of the sample's origin. United States Patent No.
5,917,927 issued June 29, 1999 of Satake et al, discloses an
apparatus and method for the inspection of rice and other
grains to determine broken rice content. United States Patent
No. 5,321,764 issued June 14, 1994 discloses the
identification of wheat cultivars by computerized visual
imaging analysis.
In view of the dispersed nature of the production and
distribution of agricultural 'commodities, and, often, the
perishable nature of the commodity, it is generally
impractical to conduct analyses using only one instrument. As
noted, grain requires analysis at several 'locations over a
wide geographic area in a relatively short time frame,
Therefore, commodity analysis systems are usually distributed
widely and may be positioned throughout the supply chain in
various locations. In some cases, more than one commodity
analysis system may exist at a single test location.
While these respective commodity analysis systems facilitate a
more objective determination of the one or more
characteristics of the respective commodities to which the
systems are directed, each system tends to operate
autonomously. The systems are not coupled to provide the
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analysis data resulting from the tests to one another or to a
collection system. The analysis data is not conveniently
available for correlation or for review by users and others
interested in the commodity.
Increasingly, a variety of identity preservation,. specialty
trait tracking and food safety certification programs are
being adopted for a variety of commodities. Such programs
impose one or more specifications defining standards for
commodity characteristics for products used or produced in a
supply chain. For example, a program may require the
identification of the variety of a particular discrete
quantity of a commodity as comprising a non-genetically
modified organism (non-GMO). In addition to defining standards
for the commodity itself, some programs mandate standards of
production for the commodity. Such standards may relate to
growing or raising conditions as well as to other production
and processing conditions. Many food safety and other
certification programs mandate such standards.
To adhere to the standards, for particular quantities of the
commodity used or produced in the supply chain, the required
commodity must be analyzed and the characteristics identified.
Thereafter, those quantities that meet the standard are
segregated from other quantities whose characteristics cannot
be assured. Further, as those quantities move through the
supply chain, the characteristics are monitored to preserve
adherence to the standards.
There is therefore a need for a system and method to manage
commodity data in a chain of production.
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SUMMARY OF THE INVENTION
There is provided a system for and method of managing
commodity data for a chain of production in which one or more
commodities are used in one or more production steps.
In accordance with an aspect of the invention, for an
information retrieval system coupled to at least one commodity
analysis system configured to analyze at least one commodity
to generate commodity data comprising at least one commodity
characteristic, there is provided a method of managing the
commodity data for a chain of production in which one or more
commodities are used in one or more production steps. The
method comprises receiving the commodity data from the at
least one commodity analysis system for discrete quantities of
at least one commodity used or produced by the chain of
production; storing the commodity data to the information
retrieval system; and determining commodity information in
accordance with the contents of the information retrieval
system.
The at least one commodity may comprise one of an agricultural
commodity, an aquacultural commodity, an industrial commodity,
a biological commodity and a pharmaceutical commodity. The
production steps may comprise- one or more of .acquiring,
blending, refining, and transporting the discrete quantities
where the commodity data is generated in response to a
production step.
In accordance with a feature of this method, the commodity
information is provided to determine a use of at least a
portion of at least one of the discrete quantities in the
chain of production. The use may be defined in accordance with
a standard .responsive to one or more commodity
characteristics. The standard may define one of an identity
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preservation program, a specialty trait tracking program and a
food safety certification program.
Determining commodity information may include tracing
commodity data for particular discrete quantities as these
quantities flow through the chain of production. For example,
in such a case, instances of the commodity data are generated
for a particular discrete quantity as the quantity flows
through the chain of production and tracing comprises
associating instances of the commodity data with one another
in the information retrieval system.
As a further feature of this method, the at least one
commodity characteristic may include at least one of a
measured characteristic of the particular discrete quantity
and a secondary characteristic determined for the particular
discrete quantity. Further the commodity data may include one
or more source data identifying characteristics of the source
of the commodity.
The method may further feature' transmitting an update to at
least one of the commodity analysis systems. The update may
comprises at least one of a software update, a lease update,
and a data update.
