Note: Descriptions are shown in the official language in which they were submitted.
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SYSTEM AND METHOD FOR PRODUCT IMPUTATION RELATING TO
SAMPLE STORES
SPECIFICATION
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of United States provisional patent
application Nos. 60/646,791, 60/646,681, 60/646,812, 60/646,814, 60/646,815,
and
60/646,816, all filed on January 25, 2005. Further, this application is
related to
United States patent application Nos.
which also claim the benefit of the aforementioned United States provisional
patent
applications. All of the aforementioned United States patent applications and
provisional patent applications are hereby incorporated by reference herein in
their
entireties.
FIELD OF THE INVENTION
The present invention relates generally to systems and methods for
predicting market conditions. The invention in particular relates to systems
and
methods for predicting market demand for pharinaceutical and other healtlicare
products.
BACKGROUND OF THE INVENTION
Manufacturers and distributors of retail, wholesale and mail-order
products monitor product sales and usage in order to maintain proper inventory
and to
be able to direct marketing efforts. Product sales are monitored by collecting
sales
data from wholesale distributors, retail outlets and mail-order facilities and
recording
this sales data at a central point for evaluation. The collected sales data is
analyzed to
provide market condition or status reports to the manufacturers and
distributors.
In the healthcare industry, millions of healthcare products are
prescribed and sold worldwide each day. Prescriptions are written by doctors
and
filled at pharmacies, medical devices are sold at doctors' offices, hospitals,
and
pharmacies. Individual businesses participating in various aspects of the
pharmaceutical and healthcare industries generate data related to the goods
sold to
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conform to governmental regulations, to track inventory, and track the market
share of
branded and generic products.
Pharmaceutical entities use data gathered from prescription drug
outlets to improve their understanding of the ever-changing healthcare product
marketplace. In particular, these business entities pay attention to
information on the
use (e.g., type of drug, dosage, number of doctors writing prescriptions per
pharmacy,
etc.) of specific products and product categories so that they can produce,
supply and
stock of such products at outlets (retailers, doctors, etc.) in a timely
manner.
Monitoring of healtlicare markets involves sampling sales at retail outlets
and
transferring sales data to a central point for evaluation and analysis.
Product demand
estimates may be based on such analysis. Retail outlets usually cooperate in
providing sales data but a significant number of retail outlets are not able
to or do not
elect to have sales data sampled in a form needed for analysis. As a result,
it is
necessary to estimate product sales of unsampled and poorly sampled individual
outlets to provide marketing information.
Estimates of business sales in small areas, such as counties of a state,
have been made based on known data for the state under the assumption that the
relationships for the state also hold for the county. The article "Small-Area
Estimation of Economic Statistics" by Cary T. Isaki, Journal of Business and
Economic Statistics, Vol. D, No. 4, October, 1990, pages 435-441 describes a
ratio
correlation (multiple regression) approach for estimating retail sales for
small areas
(counties) using county-to-state shares of retail sales from two successive
economic
censuses. While these methods provide estimates of retail sales over a
relatively
small county area from publicly available data, they are not adapted to
estimate retail
sales of individual outlets.
Estimation of physician prescribing activity has been attempted by
marketing researchers based on ratio estimators and inflation factor
estimators as
commonly described in texts such as "Sampling Techniques" by W. G. Cochran,
John
Wiley, New York 1977. These methods attempt to estimate the activity in a pre-
established geographic area of known dimensions by scaling up a sample of
activity
within the area in proportion to the level of a known auxiliary variable
(i.e., ratio
estimate) or in proportion to the level of sample coverage (via an inflation
factor) for
the entire area. Typical geographic areas encompass a number of outlets and
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prescribers. Such geographic-based methods do not yield estimates of each
individual
prescriber's activity within each individual outlet but only produce a measure
of the
total activity for the geography. If prescriber level estimates are desired,
these
methods must assume that the proportion of the total activity that is captured
in the
sample data (i.e., the captured proportion) of each prescriber is the same. If
outlet
estimates are desired, it must then be assumed that each unsampled outlet is
accurately represented by the average of the sampled outlets in the geography.
With
these assumptions, all sample data within a stratum receive the same "scale-
up"
factor. These assumptions, however, are known to be false and result in biased
estimates at the activity source level.
United States Patent No. 5,781,893 describes systems and methods for
estimating sales activity of a product at sales outlets including "sampled"
outlets and
"unsampled" outlets (i.e., at outlets at which sales activity data is sampled,
and not
sampled, respectively). Sales activity at unsampled outlets is estimated by
determining the distances between each of the sampled outlets and each of the
unsampled outlets and correlating sales activity data from the sampled sales
outlets
according to the determined distances. The sales activity of the product at
the
sampled outlets and the estimated sales activity of the product at the
unsampled
outlets are combined to obtain an estimate of sales activity at all the sales
outlets.
Sales activity of products prescribed by a physician at both the sampled and
unsampled outlets can be estimated by correlating sales activity data for a
prescribing
physician at the sampled outlets. Sales information for specific products at
particular
outlets is estimated on a monthly or weekly basis with reference to historical
use
information.
Projection estimates for immediate or near term demand for
prescription drugs or products, are based on historical data (e.g.,
pharmaceutical sales
and dispensed product data) that is obtained from product outlets ( e.g.,
dispensing
pharmacies). Pharmaceutical companies may use this demand estimate(i.e. a
month
or week's predicted demand) to guide them to manufacture and supply stocks of
the
specific products to the dispensing outlets in a timely manner. If a
particular outlet
did not report data for a particular month, the prior art estimation methods
use data
previously reported by other reporting outlets to estimate the current month
or week's
demand. The prior art manner of data reporting in the health care industry is
on a
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national level, but not specific to a particular drug at a particular pharmacy
in a
particular location.
While the prior art techniques were useful in slowly changing markets,
such estimation techniques are no longer reliable or suitable for rapidly
shifting
market conditions in which pharmaceutical companies now operate. There is
outstanding need for rapid and detailed forecasts of the demand of
phannaceutical and
healthcare products in the market.
Co-assigned pending U.S. patent Application 09/730,266 filed
December 5, 2005, which is incorporated by reference herein in its entirety,
describes
systems and methods for estimating product distribution using a product
specific
universe.
Further consideration is now being to improving systems and methods
for forecasting market demand for products. In particular, attention is
directed to
improving short term or near term forecasts of product demand. Desirable
systems
and methods will provide accurate and reliable predictions or forecasts of the
demand
of a particular drug at a particular pharmacy in a particular location.
SUMMARY OF THE INVENTION
Forecasting systems and methods are provided for accurate and
reliable prediction of the demand of a particular drug at a particular
pharmacy in a
particular location. The systems and methods involve analysis of sample data
at
product levels or other fine levels of the product market/distribution
structure. The
analysis includes continuous evaluation and recalibration of sample data and
prediction models on which near term demand forecasts are based. The
predictions
and models which they are based on are updated dynamically in response to
incoming
market data.
The forecasting systems and methods may be advantageously utilized
by pharmaceutical market researchers to understand and respond to the ever-
changing
healthcare market based on product level data. The product level marlcet data
(e.g.,
prescription (Rx) data) is obtained from prescription drug outlets. Market
data (e.g.,
products sold or dispensed data) is obtained from a number of "sample" or
"reporting" outlets that, for example, can make such data readily available.
The
market data may cover any suitable time (e.g., week or month). Market data or
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conditions for "non-reporting" outlets are estimated from the reporting
outlets' data.
The forecasting systems and methods forecast market conditions or data as a
function
of projected product-level prescription information and sales information for
all the
sample and non-sample outlets
Incoming market data from reporting outlets (e.g., for a current week
forecast) is combined with previously calculated projection factors to create
new
projection factors for the current week. The new projection factors are used
to project
the product sales for the sample stores. Based on both the reported and
projected
sales data for the sample stores, the product level distribution factors are
computed.
