105562002014
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`What is claimed is:
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`CLAIMS
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`1.
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`A demand forecasting system (100); comprising:
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`a forecast engine (130) configured to generate forecast data;
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`a history database (120) operatively coupled to the forecast engine;
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`a plurality of data collectors (110) operatively coupled to the history database;
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`a SIM-TO engine (150) operatively coupled to the forecast engine;
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`a forecast database (160) operatively coupled to the forecast engine; and
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`an application unit (170) operatively coupled to the forecast engine;
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`wherein the forecast engine and the forecast database are configured to provide inputs to
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`the application unit to plan promotion activities; achieve optimal inventory and resource
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`allocation; and minimize operating costs;
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`wherein at least some of the forecast data generated by the forecast engine is stored in the
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`forecast database; and
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`wherein the SIM-TO engine comprises a processor and a computer readable medium
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`containing instructions that; upon execution by the processor; cause the SIM-TO engine to
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`perform a process to identify items that are similar to a new item identified by the data collector;
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`enabling the forecast engine to provide forecast data indicative of a demand forecast for the new
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`item; said process comprising:
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`determining a classification of the new item (302);
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`identifying a set of attributes of the new item (306);
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`calculating matching scores for existing items within the determined
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`classification; wherein the matching scores are calculated using the set of attributes (310);
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`and
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`identifying an existing item as similar to the new item in response to determining
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`that a matching score for the existing item is equal to or greater than a predetermined
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`threshold value (312).
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`2.
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`The system of claim 1; wherein the determined classification comprises a hierarchy of
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`classifications; including as a division; a category; and a subcategory; and wherein the process to
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`identify items that are similar further comprises performing an expanded search of existing items
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`by conducting a search within a higher level of classification.
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`-21-
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`105562002014
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`3.
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`The system of claim 1; wherein the process to identify items that are similar further
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`comprises adjusting the set of attributes and the predetermined threshold value so as to identify a
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`desirable number of similar-to items.
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`4.
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`The system of claim 1; wherein the forecast engine is configured to:
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`divide gathered data from the data collectors into a set of training data and a set of test
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`data;
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`train a plurality of machine learning models using the set of training data;
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`generate fitness functions for the plurality of machine learning models;
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`select a model with the highest degree of fitness across the set of training data;
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`evaluate forecast accuracy of the selected model using the set of test data; and
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`determine whether the forecast accuracy of the selected model is acceptable.
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`5.
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`The system of claim 4; wherein the system is further configured to update the forecast
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`data generated by the selected model as data associated with existing or similar-to items become
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`available.
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`6.
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`The system of claim 4; wherein the system is further configured to adjust the data
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`gathered by the forecast engine to improve the forecast accuracy of one or more machine
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`learning models.
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`7.
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`The system of claim 1; wherein the plurality of data collectors are located in a number of
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`stores of a retail entity and are configured to collect various data relating to items sold by the
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`retail entity; including locations of stores; categories and descriptions of items; times; prices and
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`sales; and wherein the data collected by the data collectors is stored in the history database.
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`8.
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`The system of claim 1; wherein the forecast engine and the SIM-TO engine are accessible
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`via an application programming interface (API) or user interface; and wherein the forecast
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`engine and SIM-TO engine are configured to provide an analysis and summary of results to a
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`requesting user.
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`9.
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`The system of claim 1; wherein calculating matching scores for existing items comprises
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`assigning a value of "1" to an existing item if the existing item has an attribute matching one
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`among the set of attributes of the new item; and otherwise assigning a value of "0" to the existing
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`item; and wherein a matching score of each existing item is determined by dividing a sum of
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`values assigned to each existing item by the number of attributes among the set of attributes.
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`105562002014
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`10.
`
`In a system comprising a forecast engine configured to generate forecast data, a history
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`database, a plurality of data collectors, a SIM-TO engine, and a forecast database, a method
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`performed by the SIM-TO engine to identify items that are similar to a new item identified by a
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`data collector, said method comprising the steps of:
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`determining a classification of the new item (3 02),
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`identifying a set of attributes of the new item (306),
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`calculating matching scores for existing items within the determined classification,
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`wherein the matching scores are calculated using the set of attributes (310), and
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`identifying an existing item as similar to the new item in response to determining that a
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`matching score for the existing item is equal to or greater than a predetermined threshold value
`
`(312).
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`11.
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`The method of claim 10, wherein the determined classification comprises a hierarchy of
`
`classifications, including as a division, a category, and a subcategory, and wherein the method to
`
`identify items that are similar further comprises performing an expanded search of existing items
`
`by conducting a search within a higher level of classification.
`
`12.
`
`The method of claim 10, wherein the method to identify items that are similar further
`
`comprises adjusting the set of attributes and the predetermined threshold value so as to identify a
`
`desirable number of similar-to items.
`
`13.
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`The method of claim 10, wherein the forecast engine performs the following steps:
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`dividing gathered data from the data collectors into a set of training data and a set of test
`
`data,
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`training a plurality of machine learning models using the set of training data,
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`generating fitness functions for the plurality of machine learning models,
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`selecting a model with the highest degree of fitness across the set of training data,
`
`evaluating forecast accuracy of the selected model using the set of test data, and
`
`determining whether the forecast accuracy of the selected model is acceptable.
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`14.
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`The method of claim 13, further comprising updating the forecast data generated by the
`
`selected model as data associated with existing or similar-to items become available.
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`15.
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`The method of claim 13, further comprising adjusting the data gathered by the forecast
`
`engine to improve the forecast accuracy of one or more machine learning models.
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`105562002014
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`16.
`
`The method of claim 10, wherein the plurality of data collectors are located in a number
`
`of stores of a retail entity and collect various data relating to items sold by the retail entity,
`
`including locations of stores, categories and descriptions of items, times, prices and sales, and
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`store the data collected in the history database.
`
`17.
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`The method of claim 10, wherein the forecast engine and the SIM-TO engine are
`
`accessible via an application programming interface (API) or user interface, and wherein forecast
`
`engine and SIM-TO engine provide an analysis and summary of results to a requesting user.
`
`18.
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`The method of claim 10, wherein calculating matching scores for existing items
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`comprises assigning a value of "l" to an existing item if the existing item has an attribute
`
`matching one among the set of attributes of the new item, and otherwise assigning a value of "0"
`
`to the existing item, and wherein a matching score of each existing item is determined by
`
`dividing a sum of values assigned to each existing item by the number of attributes among the set
`
`of attributes.
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`19.
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`A computer readable medium containing instructions that, upon execution by a processor,
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`cause the processor to perform a process to identify items that are similar to a new item
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`identified by a data collector, said instructions comprising:
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`instructions for determining a classification of the new item,
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`instructions for identifying a set of attributes of the new item,
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`instructions for calculating matching scores for existing items within the determined
`
`classification, wherein the matching scores are calculated using the set of attributes, and
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`instructions for identifying an existing item as similar to the new item in response to
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`determining that a matching score for the existing item is equal to or greater than a
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`predetermined threshold value.
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`20.
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`The computer readable medium of claim 19, wherein the instructions for calculating
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`matching scores for existing items include instructions for assigning a value of "l" to an existing
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`item if the existing item has an attribute matching one among the set of attributes of the new
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`item, and otherwise assigning a value of "0" to the existing item, and instructions for dividing a
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`sum of values assigned to each existing item by the number of attributes among the set of
`
`attributes.
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