As a further feature, the method may include providing a user
interface for obtaining commodity information determined from
commodity data stored to the information retrieval system.
35 In accordance with an aspect of the invention, there is
provided a method of managing commodity data for a chain of
production in which one or more commodities are used in one or
more process steps. The method comprises generating commodity
data for a plurality of discrete quantities of at least one
commodity used or produced by the chain of production, the
commodity data comprising at least one commodity
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characteristic produced by analyzing the particular discrete
quantity using a commodity analysis system; and transmitting
the commodity data for storing to an information retrieval
system configured for receiving commodity data from a
plurality of commodity analysis systems.
The at least one commodity may comprise one of an agricultural
commodity, an aquacultural commodity, an industrial commodity,
a biological commodity and a pharmaceutical commodity. The
production steps may comprise one or more of acquiring,
blending, refining, and transporting the discrete quantities
where the commodity data is generated in response to a
production step.
A feature of the present method comprises retrieving commodity
information in accordance with the content of the information
retrieval system. In response to the commodity information,
the method may include determining a use in the chain of
production of at least a portion of at least one of the
discrete quantities. The use may be defined in accordance with
a standard responsive to one or more commodity
characteristics. The standard may further define one of an
identity preservation program, a specialty trait tracking
program and a food safety certification program.
Retrieving commodity information may include tracing commodity
data for particular discrete quantities as said quantities
~5 flow through said chain of production.
The at least one commodity characteristic may comprise at
least one of a measured characteristic of the particular
discrete quantity and a secondary characteristic determined
for the particular discrete quantity.
As a further feature of the present aspect, generating
commodity data may comprise generating measurement data and
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examining said measurement data in accordance with a library
of comparative data for determining commodity characteristics.
Further, the commodity analysis systems may be configured to
determine the commodity characteristics in accordance with
artificial intelligence.
Generating commodity data may include entering commodity data
using a user interface of said commodity analysis system and
the method may further comprise correlating commodity data
entered using the interface with commodity data produced by an
analysis.
A further feature of the present method provides that at least
one commodity analysis system periodically gathers commodity
data from a plurality of commodity analyses into a batch
transmits said batch.
The method optionally includes receiving an update transmitted
from the information retrieval system to the commodity
analysis system. The update could comprise at least one of a
software update, a lease update, and a data update.
In accordance with yet a further aspect, for an information
retrieval system coupled to a commodity analysis system
configured to analyse at least one commodity to generate
commodity data for a chain of production in which one or more.
commodities are used in one or more process steps, there is
provided a method of managing the commodity analysis system
comprising receiving commodity data at the information
retrieval system from the commodity analysis system for
discrete quantities of at least one commodity used or produced
by the chain of production; and tracking a use of the
commodity analysis system.
The present method may include invoicing in response to the
determined use of the commodity analysis system. Further, this
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may comprise transmitting an update to the commodity analysis
system.. The update could comprise at least one of a software
update, a lease update, and a data update.
As a feature of this present aspect, the method may include
configuring the commodity analysis system to at least one of:
automatically transmit commodity data to the information
retrieval system, receive an update from the information
retrieval system, and fail to generate commodity data in the
absence of a current permission defined by said information
retrieval system.
Another aspect of the invention provides a computer system for
managing commodity data for a chain of production in which one
or more commodities are used in one or more process steps. The
system comprises an information retrieval system for storing
commodity data for a plurality of discrete quantities of at
least one commodity used or produced by the chain of
production, the commodity data for each particular discrete
quantity comprising at least one commodity characteristic; and
a plurality of commodity analysis systems coupled to the data
storage system for generating commodity data to be stored~by
the data storage system, each commodity analysis system
operating under control of a program to perform commodity
analysis and storage operations as identified by said program;
and each commodity analysis system including at least one
instrument for analyzing the commodity for determining the at
least one commodity characteristic.
Each commodity analysis system may comprise a user interface
for receiving commodity data for storing to said information
retrieval system in association with commodity data determined
by analysis.
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Each commodity analysis system may be configured to retrieve
commodity information from said information retrieval system.
In .accordance with a feature of the system, the commodity
information is retrieved for determining a use in the chain of
production of at least a portion of at least one of the
discrete quantities.