These product level distribution factors are used to project the prescription
sales for
all non-sample outlets.
The set of outlets under consideration may be referred to herein
interchangeably as the "universe" or "store universe." A weekly or monthly
forecasting process itself may be referred to herein as "sizing of universe
stores."
In accordance with one aspect of the present invention, Current week
estimate or forecasts and collected sample data reports are generated on a
weekly and
daily basis. Current week estimates or forecasts are kept in a shadow
database. In
this manner, the forecasting systems may continually predict the present
week's
sample data even as the data for the present week is reported.
In accordance with another aspect of the present invention, the actual
data for the present week may be compared to the present week's predicted data
for
each reporting entity. The predicting model may be improved or adjusted based
on
the difference between the predicted data and the reported data.
In accordance with still another aspect of the present invention, an
adjustment factor (i.e., a calibration factor) may be determined. General
quality
control applications may be used to determine if the reported data appears
unusual,
e.g., due to a result of an outbreak of a virus, a catastrophe, or a drug
recall. The
adjustment factor is used to adjust the predicted near term demand for the non-
reporting entities, for example, the demand for the present week or the
remainder of
the present week.
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BRIEF DESCRIPTION OF THE DRAWINGS
Further features of the invention, its nature, and various advantages
will be more apparent from the following detailed description of the preferred
embodiments and the accompanying drawings, wherein like reference characters
represent like elements throughout, and in which:
FIG. lA is a block diagram of the components of a system for
predicting market demand in accordance with the principles of the present
invention;
FIG. 1 B is a block diagram of elementary business processes that are
interlinked in a common market projection methodology in accordance with the
principles of the present invention;
FIG. 2 is a flow diagram illustrating the steps of an exemplary process
for predicting market demand in accordance with the principles of the present
invention;
FIG. 3 is a flow diagram illustrating details of an exemplary process
for predicting market demand in accordance with the principles of the present
invention;
FIG. 4 is a flow diagram illustrating the steps of an exemplary process
for calculating a projection factor for predicting market demand in accordance
with
the principles of the present invention;
FIG. 5 is a flow diagram illustrating the steps of another exemplary
process for calculating a projection factor for predicting marlcet demand in
accordance
with the principles of the present invention;
FIG. 6 is a flow diagram illustrating the steps of yet another exemplary
process for calculating a projection factor for predicting market demand in
accordance
with the principles of the present invention;
FIG. 7 is a flow diagram illustrating the steps of an exemplary process
for calculating data adjustment factors for reconciling imputed and actual
demand for
reporting outlets for predicting market demand in accordance with the
principles of
the present invention;
FIG. 8 is a flow diagram illustrating an exemplary process whereby
market information is predicted in accordance with the principles of the
present
invention;
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FIG. 9 is a schematic illustration of an exemplary computer system that
is configured for performing the processes of FIGS. 2-8, in accordance with
the
principles of the present invention; and
FIG. 10 is a block diagram illustrating a processing section of the
computer system of FIG. 9, in accordance with the principles of the present
invention.
DETAILED DESCRIPTION OF THE INVENTION
Forecasting systems and methods are provided for accurate and
reliable near term prediction of the product demand. The systems and methods
can be
advantageously used to forecast demand of a particular drug at a particular
pharmacy
in a particular location. The forecasts are based on data collected from
reporting or
"sample" stores or market outlets. The systems and methods involve continuous
evaluation and recalibration of sample data collected from market outlets. The
prediction models on which near term demand forecasts are based also may be
dynamically updated or adjusted in time.
FIG. 1A shows an exemplary forecasting system 1000 for predicting
market demand. Forecasting system 1000 includes a processor 1010, a database
1030
(e.g., Oracle database) and a report generator or writer 1040. Processor 1010
may
communicate with database 1030 and report generator 1040 via suitable
electronic
links, for example, a network 1020. System 1000 generates forecasts or
predictions of
market conditions (e.g., product demand or other information), which are then
provided to clients (e.g., product manufacturers). The market information may
be
formatted or assembled by report writer 1040 into a report for delivery to the
clients.
The report may be provided as a hardcopy and/or in a dataset format. System
1000
may include suitable access interfaces (e.g., FTP 1050, CD 1060, or Web login
1070),
which may be utilized by the clients for viewing the reports.
FIG. 1B shows a block diagram of interlinked elementary
business processes ("EBP") 1210- 1310 in a market projection methodology that
is
supported by the inventive forecasting systems and methods. Interlinked EBPs
1210-
1310 may be conducted contemporaneously in parallel or in series as
appropriate for
monitoring and cyclically (e.g., weekly) predicting market conditions. With
reference
to FIG. 1B, process 1210 involves, for example, a definition of monthly
production
store universes. Following process 1210, process 1220 involves creating or
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generating multi-channel store distance files, and process 1230 'involves
sizing the
universe stores. Processes 1220 and 1230 lead to process 1270 for calculating
required projection factors. Process 1240 involves updating the Reporting
database
for a subject week and leads to process 1230 and process 1250. The latter
process
1250 involves generating imputed scripts and in combination with process 1260
("weekly supplier cutoff') leads to process 1280 for utilization of the
imputed scripts.
Required projection factors calculated at process 1270 are appended to the
imputed
data in process 1290. This data is then loaded to the reporting database
(process
1300). In process 1310, the reporting database is updated with imputed weekly
prescriptions (Rx) and factors.
FIG. 2 shows an exemplary "store sizing" process 2000 for predicting
market conditions or product demand using system 1000 of FIG. lA. Process 2000
utilizes historical market data gathered from any number of "sainpling"
sources,
including for example, retail and software vendors to provide initial or
preliminary
forecasts. The historical data may be assembled in database 1030. At step
2010,
process 2000 reads or collects the historical data from the database 1030. The
collected historical data is reassembled in, for example, an extended database
segment
in the database 1030. At step 2030, process 2000 uses a suitable financial or
business
model to predict or generate a preliminary forecast of the product demand
based on
the historical data from sampling sources (i.e. sample pharmacy stores or
outlets).
The forecast may cover any suitable time interval (e.g., the current or
present week).
At step 2030, process 2000 also calculates a projection factor to estimate
product sales
or demand for non-sample outlets. The present week's predicted product sales
or
demand ("Weekly Forecast") may include forecasted values for a select set of
parameters (e.g., the number of prescriptions of a particular product, the
dosage, and
the number of prescriptions written by a certain doctor. At step 2020, the
Weekly
Forecast (including the forecasted values for the selected set of parameters)
is stored
in a shadow database. The shadow database may have any convenient structure or
format (e.g., a relational database or table, or flat files).
The Weekly Forecast is a work-in-progress and is continually updated
by process 2000. At step 2040, "live" market data is gathered from a group of
reporting sources (i.e., sample outlets) during a time interval. This time
interval may
include or overlap with all or some of the time (e.g., days of the week) that
is the
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subject of the Weekly Forecast. At step 2060, the live data may be subject to
quality
control routines or algorithms to screen or note any of the live data, which
may be due
to an unusual event, for example, a result of a catastrophe, flu outbreak,
etc. Then at
step 2070, process 2000 compares the live data with forecasted data in the
Weekly
Forecast for the group of reporting outlets, and accordingly calculates an
adjustment
factor. The adjustment factor is based on the difference between the live data
and the
predicted data in the Weekly Forecast for the group of reporting sources.