The information retrieval system is preferably configured to
enable tracing of commodity data as particular quantities of a
commodity flow through said chain of production.
The commodity analysis system may be configured for
determining at least one commodity characteristic for
evaluating compliance with a commodity standard. The commodity
standard may define one of an identity preservation program, a
specialty trait tracking program and a food safety
certification program.
The commodity data may include source data identifying the
source of the commodity.
In accordance with a feature of the present aspect, the
commodity analysis systems are configured to analyze one or
more commodities to provide measurement data for each
commodity analyzed; examine said measurement data to determine
at least one commodity characteristic; and generate the
commodity data for particular discrete quantities of the one
or more commodities. The commodity analysis systems may be
configured to examine the measurement data in accordance with
a library of comparative data for determining commodity
characteristics. And further, the commodity analysis systems
may be configured to use one or more artificial intelligence
programs for determining commodity characteristics.
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Preferably, at least one commodity ~ analysis system is
configured to periodically gather commodity data from a
plurality of commodity analyses into a batch and transmit the
batch to the information retrieval system for storing said
commodity analysis data. As well, the information retrieval
system may include a billing component for billing a use of
the commodity analysis systems. As a further option, the
information retrieval system may comprise an update component
to transmit an update to at least one of the commodity
analysis systems, the update comprising one of a software
update, a lease update and a data update.
Another feature of the system provides that at least one
commodity analysis system comprises a regulation component to
regulate the generation of commodity analysis data in response
to a current permission defined by the information retrieval
system.
Further aspects. of the invention provide for one or more
computer program products having a computer readable medium
tangibly embodying computer executable code to manage the
commodity data for a chain of production in which one or more
commodities are used in one or more production steps.
BRIEF DESCRIPTION OF THE DRAWINGS
Further features and advantages of the present invention will
become apparent from the following detailed description, taken
in combination with the appended drawings, in which:
Fig. 1 is a schematic diagram of an embodiment of the system
in accordance with the invention in a chain of production
showing a flow of a commodity through the chain and flow of
data;
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Fig. 2 is a block diagram of a preferred embodiment of the
system in accordance with the invention; and
Fig. 3. is a flowchart of a preferred embodiment, of the method
in accordance with the invention.
It will be noted that throughout the appended drawings, like
features are identified by like reference numerals.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
Fig. 1 is a schematic diagram of an embodiment of a chain of
production system (CPS) 100 in accordance~with an embodiment
of the invention for managing commodity data. Fig. 1
illustrates a flow of a commodity (for example grain) between
members (104-112) of the chain of production and a flow of
data between each member and a central information retrieval
system defining a commodity analysis collection system (CACS)
102.
Generally, a commodity such as grain originates with a grower
or other producer 104. Growers can generate commodity data,
for example by measuring the commodity using a commodity
analysis system (CAS) (not shown) before transferring the
commodity along the chain to an elevator 106. The commodity
data is sent to CACS 102 including a database for receiving
such data. Grain elevators or other terminals in the chain may
analyze incoming and outgoing shipments of the commodity to
generate further commodity data. Again, analysis may be
performed using a CAS (not shown) and transmitted to CACS 102.
Similarly other members in the chain such as users 108,
exporters 110 and importers 112 may perform commodity analysis
to generate and transfer commodity data as the commodity flows
through the chain.
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In addition to generating commodity data, members in the chain
may retrieve commodity data or information from CACS 102. The
data may facilitate the determination of a use of the
commodity, for example, to determine a blending of a quantity
of the commodity with another quantity of the same or a
different commodity. As quantities or blended or used,
commodity data may be associated with the resulting commodity
to facilitate tracing throughout the chain of production. Uses
of the commodity as facilitated by the commodity data may be
in accordance with a standard established for the oommodity.
Other data users 114 such as suppliers or service providers to
or regulators of the members of the chain may also be given
access to the commodity data via CACS 102.
Fig. 2 illustrates a block diagram of a system 200 for
managing commodity analysis data and information in accordance
with a preferred embodiment of the invention. System 200
comprises at least one commodity analysis system (CAS) such as
systems 212, 214 and 216 coupled for communication with an
information retrieval system (i.e. commodity analysis
collection system (CACS)) 240. In the preferred embodiment,
CAS 212, 214 and 216 and the CACS 240 are coupled for network
communication via the Internet 238. However, it is understood
that other public or private networks or combinations thereof
may be employed, whether wired or wireless, sufficient to
communicate signals between CACS 240 and each CAS 212, 214 and
216. Therefore, CAS 212, 214 and 216 may be positioned in
remote locations from CACS 240, such as, in the case of grain
analysis systems, at a farm, grain elevator, transportation
port, mill, or other point in a chain of production (see Fig.
1 ) .
Each CAS 212, 214 and 216 typically comprises a computer (e. g.
a personal computer (PC)) including a programmable processor
(not shown) coupled with one or more instruments, for example,
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an imaging sensor (not shown), for detecting at least one
characteristics of at least one commodity. Preferably, each
computer includes a display device such as a display monitor
and one or more input devices, for example a keyboard,
pointing device and the like for operating the computer (all
not shown). The computer also includes a network interface
device (not shown) for facilitating Internet communications
and one or more storage devices (not shown) for storing
programs and data such as an operating system and applications
as described further below.
Each CAS processor may be programmed by a respective commodity
analysis program (CA program) 218, 220 and 222. A CA program
instructs steps for analyzing the output of the instruments)
with which a processor is coupled and for determining one or
more characteristics of the commodity giving CA data 224, 226
and 228. Further, the CA program may instruct steps for
displaying .the CA data to a user of the CAS and for locally
storing the CA data as described further below.
A primary function of each CAS is the analysis of a commodity
to determine one or more commodity characteristics of interest
to members of the chain. It is understood that such
characteristics may vary in accordance with the commodity as
well as the member. Thus, each CAS is preferably configurable
to determine a plurality of characteristics for any one
commodity and preferably is configurable to analyze more than
one commodity. The commodity characteristics determined by an
instrument controlled by a CA program may include inherent
characteristics such 'as color, weight, moisture content,
shape, etc. and other commodity properties as well as
secondary characteristics determined from such inherent
measurable characteristics. Secondary characteristics may
include variety, disease presence, quality or other valuations
in accordance with one or more defined standards for a
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commodity. For example, a CAS may be configured for detection
of grain varieties with specific traits such as genetically
modified organism (GMO) varieties or other disease tolerances
such as Fusarium tolerance described below.
While each CA program 218, 220 and 222 is illustrated
schematically as a single item, it may comprise'a plurality of
parts such as software and data therefor. For example, the CA
program may comprise software for operating the instrument to
obtain instrument readings, for manipulating the instrument
readings to obtain data for evaluation and for evaluating the
data. The CA program may include image or other recognition
software such as artificial intelligence program, for example,
a neural network and one or more libraries of training data
for the neural network defining commodity characteristics
and/or standards against which the characteristics may be
compared.
Additionally, each CA program 218, 220 and 222 defines a user
interface (not shown) such as a graphical interface for
operating the respective CAS 212, 214 and 216 as described
further below. As well as determining CA data indicative of
one or more characteristics of the commodity, the CA program
218, 220 and 222 is configured for receiving additional CA
data 224, 226 and 228 through the user interface. Such
additional data may comprise one or more identifiers for
identifying the particular quantity of the commodity analyzed
such as by lot identifier, storage location or a shipping
identifier. The CA data may include other identifiers for the
specific analysis such as the CAS location, one or more tests
performed, date, grower and operator, etc.
Grower or other source data for a commodity received via the
user interface may be extensive. Source data for crops may
comprise identifiers for determining a grower's particular
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field, seed variety, soil conditions, fertilizer and other
treatments used, and other inputs known to those skilled in
the art. To avoid duplicitous entry, at least some of the
grower data may be stored, as described further below, to
permit correlation with subsequent commodity analysis and
other data. For example, a CAS may be configured to set up
entries for particular farms, fields or growers which may be
correlated. For example, a field may be correlated with one
grower one year and another grower another year to reflect new
ownership or field use arrangements.