The adjustment factor may be calculated on a product-level, a local
geography level, a product dosage level, or Prescriber level, or any other
suitable or
appropriate market definition category or subcategory. A product level may,
for
example, refer to specific products, which are identified by a corresponding
"CFM"
descriptor (e.g., Lipitor). Process 2000 then updates the Weekly Forecast
stored in
the shadow database using the calculated adjustment factor. For example, at
step
2080, process 2000 may update data entries for non-reporting outlets stored in
the
shadow database using the adjustment factor obtained at step 2070 and other
relevant
data. Process 2000 then readjusts (e.g., iteratively recalculates) the Weekly
Forecast
stored in the shadow database (step 2090). At step 2100, the updated the
Weekly
Forecast is provided to clients so that they can rapidly respond to changing
market
conditions.
FIG. 3 show details of an exemplary store sizing procedure 3000 that
may be used in process 2000. Utilizing a DDD History File, the system may
extract
DDD $ data at the outlet/CMF 10 level for all outlets in the universe. For
example, at
step 3110 in procedure 3000, process 2000 may extract wholesale product dollar
sales
amounts ("DDD$" or "wholesale amounts") from a wholesale product sales history
file. The wholesale amounts may be categorized by outlet and/or drug product
levels
for all product channels and outlets in the subject store universe (e.g., the
entire
population of outlets including Retail, Long Term Care and Mail Order
Universes,
etc.). It will be understood that the wholesale amounts may be negative, for
example,
in the case of product returns or refunds.
At step 3120, product and outlet cross-reference data may be utilized to
reflect changes to outlet and/or product information. Accordingly, the
wholesale
amounts may be consolidated under current outlet and product numbers, for
example,
by combining or eliminating outlets and product not in the current or subject
market
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universe. At step 3120, for example, DDD $ amounts may be consolidated under
current CMF outlet and CMF product numbers, split outlet DDD$ may be combined,
and outlets in the current Retail, Long Term Care and Mail Order Universes may
be
eliminated.
At step 3130, running six-month average wholesale amounts are
computed using the consolidated wholesale amounts. The running six-month
averages may be computed monthly for a number of months (e.g., for six months)
preceding the latest month for which sales data is available.
After calculating the six-month average wholesale amounts for all
outlets, procedure 3000 at step 3140 may reset any negative average wholesale
amounts to zero. Procedure 3000 may be configured to generate a "Wholesale
dollar
Amounts" data file (e.g., a "DDD$" file) listing wholesale amounts by product
/
therapy class level for each outlet or channel. For each outlet or channel, a
product
/therapy class level data record may include, for example, channel identifier,
a retail
outlet identifier; a product identifier (e.g., a CFM7 descriptor); a therapy
class
identifier (e.g., a USC5 descriptor); and average wholesale product dollar
sales
information. A therapy class is understood to mean a type or category of drugs
directed to a therapy or treatment, i.e., cholesterol reducers, high blood
pressure
treatment, etc.
Certain stores may be excluded from store sizing procedure 3000
based, for example, on experience or traditional considerations. At step 3150,
data
related to such stores are identified and isolated in the Wholesale Amounts
file before
further processing. The data related to these excluded stores may be
segregated and
written to a separate data file (e.g., Excluded Stores File). The Excluded
Stores File
data may be remerged with the Wholesale Amounts file at the end of the
procedure
3000 (e.g. monthly). The Excluded Stores File may also include data related to
stores
that have atypical or abnormal wholesale product sales information. A
projection
factor of "1" may be assigned to each sample store excluded from the sizing
process
(procedure 3000).
Further, at step 3160, the Wholesale Amounts file may be vetted to
remove or isolate information related to all non-purchasing organizations. For
convenience in data processing, a list of non-profit purchasing organizations
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stored in a file or table. The list of non-purchasing organizations may be
created by
using a non-wholesale product sales / non-prescription warehouse sizing
process.
Next at step 3170 in procedure 3000, all good and imputed total
prescription data is extracted from a total prescriptions data file. The total
prescriptions data for non-purchasing organizations and "excluded" stores may
be
removed from further processing in a manner similar to data excluded from the
Wholesale Amounts file at steps 3150 and 3160.
Procedure 3000 then applies adjustments the wholesale amounts at the
channel, outlet, product, and/or therapy class levels (step 3180). Procedure
3000 may
be configured to perform the adjustments at one or more of these levels. The
adjustments may include retaining total prescription data (TRx) for stores
that were in
the sample every week in the extracted time period, running a cross-reference
to pick
up the latest drug product codes (e.g., CMF-10) and proprietary therapy
classes
(USC5), summing the total prescription counts to the channel / outlet /
product
(CMF7)/ tlierapy class (USC5) level for this time period; and merging on the
average
wholesale product dollar amount for these records only.
Procedure 3000 may generate a combined total prescription and
adjusted wholesale amounts file at step 3190. The combined or adjusted
prescription/wholesale amounts file (e.g., "TRx / DDD $" file) may be include
total
prescriptions (TRx) and wholesale amounts (DDD$) information categorized by
channel, outlet, and product / therapy class. The data in the files may be
formatted or
extended to include, for example, a one-byte outlier field to an adjusted
wholesale
product dollar amount file.
Procedure 3000 may generate an adjusted wholesale amounts file with
adjusted wholesale product dollar amounts for each outlet with total
prescriptions
greater than zero. The output file may be formatted to contain the following
fields:
channel; outlet; product / therapy class (CMF7/USC5); total prescriptions
(TRx);
wholesale product dollar amount (e.g. DDD$); and estimated wholesale product
dollar
amount (e.g. Estimated DDD$).
Procedure 3000 determines if an outlet's estimated wholesale product
dollar amount (Estimated DDD$) should replace its actual wholesale product
dollar
amount, and accordingly generates the adjusted wholesale product dollar amount
file
("Adjusted DDD$ file"). This file may contain the following fields: channel;
outlet;
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product / therapy class(CMF7/USC5); wholesale product dollar amount (DDD$);
adjusted wholesale product dollar amount(Adjusted DDD$); and an outlier
identifier.
To adjust the wholesale product dollar amounts for all outlets having
total prescriptions greater than zero, procedure 3000 cycles through data
records
having a product dollar amount greater than zero, for each of the outlet,
product and
therapy classes. A "Regression Factor" is computed at the channel / product /
therapy
class levels by regression analysis of the total prescriptions and the
wholesale product
dollar amounts. An exeinplary function that may be used for regression
analysis is:
Regression_Factor = (sum of total prescriptions*DDD) / sum(DDD*DDD) (1)
The computed or fitted Regression_Factor may be merged back into the "TRx /
DDD" file by channel / product / therapy class for all outlets. Procedure 300
may
generate an Estimated Wholesale Product Dollar Amounts file based on the
Regression_Factors. Entries in the Estimated Wholesale Product Dollar Amounts
may be calculated by multiplying the Regression_Factors with the total
prescriptions.
Procedure 300 may also calculate the standard deviation of the estimated
wholesale
product dollar amount. Any suitable statistical programs or routines may be
used for
regression analysis. A suitable statistical program, which is a commercially
available,
is a statistics calculations program SAS 8.2.
Procedure 3000 then evaluates if the estimated wholesale product
dollar amount should replace the actual wholesale product dollar amount based
on
statistical criteria and rules. For example, if the estimated wholesale
product dollar
amount is within three standard deviations of the actual wholesale product
dollar
amount, then the adjusted wholesale product dollar amount may be set equal to
the
actual wholesale product dollar amount. Conversely, if the estimated wholesale
product dollar amount is not within three standard deviations of the actual
wholesale
product dollar amount, the adjusted wholesale product dollar amount may be set
equal
to the estimated wholesale product dollar amount. The outlier field in the
data record
may be set positive (e.g., "yes") to indicate such replacement. If after such
replacement or otherwise, the adjusted wholesale product dollar amount is less
than a
preset minimum wholesale product dollar amount, procedure 3000 may delete the
data record.
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Procedure 3000 may merge the total prescription/stores adjusted
DDD$ file with the monthly universe with average wholesale amounts file (step
3190). The merger may be indexed based upon a key for each record of each
input
data file. Procedure 3000 may keep the wholesale product dollar amount and
outlier
flag record in the merged file when a key is in both input files. In the event
the key is
not on both input files, procedure 3000 may indicate as much by resetting the
outlier
field byte (e.g., to "blank" "space"), and adjust the wholesale product dollar
amount
to equal the wholesale product dollar amount. In merging the noted input
files,
procedure 3000 may create a Final Monthly Universe file with Average wholesale
product dollar amount file.
At step 3200, procedure 3000 may add the outlet's state, size and type
data to the Universe file having wholesale product dollar amounts information
to
generate a Total universe file with adjusted wholesale product dollar amounts
(step
3200). In the event, a channel does not include the outlet's type or size,
this variable
may be ignored in further processing.
At step 3210, procedure 3000 may sum the adjusted wholesale product
dollar amount at the following levels: Channel / Size / Type / State / Product
(CMF7)
/ Therapy Class(USC5); Channel / Size / Type /Product / Therapy Class; Channel
/
Size / Product / Therapy Class; and Channel /Product / Therapy Class. The
sequence /
hierarchy of the size, type and state fields may be based on monthly
parameters.
These "parameters" may be provided by or obtained form channel data fields.
For the
summing procedure, it will be understood, that a parameter is a constant that
can be
changed in program code. By "sum ... at the following levels," it will be
understood
that a process of adding numeric values at all possible combinations of levels
is
performed indexed at one of the listed levels.
At step 3210, procedure 3000 may then sum the adjusted wholesale
product dollar amount to channel level, e.g., at the following levels: Channel
/ Size /
Type / State /; Channel / Size / Type; and Channel / Size (step 3220). Again,
the
sequence / hierarchy of the size, type and state fields may be based on
monthly
parameters. These parameters may be provided in channel data.
At step 3230, procedure 3000 may merge the Total Universe file
having adjusted wholesale product dollar amount summed to channel with the
Total
Universe File having adjusted wliolesale product dollar amount summed to
product /
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therapy class / channel. Procedure 3000 may generate a named product name
(i.e., a
named CMF7 drug) file (i.e., the final monthly universe with adjusted
wholesale
product dollar amount file). The named drug file may include the following
fields:
channel / product (CMF7)/ therapy class(USC5); size (may be blank); type (may
be
blank); state (may be blank); product / therapy class adjusted wholesale
product dollar
amount (CMF7/USC5 Adjusted DDD$); total adjusted wholesale product dollar
amount; and total outlet count.
Utilizing the named CMF7 file, procedure 3000 may generate product /
therapy class_distribution variable for each product / therapy class
(CMF7/USC5)
using empirical rules at step 3240. According to an exemplary rule, a product
/
therapy class_distribution variable may be created as follows for each channel
/
product / tlierapy class: if the outlet count is greater than a channel
distribution outlet
count, then:
product / therapy class_distribution =(Drug / therapy class Total Adjusted
wholesale
product dollar amount file / Adjusted wholesale product dollar amount file),
however, if the Outlet count is less than or equal to the channel distribution
outlet
count, then:
the record is deleted.
Procedure 3000 may generate a drug / therapy class distribution file
that includes the following fields: channel / product / therapy class; size
(may be
blank for higher levels); type (may be blank for higher levels); state (may be
blank for
higher levels); and product/therapy class_distribution.
Further at step 3250, procedure 3000 may merge the product/therapy
class_distribution value with a non-purchasing organization file having store
wholesale product dollar amount, using the following hierarchy (if the select
level is
not available): State / size / type; size / type; size; all. The merge product
/ therapy
class distribution values against non-purchasing organizations process may
require the
non-purchasing organization file with store wholesale product dollar amount.
This
file may be created by using the non-wholesale product sales / non-
prescription
warehouse sizing process.
Next at step 3260, procedure 3000 may calculate Adjusted wholesale
product dollar amounts as follows:
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Adjusted wholesale product dollar amount (Adjusted DDD $) = product/therapy
class_Distribution * Outlet wholesale product dollar amount.
At step 3260, procedure 3000 may generate a non-purchasing wholesale product
dollar amount file that will include the following fields: channel, outlet,
product /
therapy class, wholesale product dollar amount, and adjusted wholesale product
dollar
amount.
At step 3270, procedure 3000 may combine the non-purchasing
wholesale product dollar amount file generated at step 3260 with the final
monthly
universe having adjusted wholesale product dollar amount file generated at
step 3230,
and the "stores excluded from monthly sizing generated at step 3150. Procedure
300
also may set the outlier equal to "blank" and adjusted wholesale product
dollar
amount equal to wholesale product dollar amount, and further run product cross-
references to pick up the latest drug products and therapy classes. The
combined file
may contain wholesale product sales data for all outlet / product / therapy
class
combinations in the universe. Step 3270 completes the montlily store sizing
process
portion of procedure 3000.
With continued reference to FIG. 3, step 3280 represents the start of
the weekly sizing process portion of procedure 3000. From the updated
reporting
database, procedure 3000 may extract raw (both actually reported and imputed)
total
prescription data for all outlets from the predefined number ("W") of weeks of
data.
Only data from stores that had good or imputed prescription data in all
extracted
weeks may be kept or processed. Step 3280 may also involve running product
cross-
references to pick up the latest drug products (CMF10)and therapy classes
(USC5).
Using the total prescription data extracted at step 3280, procedure 3000
may next at step 3290 sum the total prescriptions to the outlet / product /
therapy class
level and then divide the sum of the total prescriptions by the number of
weeks
parameter value W to obtain average total prescriptions ("Average TRx"). The
field
size of the average total prescriptions field in the data records may, for
example, be
eight (8) whole and four (4) decimal places.
At step 3300, procedure 3000 may remove stores and products that
have been designated for exclusion from the weekly sizing process using the
total
prescriptions summed to outlet/product/therapy class file. For this purpose,
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3000 may further utilize "the stores excluded from monthly sizing process" and
"products (CMF7s) requiring therapy class (USC5)level projections" files.
Next at step 3290, procedure 3000 may merge the weekly total
prescription data summed to the outlet/ drug (CMF7) / therapy class(USC5)
level file
with the monthly-adjusted wholesale product dollar amount for all outlet /
product /
therapy class combinations in universe file generate at step 3270 using the
following
rules:
1) if the outlet / product / therapy class is present on the wholesale
product dollar amount monthly file and not on the total prescriptions weekly
file, then
the system may ignore the outlet; and
2) if the outlet / product / therapy class is present on the total
prescriptions weekly file and not on the wholesale product dollar amount
monthly
file, then the system may set wholesale product dollar amount equal to zero
Procedure 3000 may use the monthly file that matches the first day of the
week when conducting the weekly sizing process, and every week of a given
month
will use the same monthly file. For example, if the first day of the week is
November
27, the November monthly file may be used in the weekly sizing process for
that
week as well as all other weeks whose first day has a November date.
Similarly, if
the first day of the week is December 1, the December monthly file may be used
in
the weekly sizing process for that week as well as all other weeks whose first
day has
a December date. Procedure 3000 may at step 3280 generate a wholesale product
dollar amount / total prescriptions file that will include the following
fields: channel,
outlet, product / therapy class (CMF7/USC5), wholesale product dollar amount,
adjusted wholesale product dollar amount, and total prescriptions. At step
3290,
procedure 3000 may sum all product / therapy classes with total prescriptions
greater
than zero to identify the potentially missing population and identify all
outlets with no
total prescriptions.