Source data may be useful in order to facilitate certain
identity preservation, specialty trait tracking or
certification programs through all or part of the supply
chain, for managing or planning for particular farms, for
studying yield or other measures for a particular seed variety
or fertilizer, etc. A CAS or other computer such as a computer
250 described below may be configured, as is understood to
persons skilled in the art, with one or more tools or modules
to assist with such data uses.
As it is intended in the preferred embodiment that a plurality
of CAS will be distributed through out various points or
stages in a supply chain, the CA data of interest to be
collected at the various points may differ. A CAS that is used
for analyzing a commodity as it is received from a grower or
other source at an initial stage of a supply chain may be
configured to receive different CA data than a CAS that is
used to analyze the commodity at a subsequent stage in the
supply chain. As is understood by persons skilled in the art,
a CA program may be configured for use at one or more stages
and may be user selective. Alternately, a CA program may be
configured for dedicated use.
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For communicating CA data 224, 226 and 228 to CACS 240 as
described further below, each CAS 212, 214 and 216 further
comprises a respective collection agent 230, 232 and 234.
CACS 240 comprises at least one computer, preferably
configured to be, suitable as a server and including one or
more programmable processors, storage devices and network
interface devices) (all not shown) for storing programs and
data therefore to receive CA data from each CAS 212, 214 and
216. As such, CACS 240 comprises a corresponding collection
server 242 configured as an information retrieval system
cooperating with the collection agents 230, 232 and 234 and a
CA database 244. CACS 240 may be configured as a form of
information retrieval system for managing computerized records
contained in a database known as a relational database
management system. Between an actual database such as CA
database 244 (that is, data stored for use by a computer) and
users of the contents of that database is a software layer
known as the relational database management system (RDBMS or
DBMS). The DBMS is responsible for handling all requests for
access to the database and shielding the users from the
details of any specific hardware and/or software
implementation. Using relational techniques, the DBMS stores,
manipulates and retrieves data in table form. Typically,
these relationships are defined by a set of columns, which are
also referred to as attributes, of data types and a set of
rows, which are also referred to as records or tuples, of
data.
In addition to facilitating the collection of CA data in CA
database 244, CACS 240, as an information retrieval system,
provides a manner to access the collected data in CA database
244 through database queries. Queries may generate reports
including information determined from the CA data or may
retrieve specific instances of CA data. CACS 240 may further
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provide an interface to add further context data for
correlation with specific CA data. General context data may
include weather data or data indicating the known presence of
certain commodity diseases in a general geographical area
related to the CA data.
More specific context data may include further particulars for
the commodity source (e. g. a grower history of disease
incidence, farm or other inspection reports, summary of
growing practices, etc.), shipping or other transportation or
handling data from the particular lot of the commodity, etc.
User access to database 244 may be available through
collection server 242 or another server (not shown).
Preferably, CACS 240 provides a web-based user interface
access method for receiving and answering queries to CA
database 244. In the preferred embodiment, the web-based
service is a subscription-type service available to registered
users for a fee. Access to the CA database 244 through the
service may be made via a CAS 212, 214 and 216. Access may
also be made via other computers such as by a commodity
analysis collection subscriber having a user computer 250
coupled to the Internet. Such users may include, in the
context of grain analysis for example, CGC, CZB, grain
elevator companies, transportation providers, as well as grain
users and purchasers among others in a supply chain for grain.
Exemplary uses of CA data in database 244 are described
further herein below.
Optionally, CACS 242 also includes the current versions of CA
program 246 and collection agent 248 for distribution to a CAS
to ensure the CAS is up to date as described further below.
Fig. 3 illustrates a flowchart of operations 300 in accordance
with a method of managing commodity information of the
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invention. At step 302, a CAS operator performs a commodity
analysis to determine .one or more characteristics of a
commodity. Preferably, the operator gains access to the CAS
through a password-protected user interface. To analyze a
grain sample, for example, the grain sample may be deposited
into a feeding mechanism for the sensor. Using a touch screen
or other pointing-like interface, the operator selects the
tests to be performed by the CA Program of the CAS. The
analysis is performed, generating CA data stored locally on
the CAS representative of the determined characteristics of
the commodity and data to identify the analysis.
Analysis may involve the exemplary steps of:
Capturing a digital image of the grain sample that
has a particular resolution;
Digitizing the image to oreate individual datasets
for each seed in the image;
Providing the datasets for interpretation by an
image recognition operation (for example, a neural
network);
Determining one or more characteristics of the seed
(e.g. by the neural network) in accordance with the
selected tests;
Presenting the analysis results on a display and
making the results available for printing; and
Storing the results.
Additional data may be entered by the operator for
identifying the sample analysis as discussed
previously.
Preferably, only relevant information will be retained from
each analysis - for example, the digital images need not be
stored for future use.
Additional analyses may be selected and conducted throughout
the day and the CA data therefor stored locally on a storage
device coupled to the CAS computer.
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Periodically and preferably at regularly scheduled times,
selected CA data for the period (e.g. each day or week) are
electronically transmitted via the Internet to CACS 240 for
storing in central CA database 244 (steps 304 and 306).
Preferably, prior to transmission, the CA data for each test
are gathered in a batch. The batch may be compressed in
accordance with a data compression protocol and/or encrypted
in accordance with an encryption protocol all as understood to
persons skilled in the art. Preferably, only relevant
information selected from the CA data is transmitted for
storage. The relevance of the data may be determined by
persons skilled in the art with a view to the anticipated uses
of the information by a variety of users. Preferably, the CA
database 244 and any transmission protocol that may be
employed for transmitting the batch data is flexible to
account for different data required by different commodity
tests.
Collecting CA data in batches facilitates off-line analysis
and temporary collection at the CAS. Thus a CAS may be
portable for transporting to particular test locations such as
a farm. Following one or more commodity analysis tests, the
operator may connect the CAS to the Internet to transmit a
batch. It is understood that operations may be configured for
performance while connected to the Internet as well.
At step 308, the transmitted batch is received by CACS 240.
Preferably one or more integrity checks are performed to
validate the received CA data, authenticating that the
transmission is from an approved CAS and/or operator, etc. At
step 310 the CA data is stored to CA database 244.
Acknowledgement of the receiving and storing of the data may
be transmitted to the CAS (not shown).
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This stored CA data is thus available to subscribing users of
the service, for example, by way of value-added reports. Step
312 illustrates an exemplary user query of database 244.
Subscribers, such as various members in the chain or other
parties can submit user queries to access CA data and
correlated data and generate reports. In response to the user
query, reports can be viewed online, downloaded and printed.
Different subscribers to the service may have different access
to information in CA database 244 in accordance with security
and other parameters configured for the subscriber. For
example, grain company head offices may have a wide degree of
access to reports while elevator managers may have a lower
level of access to reports from their own elevators. In
accordance with conventional methods, access to the subscriber
service should be secure to prevent unauthorized access to the
database, the reports and subscriber information especially
during transmission over the network.
The subscriber service may offer pre-defined reports or
customizable reports as is well understood to persons skilled
in the art. While it is contemplated that reports are
generated in response to a subscriber request via, a web-based
interface, persons skilled in the art will recognize that
other reporting mechanisms may be within the scope of the
invention. For example, a subscriber may select to have a
particular report generated periodically (e.g. monthly) and
electronically transmitted to the subscriber such as via
email.
The commodity analysis data managed and information therefrom
may be used in a variety of ways by members of the supply
chain. Upon initial receipt of a quantity of a commodity. CA
data therefor may be used to determine a storage location for
the commodity, for example, to segregate commodities with
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desired traits or in accordance with grade or other measures.
Some members may use CACS 240 when determining a particular
use for a oommodity in the supply chain (,step 314). For
example, database 244 may be queried when combining (e. g.
blending) quantities of a commodity in accordance with a
standard for the commodity. Database queries may be performed
to determine particular quantities of a commodity that exhibit
(or do not exhibit) certain traits to facilitate blending. A
user may desire a commodity that is free of a certain disease
or comprises disease resistant varieties. Conversely, a user
may wish to avoid certain varieties. Though not shown in Fig.