Based on the wholesale product dollar amount / total prescriptions file
created at step 3290, procedure 3000 may at step 3320 estimate total
prescriptions for
stores not in the sample all extracted weeks. First at step 3300, procedure
300 drops
all outlets and product/therapy classes that are to be excluded from weekly
sizing
processes. Step 3320 results in an output file of sample stores only with
adjusted
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wholesale product dollar amounts greater than zero ("Adjusted DDD$ > 0"). This
output file may contain the following fields: channel; product / therapy
class; size
(may be blank); type (may be blank); State (may be blank); and ratio. Step
3320 may
further result in an output file of the estimated total prescriptions for non-
sample
stores only with the adjusted wholesale product dollar amount greater than
zero. This
output file may contain the following fields: channel; outlet; product /
therapy class;
and estimated total prescriptions. Step 3320 may also result in other output
files.
An exemplary output file for outlets in the sample all extracted weeks
and Adjusted wholesale product dollar amount equal to zero, may contain the
following fields: channel / product / therapy class; size (may be blank for
higher
levels); type (may be blank for higher levels); State (may be blank for higher
levels);
total prescriptions; and N, where N equals the outlet count represented by
total
prescriptions.
Another exemplary output file for outlets in the sample all extracted
weeks and adjusted wholesale product dollar amount equal to zero may contain
the
following fields: channel; therapy class; product; size (may be blank); type
(may be
blank); State (may be blank); and estimated total prescriptions. Yet another
exemplary output file of estimated total prescriptions for non-sample stores
with
adjusted wholesale product dollar amount equal to zero, may contain the
following
fields: channel; outlet; product / therapy class; and estimated total
prescriptions.
With continued reference to step 3320, for all outlets in sample for all
extracted weeks with adjusted wholesale product dollar amount greater than
zero and
outlier not set equal to "Yes," procedure 3000 may sum adjusted wholesale
product
dollar amount and total prescriptions and get a count of outlets at the
following levels:
channel; size / type / state / product / therapy class; size / type / product
/ therapy
class; size / product / therapy class; sequence / hierarchy of the size, type
and State
fields should be based on weekly parameters. These parameters are distinct
from the
monthly parameters but like the monthly parameters may also be provided by
channel.
For processing data records, the parameters N1 and N2 are defined as follows:
N1 channel / total prescription / Ratio Outlet Count; and
N2 = channel / total prescription / wholesale product sales ratio cell
minimum.
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In cases where N1 is less than N2, procedure 3000 may delete the data
record. Conversely, for cases where N1 is not less than N2, procedure 3000 may
compute a ratio equal to the: (sum of total prescriptions) / (sum of adjusted
wholesale
product dollar amounts) by channel.
Procedure 3000 may extract the remaining outlet data (all outlets that
were not in the sample all of the previous W weeks). Procedure 3000 may merge
by
the ratio from adjusted wholesale product dollar amount greater than zero file
by the
lowest hierarchy defined by the parameters. If there is no ratio available,
then
procedure 3000 may merge by the first parameter noted in parameter file /
product /
therapy class. Procedure 3000 may continue to the next parameter in noted
parameter
file / product / therapy class, and then to the product / tlierapy class
level. If there is
no data to merge at product / therapy class level, then procedure 3000 may set
the
ratio equal to zero. Procedure 3000 may calculate estimated total
prescriptions using
the following equation:
estimated total prescriptions = ratio * wholesale product dollar amount (2)
Procedure 3000 may produce files for two sets of outlet counts: one
count for each channel / state / size / type ("outlet count") and another
count for
outlets with wholesale product dollar amount greater than zero by product /
channel /
state / size / type ("product outlet count"). The first file may be merged by
channel /
state / size / type, and the second file may be merged by product / channel /
state / size
/ type. For any data record for which N is less than N3, where N="DDD$ = 0
Cell
Count" = "Outlet Count" - "Product Outlet Count" and N3 = DDD$ = 0 Cell
Minimum, then procedure 3000 may delete the data record. The values of N and
N3
may be calculated or obtained based on research.
At step 3320, procedure 3000 may calculate estimated total
prescriptions (Estimated TRx at each level) using the following equation:
estimated total prescriptions =(sum of total prescriptions) /(total outlet
count - outlet
count with wholesale product dollar amount > 0), (3)
where total outlet count = total number of unique outlets in file at that
level, and
Outlet count with wholesale product dollar amount > 0= Unique Outlet Counts by
drug (CMF7) from the wholesale product dollar amount > 0 file.
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If estimated total prescriptions are less than the parameter (total
prescriptions size minimum), then procedure 3000 may delete the record.
Next procedure 3000 may extract all outlets that were not in the sample
of the previous extracted weeks and wholesale product dollar amount equal to
zero
from the wholesale product sales / current total prescriptions file.
At step 3320, procedure 3000 may merge on "the estimated total
prescriptions from wholesale product sales equal to zero" file by the lowest
hierarchy
defined by the parameters. If there is no ratio or estimate of total
prescriptions
available, then procedure 3000 may merge on estimated total prescriptions by
channel
/ size / type / product / therapy class. Procedure 3000 may continue to the
channel /
size / product / therapy class level. If there is no data to merge at product
/ therapy
class level i.e. CMF7/USC5 level, then procedure 3000 may delete outlet /
product /
therapy class.
At step 3330, procedure 3000 may combine the estimated total
prescriptions file for non-sample stores witli wholesale product dollar amount
greater
than zero, the estimated total prescriptions file for non-sample stores with
wholesale
product dollar amount equal to zero, and "the wholesale product dollar amount
file /
current total prescription" file to produce the "weekly universe size" file
for the
current week.. Procedure 3000 may accordingly generate a "weekly universe
size"
file for the current week that may include the following fields: channel,
outlet,
product / therapy class, estimated total prescriptions (only for stores not in
sample for
previous W weeks), and total prescriptions (only for stores in sainple for
previous W
weeks).
Using the universe size files from the current week and previous week,
procedure 3000 at step 3340 generates a weekly universe-sizing file at the
channel /
outlet / product / therapy class level using the following rules:
1) If (current Size / previous Size) > X5 and (current size - previous
size) > X7, then current size = previous size * P1; and ,
2) If (current size / previous size) < X6 and (previous size - current
size) > X8, then current size = previous size * P2,
where X5 = week size change max. ratio, X6 = week size change min. ratio, X7 =
week size change max., X8 = week size change minimum, P 1= week size change
maximum percent, P2 = week size change minimum percent, and size = total
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prescriptions, if a total prescriptions value is available, or size =
estimated total
prescriptions, if a total prescriptions value is not available.
Procedure 3000 at step 3350 may roll up product/therapy classes to
higher levels e.g., to summarize product / therapy class to outlet, product
(CMF7),
therapy class 5 (USC5), therapy class 4(USC4), therapy class 3 (USC3), therapy
class
2 (USC2), therapy class 1(USC1), and store, etc. Procedure 3000 may merge the
appropriate monthly CPM (retail / long term care / mail order combined) store
universe and pick up the store type and monthly sample flag, and create the
"weekly
universe sizing file for projections" containing the following fields:
channel, outlet,
store type, monthly sample flag, product sizing level, size.
FIG. 4 shows an exemplary procedure 6000 for calculating factors for
predicting market conditions and data using system 1000 of FIG. lA. Procedure
6000
is designed to generate projection factors for extrapolating market data from
sample
stores and outlets to non-sample stores and outlets. System 1000 may be
configured,
at step 6010 in procedure 6000, to extract all sample stores from a main
database file
except stores having the following descriptors: a) data imputation override,
and/or b)
excluded stores from projections parameter.