3, a blended commodity may be analyzed and CA data therefore
stored in CA database 244. This CA data may be correlated to
CA data from the particular commodities used to make the
blend. Similarly other commodities used and produced in the
supply chain may be linked to facilitate ready tracking. ,
CA database 244 presents numerous other advantages. One such
advantage is the facilitation of traceability. Traceability
refers to the ability to track a commodity and thereafter
recall its CA data as the commodity flows through a supply
chain. In the grain industry, for example, grain from multiple
sources may be blended and distributed widely for different
uses. CA database 244 provides a manner in which to track CA
data throughout the supply chain from farmer to grain
elevator, transportation provider, intermediaries and end
user(s). Traceability of source identity and commodity
characteristics such as quality or disease is particularly
important. At any point in the distribution chain, appropriate
queries to CA database 244 may be made to provide one or more
reports concerning the commodity. For example, a user may
wish to evaluate a particular grain shipment for its reported
history of disease detected by a CAS at some point in the
supply chain, to identify a source (or sources) of the
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commodity or the geographical location (or locations) of the
source of the commodity. The geographical location may be an
indicator of the likelihood of the presence of a particular
disease. Again, the query result may determine a use for the
particular commodity.
Having more commodity analysis data readily available for
commodities such as grain provides many enhancement
opportunities to those working with the commodity. The data
is useful for .reducing health risks from diseased grain to
those consuming the grain, including animals. Grains such as
wheat and barley, oats and other small cereal grains and corn
may suffer a fungal disease known as Fusarium Head Blight
(FHB) caused by several species of Fusarium. This, disease
reduces crop yield and grade, but more importantly, may also
contaminate the grain with fungal toxins (mycotoxins).
Diseased crop spikelets can contain visibly affected kernels,
termed fusarium damaged kernels (FDK) in the grading of wheat
or fusarium mould for barley. Wheat and barley infected with
FHB may contain toxins such as deoxynivalenol (DON) also known
as vomitoxin. Vomitoxin, if consumed by animals, may result
in reduced feed consumption or feed refusal increasing the
cost of production. Rates and geographical, locations of
fungal infections of crops are tracked by various agencies in
order to assess the risks presented to various industries, the
environment and people.
More and better commodity data permits blending of grain
closer to required specifications and the better matching of
grain to a required end use. Certain grains exhibit better
baking characteristics and may be directed to use as flour,
for example. Better and more consistent grain analysis lowers
the risk of downgrades at ports or other points along the
distribution chain. A central data warehousing approach that
.collects data from geographically disperse points in a supply
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chain facilitates convenient value-added use and re-
distribution. As such, all members in the supply chain for the
commodity may be part of a common system.
In the preferred embodiment, the CACS 240 further provides a
mechanism (not shown) to update via a software update a CA
Program at a CAS, in whole or in part. The updates may reflect
changes to previous functionality or to add new functions
including particular commodity tests. In an exemplary method
of updating, on a regular basis, the version of any CA Program
(e.g. recognition program, such as a neural network and
training data, or user interface) installed on the CAS may be
compared with the latest versions of same indicated by CA
Program 246 of Fig. 2 stored at CACS 240. If the version at a
CAS is out of date, a notification may be made to the operator
of the CAS and a new version may be automatically downloaded
and installed to the CAS in accordance with conventional
methods understood to persons skilled in the art. Preferably,
to ensure that each CAS is always using the most current
software, the operators thereof are not given an opportunity
to decline a software update.
Optionally, a billing mechanism may be integrated into CACS
240 for tracking the use of each CAS for generating invoices.
For example, CACS 240 may be configured to invoice routinely
and automatically a member of the chain, such as a CAS
operator, in accordance with the number and type of commodity
analyses preformed and tracked during .a particular time
period. The billing mechanism may be configured for electronic
or non-electronic notification and payment methods in
accordance with .conventional techniques. Preferably, invoice
and reporting formats are flexible to meet customer needs. A
billing mechanism for the retrieval of CA data and information
from CACS 240 may also be incorporated. A subscription or
other service model may be used. Charges may be based upon the
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types of retrievals and reports generated, upon a periodic
flat rate (e. g. monthly subscription fee) etc. which may vary
by the numbers or types of users. Enterprise rates, individual
user rates, supply chain member rates, third party rates,
among others may be contemplated.