Outlets associated with a data imputation override descriptor are listed
in a data imputation override descriptor file. The sample stores in a
repository for
input transactions file ("SUSF" file) may have a monthly sample use flag set
equal to
one. The data imputation override file may contain a list of product outlet
identifiers
for sample stores that are treated as non-sample stores. The excluded stores
from
projections parameter file may contain a list of product outlet identifiers
for sample
stores that are forced to have a projection factor of "1" and are not used to
project
onto non-sample stores. This parameter may be stored within a production
parameter
library. A static weekly copy of the production parameter library may be
archived
(e.g., saved for two years). System 1000 may save the following data for all
stores
that are extracted: product outlet identifier, channel, store type (if retail
channel), all
product levels identified in the SUSF file for the product outlet identifier,
and average
total prescriptions for every product level identified in the SUSF file for
the product
outlet identifier. Drugs or products for which projections will not be created
are set to
therapy class defaults.
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At step 6020, system 1000 may extract all non-sample stores data from
the SUSF file and sample stores in the data imputation override file. The non-
sample
stores in the SUSF file may have a monthly sample use flag set equal to zero.
The
data imputation override file may contain a list of product outlet identifiers
for sample
stores that are treated as non-sample stores. System 1000 may save the
following data
for all stores that are extracted: product outlet identifier, channel, store
type (if retail
channel), all product levels identified in the SUSF file for the product
outlet identifier
and total prescriptions for every product level identified in the SUSF file
for the
product outlet identifier.
Next at step 6030, system 1000 may extract the "to" product outlet (i.e.
non-sample outlets), the "from" product outlet (i.e. sample outlets) and the
distance
(miles apart) from a multi-channel store distance file. Based on the extracted
data,
system 1000 may create a projections store distance file.
Next at step 6040, system 1000 may use "a store distance evaluation
process" to find the closest "usable" sample stores in the store distance file
for each
store (i.e. non-sample stores) for which market conditions need to be
projected. The
nurnber of sample stores may be restricted to be between a maximum number
sample
stores parameter and a minimum number sample stores parameter. The sample
stores
may also be required to be within a distance indicated by a "maximum distance
between stores" parameter. A "miles apart" data field value in the store
distance data
file may determine the closest stores. If the minimum number of sample stores
are
not found within the distance indicated by the "maximum distance between
stores"
parameter, then stores that are over that maximum distance may be selected
until the
minimum number of stores are found in the projection store distance file for
the store
that needs to be projected. Possibly the minimum number of sample stores may
not
be found if the number of sample stores that can be selected is limited.
Stores that are
"usable" or eligible for this purpose may generally refer to stores that are
not in "the
exclude from projections" list or table, and are not listed in the data
imputation
override file. The usable stores also must be in an eligible channel for the
subject
non-sample store. The usable stores eligibility filtering may be done in the
store
distance process.
All usable stores may be saved and used in a projection formula for
calculate weights. The maximum number sample stores parameter may contain a
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number representing the maximum number of sample stores that can be used to
project market conditions onto a non-sample store. The minimum number sample
stores parameter may contain a value for a desired minimum number of sample
stores
that should be used to project a non-sample store. The maximum distance
between
stores parameter indicates the maximum distance (e.g. number of miles) that a
sample
store can be from a subject non-sample store, so that the sample store's
market data
can be used to project the non-sample store market data or conditions. The
maximum
distance between stores parameter may not be enforced if a minimum number of
sample stores cannot be found to project to a non-sample store. The maximum
distance between stores parameter may be defined by channel and stored in a
production parameter library. A static weekly copy of this library may be
archived
(e.g., saved for two years). System 1000 may save the product outlet
identifier and
the miles apart value for each sample store that was found and saved to
calculate
weights. System 1000 may save the average total prescriptions from the sample
store
factor data for every product level that the sample store and non-sample store
have in
common.
System 1000 may ignore a product level if a sample store has a product
level that is not present or available in the non-sample store. System 1000
may assign
total prescriptions equal to zero for a product level if a non-sample store
has a product
level that is not in the sample store. System 1000 may save the following
data: non-
sample product outlet identifier; channel; store type; product level; average
total
prescriptions for non-sample store for product level; sample store product
outlet
identifier; miles apart; and average total prescriptions for sample store for
product
level. The chaimel criteria in the store distance evaluation process may be
used to
select stores that are added to the multi-channel store distance file for a
given product
outlet. The channel of the non-sample store may determine which stores will be
selected as the sample stores within the store distance evaluation process. A
weight
of zero may be assigned for a product level within a non-sample store, if the
product
level is not present in the sample store that is being used to project market
conditions
to the non-sample store.
At step 6050, system 1000 may calculate a weight for each product
level that the subject non-sample store and the sample store have in common
for each
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non-sample store found and saved. The following exemplary equation may be used
to
calculate the weight for each product level:
Wi = ((1 / di ** p) / (S (i = 1 to n) ti / di ** p)) *tu, (4)
where Wi = weight applied to the sample store data, di = distance from non-
sample to
sample store; (in relative distance sequence, where 1 is the closest); ti =
store size in
Total prescriptions Volume of the sample store; tu = store size in Total
prescriptions
Volume of the non-sample store, p = a variable parameter, n = the number of
sample
stores, and S = sum.
A weight cap parameter may indicate the maximum weight a sample
store can be assigned for a particular product level. The weight cap parameter
may be
defined by channel and stored in a production parameter library. As previously
noted,
a weekly copy of the production parameter library may regularly stored in an
archive
(e.g., saved for 2 years). System 1000 may limit the weight assigned to a
sample store
when the normally computed weight (e.g., by equation 4) exceeds maximum weight
cap.
System 1000 may save the values of both the normally computed and
limited weights assigned to a sample store. System 1000 may also save the
following
data: non-sample product outlet identifier; channel; store type; product
level; average
total prescriptions for non-sample store for product level; sample store
product outlet
identifier; miles apart; average total prescriptions for sample store for
product level;
weight assigned to sample store before capping; and weight assigned to sample
store
after capping.
Next at step 6060, system 1000 may add non-sample store weights to
generate sample store factors.
FIG. 5 shows further details of a process 5000 involved at step 6060 to
create sample store factors. At step 6110 in process 5000, the following
factors are
saved for each sample store: chain, independent, food, mass merchandise (MM),
long
term care (LTC) and mail order (MO). At step 6120, system 1000 may add a
weight
for each non-sample store product level to the appropriate factor for the
sample store
product level. System 1000 may add the weight to a particular factor as a
function,
for example, of channel and store type of the non-sample store. The functions
relating
channel and store types to particular factors may be defined, for example, as:
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a) Retail channel and food store type: add weight to food factor.
b) Retail channel and chain store type: add weight to chain factor.
c) Retail channel and independent store type: add weight to independent
factor.
d) LTC channel: add weight to LTC factor.
e) MO channel: add weight to MO factor.
f) Retail channel and MM store type: add weight to MM factor.
At step 6130, system 1000 may assign or add a value of "1" to the
factor corresponding to the store type for a retail store, the LTC factor for
a LTC
store, or to the MO factor for a MO store. A value of "1" may be added or
assigned to
all product levels for the product outlet.
At step 6150, system 1000 may perform a one-time factor capping
process after all the factors have been computed. When capping a LTC or MO
factor,
system 1000 may change or reset the final computed factor to a maximum value
if the
final computed factor exceeds a designated value stored in a factor cap
parameter file.
A factor for a sample store may be set to the designated factor cap value less
one. All
other factors may be set to the factor cap value. For capping a retail factor,
the system
may add the values in the chain, MM, independent and food factors. If the sum
of
these values exceeds the value in the factor cap parameter, then the equation
below
may be used to adjust the chain, MM, independent and food factors.
adjusted factor = current factor x (A / (chain factor + MM factor +
independent factor
+ food factor)), (6)
where A = (factor cap value - 1) if the sample store is retail, or A = factor
cap value if
the sample store is mail order (MO) or long term care (LTC).