Optionally, in order to ensure that a CAS is not used in a
manner that prevents correct invoicing, operation of a CA
Program may be regulated. Should a CAS continue to be operated
but fail to provide regular updates of CA data to CACS 240 to
trigger a billing event or check for software updates, that
CAS may be regulated to prevent further use of the CA Program.
For example, each time a CAS transmits a batch of CA data to
CACS, CACS may transmit and CAS may receive a lease update or
other current permission defined by the CACS permitting CAS to
operate for a predetermined time period or number of tests. If
the CAS does not reconnect to CACS and transmit a batch of CA
data, a regulation mechanism may prevent CAS from performing
further tests unless and until the CAS provides the CA data as
required. Of course, if no data was generated during the
period, a transmission advising that no data is available may
also be sent to the CACS.
One or more warnings may be generated by CA Program as the end
of the lease approaches or should an error occur such as the
unsuccessful automatic transmission of CA data or the
unsuccessful receipt of a lease or software update. CA program
or a CAS operator may then initiate a further transmission to
obtain the lease. If necessary, an override may be permitted
to allow further testing despite a lease expiry or to use a
previous version of the software.
In addition to transmitting data to a CACS, a CAS' may be
optionally configured for transmitting CA data for storage to
another database such as a database for a chain of production
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member's corporate Enterprise Resource Planning (ERP) system.
Similarly, CAS may be configured to optionally receive a data
update of information particular to a member's CAS such as a
customer list so that a CAS operator may select from a pre-
y populated list of customers and avoid entering duplicate or
erroneous data.
Many advantages to the method and system of the present
invention are apparent. For example, the.invention provides a
manner to conveniently generate commodity analysis data
reports from dispersed analysis systems to track a commodity
as it flows through a supply chain. Users may determine better
uses for a commodity, directing the commodity to appropriate
uses that may increase value. For example, more precise
blending through more frequent sampling and more accurate
analysis of those samples is enabled, resulting in reduced
risk for the elevator and assuring more accurate payment to
the farmers for their grain.
In addition to comprising one or more instruments for
analyzing a commodity 'per se, a CAS computer may be coupled to
one or more instruments (not shown) for measuring or acquiring
other related data. For example, a global positioning sensor
may be employed to provide location data particularly for
portable test systems. Devices for measuring characteristics
of soil, water or growing environment variables may also be
used for relating to particular commodities. Data from these
.~ measurements may be incorporated as CA data for providing to
the CACS.
A CAS, particularly one employing neural network or other
artificial intelligence for recognition of characteristics may
be configured and trained for agricultural commodities other
than grain. For example, a CAS may evaluate flour based on
color and texture characteristics, or be trained to evaluate
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the quality of meats and detect a presence of a steroid from
the meat fiber texture. The CAS could also be used to help in
the blending of grass mixtures for different turf uses.
Additionally, a CAS may be trained to assess the size of feed
particles after milling, the presence of mycelium of different
plant diseases on leaves, the count of bacteria in water
samples or the sugar content in potatoes. The present
invention may be adopted for commodities other than those
generated by an agricultural chain of production as well. Such
other commodities include commodities for other industries
such as the aquacultural, biological or pharmaceutical
industries and industrial manufacturing. For example, the CAS
could be trained to evaluate the color consistency of white
paper and, potentially, paper porosity, eliminating the
hazardous use of mercury and significantly reducing cost. The
CAS could be used to count or inspect small particles
currently done by more expensive machine,vision equipment. CA
data for such tests may be stored to a CACS and tracked
through a chain of production. Aggregate data may be compiled
and retrieved via the CACS.
Though neural networks are described for recognition of
characteristics, other artificial intelligence techniques,
such as fuzzy logic-based recognition, or combinations of
techniques may be used without departing from the scope of the
teachings herein.
The embodiments) of the invention described above is(are)
intended to be exemplary only. The scope of the invention is
therefore intended to be limited solely by the scope of the
appended claims.