A factor cap parameter may have a value for the maximum factor a
sample store can be assigned for a product level. This parameter may be
defined by
channel and may be stored in a production parameter library. A static weekly
copy of
the factor cap parameter values may be saved for 6 years.
At step 6160 system 1000 may populate a LTC cap flag when a LTC
factor is capped, a Retail cap flag when a retail factor is capped and a MO
cap flag
when a MO factor is capped, etc.
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At step 6170, system 1000 may save the values of the computed and
capped factors. However, values of the factors prior to capping may not be
saved.
Further at step 6180, system 1000 may save the following data: sample product
outlet
identifier; channel; store type; product level; average total prescriptions;
chain factor;
MM factor; Ind factor; food factor; LTC factor; MO factor; retail factor cap
flag; LTC
factor cap flag; and MO factor cap flag.
At step 6190, system may include all sample stores in the sample store
factor data. When a sample store is not used to project a non-sample store
market
conditions or data, all product levels for the sample store may have a factor
of one (1)
for the factor corresponding to the sample store's channel and store type. A
sample
store is not be used to project non-sample stores market conditions or data
when it is
in the excluded stores from projections parameter file or when if it was not
selected
during a "find sample stores" process (i.e., an elementary business process
(EBP)).
FIG. 6 is a flow diagram of an exemplary forecasting procedure 8.000
using system 1000 that may be utilized for predicting market conditions, data
or
statistics. At step 8110 in procedure 8000, system 1000 may identify products
that
have been launched, for example, in the last 13 weeks, based on analysis of
prescription information stored in database 1030. At step 8120, system 1000
may
identify top or leading products (e.g., top 500 products) based on analysis of
national
prescription counts information (e.g., based on prescription volume). At step
8130,
system 1000 may create product groups by grouping all top 500 products by a
therapy
class and may treat each of the top 500 products as its own group.
Next at step 8140, system 1000 may generate data files containing
projected national prescription count information (e.g., national prescription
audit or
NPA information) by product for each of three channels (namely retail, mail
order,
and long term care). The files may include may include, for example, 25 weeks
of
information from history, and also an estimate of national current week volume
from
an early insight database. The files may be grouped by product groups.
At step 8150, system 1000 generates data files containing raw
prescription counts at the outlet / product level for the previous 25 weeks.
These files
also may be grouped by product groups. At step 8160, system 1000 may combine
files generated at steps 8140 and steps 8150 for the retail, long term care,
and mail
order channels. Outlet / product data may be separated or classified into two
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for example, normal and low volume groups. Average prescriptions per week,
number of missing weeks, and maximum prescriptions per week data may be used
to
determine how a particular outlet / product data is classified. At step 8170
system
1000 determines outlet / product data corresponds to a normal or low volume
group
classifications and at step 8180 determines whether a new product is involved.
At
step 8190, for low volume outlets data involving new or old products, system
1000
may forecast the current week volume by taking a four-week average of outlet /
product raw prescription counts. Conversely, for normal volume outlets data
that
does not involve a new product, at step 8180 system 1000 may use a suitable
model
(e.g., Autoregressive Integrated Moving Average (ARIMA) model) to forecast the
current week volume based on outlet / product raw prescription counts for the
past 25
weeks and projected national prescription counts for both the current week and
the
past 25 weeks. Further, for normal volume outlets data that does involve a new
product, at step 8200 system 1000 may forecast a new product volume based on a
national ratio of product to therapy class prescription counts applied to the
outlet level
therapy class prescription counts. System 1000 may combine the forecasts
generated
at steps 8180, 8190 and 8200, to generate a final forecast.
FIG. 7 shows steps 8210-8470 that may be performed by system 1000
in an exemplary imputation procedure 8205 which may be used to allocate
prescriptions, product or prescriptions data to non-reporting outlets or
entities (e.g.
physicians) in the store universe. At step 8210, system 1000 may extract raw
prescription information (e.g., from database 1030). System 1000 may then at
step
8220 pull in special outlet information, and accordingly at step 8230 identify
outlets
with insufficient history to be imputed. The outlets with insufficient history
may be
excluded from further processing. Next at step 8240, system 1000 may calculate
the
distribution of prescriptions by day of the week for each outlet. This
distribution may
be adjusted for a holiday week based on a holiday proportion file. System 1000
may
then at step 8250 count prescriptions by outlet / product group / sort_key
(numerator),
and separate data into various files for future processing. At step 8260, each
forecast
estimate in a final forecast file (e.g., from process 8200 FIG. 6) is
converted to an
integer value. At step 8270, system 1000 may further separate the files by a
product
grouping method (e.g. product / therapy class or therapy class)
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System 1000 may perform subsequent processing steps 8280, 8300,
8320, 8340, 8360, 8365 and 8380 on the separated files for each grouping
(i.e.,
product / therapy class and therapy class). At step 8280, system 1000 may
count
prescriptions by outlet / product. At step 8300, these counts may be combined
with
the forecast estimate. If there is a forecast estimate but no prescription
counts, then
the data may be placed a separate output file. At step 8320 system 1000 may
calculate the needed raw prescriptions counts by imputation (e.g. as forecast
*
numerator / denominator). At step 8340, system 1000 may sort the raw
prescriptions
counts by outlet / therapy class / sort key and random number. At step 8360,
system
1000 may determine whether the estimated number of applicable prescriptions
from
the raw prescriptions file is available. If estimated number of applicable
prescriptions
is available, system 1000 may at step 8365 randomly select the needed
prescriptions
from the raw prescriptions file. Step 8365 may be repeated twice if necessary.
If this
is not sufficient, the remaining number of prescriptions needed may be output
to a
separate file.
If the estimated number of applicable prescriptions from the raw
prescriptions file are not available, system 1000 may at step 8380 access or
generate a
repository of national prescriptions by product file. Where more prescriptions
are
needed for imputation than are available in that outlet's history, the
prescriptions may
be selected from the pool of all national prescriptions. At step 8400, the
needed
prescriptions may then be randomly selected (e.g., using steps 8320 and 8330)
from
the national pool of prescriptions.
After a sufficient number of needed prescriptions have been generated,
system 1000 at step 8410 may combine all allocation files. Before the close of
week,
at step 8420 system 1000 may identify non-reporting outlets, which become
eligible
for use of imputed prescriptions. At step 8430 system 1000 may reallocate days
of
the week in the imputed prescriptions to match history distribution. After the
close of
week, system 1000 may repeat step 8430 for any non-reporting outlet that was
not
identified as such before the close of week (step 8440). Next, at step 8450
system
1000 may load imputed prescriptions data to a database (e.g., database 1030),
and at
step 8460 may compare imputed data estimates to actual values for outlets that
did
report prescriptions data. Further, at step 8470, system 1000 may generate an
imputed
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data adjustment factor to adjust for any difference between imputed total and
actual
totals for reporting outlets.
In the context of weekly forecasting of market conditions based on
prescriptions/scripts data or other data, it will be understood that
"trailing" data is old
data received in the current week, in other words the data represents a
prescription
with a dispense date that is older then the current cycle week. Trailing data
may be
received on a regular basis from stores and suppliers. The trailing data may
be
expected to show repeatable trends similar to the other store monitoring
evaluations.
A trailing data factor reflects the trend of the trailing data.
Back data is similar to trailing data in that the scripts are for an older
week than is currently being processed. Scripts data may be labeled as back
data
when the trailing data is unusual or exceeds some threshold parameter. To
avoid
breaking or disrupting trends, back data may not be used in current or future
trailing
data factor calculations.
In the operation of system 1000, all of the trailing data parameters are
stored in the databases and processing files a manner similar to other
statistical
parameters. The trailing data parameters are defined at the global level;
however,
supplier and store overrides of the global settings are possible. If the
supplier or store
level override parameters exist and are available, they are used for data
processing in
favor of the global parameters.
A trailing data factor for a particular outlet may be calculated based on
the average of the prior weeks of trailing data (e.g., six weeks). The
trailing data for a
supplier / store is added into the daily data and trailing data of the data
week for
which it belongs. The trailing data does not affect any already-existing
processing
status code (e.g., the data will not be reevaluated). If there is no data for
a particular
data week for the particular supplier / store, then default processing status
code may
be blank.
FIG. 8 illustrates an exemplary procedure 9000 for calculating a
trailing data factor (TDF) by store (i.e., how much of the data is trailing)
using the
system 1000. For purposes of the trailing data factor calculation in procedure
9000,
the total prescriptions are given by the following equation:
total prescriptions = sum of good prescriptions and trailing prescriptions
(10)
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The trailing data calculations (e.g. procedure 9000) may require that suitable
initialization parameters and limits are defined. The initialization
parameters and
limits may, for example, include terms such as "Default TDF", "Max TDF", "Min
Week Percentage", "Max Week Percentage Back Data Max %", "Min Weeks for
TDF", and "TDF Weeks", which are defined as follows:
Default TDF: if after preprocessing the Trailing data history file a Store has
two or
fewer weeks in the file, the system may set trailing data factor to the
default. The
initial value should be 1.
Max TDF: if the TDF > Max TDF, the system should set TDF to Max TDF. The
Max TDF initial value should be 1.5.
Min Week Percentage: During evaluation of history, if the calculated
percentage
between Good Prescription and Total Prescriptions for a week is less then Min
Week
Percentage, the week should not be used as part of the TDF calculation. The
Min
TDF initial value should be 1.
Max Week Percentage Back Data Max %: during evaluation of history, if the
calculated percentage between Good Prescriptions and Total Prescriptions for a
week
is greater then Max Week Percentage, the week should not be used as part of
the TDF
calculation. The Min TDF initial value should be 1.5.
Min Weeks for TDF: the minimum number of weeks required to calculate the TDF.
If less than the minimum, then the system should use the default TDF. The
initial
value should be 3.
TDF Weeks: the number of weeks to examine for TDF calculations. The initial
value
should be 6.
The following data fields from the history file may be required for
trailing data calculations and storing results: 1) store (trailing data is
calculated for
each store); 2) week data received (week trailing data factor is calculated);
3) total
prescriptions (current week good prescriptions (dispense date week same as
data
receipt)); 4) trailing prescriptions (current week trailing prescriptions
(dispense date in
earlier week then receipt date)); 5) total good prescriptions (calculated)
(sum of total
prescriptions and trailing prescriptions); 6) trailing data factor (trailing
data factor
percentage is calculated from total prescriptions and good prescriptions); and
7) back
data indicator (value indicating whether or not the trailing data is really
back data and
not part of a normal trend).
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With renewed reference to FIG. 8, in procedure 9000, at step 9010
system 1000 may extract weekly data and calculate a previous week percentage
for
each of the last 6 weeks. The previous week percentage is equal to trailing
prescriptions / total prescriptions at the same level. At step 9020 system
1000 may
determine whether the previous week percentage is between a minimum week
percentage and the maximum week percentage. If the previous week percentage is
not within the minimum and maximum limits, system 1000 may set a back data
flag,
and further at step 9040 determine whether there are fewer than three weeks of
acceptable previous week percentages. If there are fewer than three weeks of
acceptable previous week percentages, then at step 9050 system 1000 may set
the
trailing data factor to default trailing data factor. Conversely, when more
than three
weeks of acceptable previous week percentages are present system 1000 at step
9060
may set the TDF equal to the ratio of the sum over remaining store / weeks (up
to 6)
of total prescriptions to the sum over remaining store / weeks of good
prescriptions.
If the TDF exceeds a maximum allowed value "Max TDF.", then the TDF may be set
to Max TDF (step 9070).
It will be appreciated by those skilled in the art that the methods of
Figs. 1-8 can be implemented on various standard computer platforms operating
under
the control of suitable software defined by Figs. 1-8. In some cases,
dedicated
computer hardware, such as a peripheral card in a conventional personal
computer,
can enhance the operational efficiency of the above methods.
FIGS. 9 and 10 show exemplary computer hardware arrangements
suitable for performing the methods of the present invention. Referring to
FIG. 9, the
computer arrangement includes a processing section 910, a display 920, a
keyboard
930, and a communications peripheral device 940 such as a modem. The computer
arrangement may include a digital pointer 990 such as a "mouse." The computer
arrangement also may include other input devices such as a card reader 950 for
reading an account card 900. In addition, the computer arrangement may include
output devices such as a printer 960. The computer hardware arrangement may
include a hard disk drive 980 and one or more additional disk drives 970 that
can read
and write to computer readable media such as magnetic media (e.g., diskettes
or
removable hard disks), or optical media (e.g., CD-ROMS or DVDs). Disk drives
970
and 980 may be used for storing data and application software.
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FIG. 11 shows an exemplary functional block diagram of processing
section 910 in the computer arrangement of FIG. 10. Processing section 910
includes
a processing unit 1010, a control logic 1020, and a memory unit 1050.
Processing
section 910 may also include a timer 1030 and input/output ports 1040.
Processing
section 910 may further include an optional co-processor 1060, which is
suitably
matched to a microprocessor deployed in processing unit 1010. Control logic
1020
provides, in conjunction with processing unit 1010, controls necessary to
handle
communications between memory unit 1050 and input/output ports 1040. Timer
1030
may provide a timing reference signal for processing unit 1010 and control
logic
1020. Co-processor 1060 enhances system abilities to perform complex
computations
in real time, such as those required by cryptographic algorithms. Memory unit
1050
may include different types of memory, such as volatile and non-volatile
memory and
read-only and programmable memory. Memory unit 1050 may, for example, include
read-only memory (ROM) 1052, electrically erasable programmable read-only
memory (EEPROM) 1054, and random-access memory (RAM) 1056. Various
computer processors, memory configurations, data structures and the like can
be used
to practice the present invention, and the invention is not limited to a
specific
platform.
In accordance with the present invention, software (i.e., instructions)
for implementing the aforementioned demand forecasting systems and methods
(algorithms) can be provided on computer-readable media. It will be
appreciated that
each of the steps (described above in accordance with this invention), and any
combination of these steps, can be implemented by computer program
instructions.
These computer program instructions can be loaded onto a computer or other
programmable apparatus to produce a machine such that the instructions, which
execute on the computer or other programmable apparatus, create means for
implementing the functions of the aforementioned demand forecasting systems
and
methods. These computer program instructions can also be stored in a computer-
readable memory that can direct a computer or other programmable apparatus to
function in a particular manner such that the instructions stored in the
computer-
readable memory produce an article of manufacture including instruction means,
which implement the functions of the aforementioned demand forecasting systems
and methods. The computer program instructions can also be loaded onto a
computer
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or other programmable apparatus to cause a series of operational steps to be
performed on the computer or other programmable apparatus to produce a
computer
implemented process such that the instructions which execute on the computer
or
other programmable apparatus provide steps for implementing the functions of
the
aforementioned demand forecasting systems and methods. It will also be
understood
that the computer-readable media on which instructions for implementing the
aforementioned demand forecasting systems and methods are be provided include,
without limitation, firmware, microcontrollers, microprocessors, integrated
circuits,
ASICS, and otller available media.
It will be understood, further, that the foregoing is only illustrative of
the principles of the invention, and that those skilled in the art can make
various
modifications without departing from the scope and spirit of the invention,
which is
limited only by the claims that follow.
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