Method and System for Optimizing Store Space and Item Layout

Abstract

A method in a computer system for generating an efficient item assortment associated with a plurality of items includes obtaining choice set data specifying a multiplicity of choice sets associated with the plurality of items, where each of the multiplicity of choice sets includes several of the plurality of items, at least some of which are mutually substitutable, obtaining item interaction data descriptive of substitutions between pairs of items in each of the multiplicity of choice sets, obtaining a set of benefit metrics associated with the plurality of items, obtaining a constraint parameter associated with the item assortment, and generating an item selection based at least on the item interaction data, the set of benefit metrics, and the constraint parameter, where the item selection identifies at least one of the plurality of items selected for inclusion in the item assortment.

Description

FIELD AND BACKGROUND OF THE DISCLOSURE[0001]1. Field of the Disclosure[0002]This disclosure relates generally to optimizing store space and, in particular, to developing efficient assortment and merchandizing fixture layout for a plurality of items.[0003]2. Background Description[0004]To increase profits and improve customer experience, retailers often develop diagrams that specify where and in what quantity items should be placed on shelves, in slots of vending machine, or on a sales floor. These diagrams are known as planograms or POGs. In general, the development of a planogram includes generating a selection of items for inclusion in a planogram (known as product assortment), and determining relative placement of these items within the planogram (known as item layout). While a relatively effective planogram for two or three items may be generated manually by simply trying various combinations and relying on subjective judgment, developing a planogram for tens or hundreds of item...

AI Technical Summary

Benefits of technology

[0012]In some embodiments, the system further includes a layout optimizer to improve the layout of the generated assortment. The layout optimizer may include a first component to efficiently block (place close together) items in accordance with a set of business rules, and a second component to sort the items within each block or across blocks, if desired. The layout optimizer may further include a control to adjust the layout in view of shoppability and profitability of the items.

[0015]In another embodiment, a method in a computer system for generating an efficient item assortment associated with a plurality of items, to be disposed in a retail area having a plurality of regions, includes receiving item data that includes, for each of the plurality of items, a first metric associated with a physical parameter of the item and a second metric indicative of profitability of the item, receiving retail region data that includes a plurality of metrics, where each of the plurality of metrics is associated with the physical parameter of a respective of the plurality of regions, receiving choice set data specifying a multiplicity of choice sets, where each of the multiplicity of choice sets includes several of the plurality of items, at least some of which are mutually substitutable, receiving item interaction data that includes, for each of the multiplicity of choice sets, a metric of substitutability between items in the corresponding choice set, generating a multiplicity of lists of facing combinations, where each of the multiplicity of lists corresponds to a respective one of the multiplicity of choice sets, and where each facing combination in each of the multiplicity of lists includes one or several facings of at least one of the items in the corresponding one of the multiplicity of choice sets, calculating at least a profit metric and a physical parameter metric for each facing combination in each of the multiplicity of lists of facing combinations based on the first metric and the second metric of each item included in the facing combination and the interaction data associated with the corresponding choice set, and selecting zero or more facing combinations from each of the multiplicity of lists of facing combinations to generate a selection so as to maximize a total profit associated with the selection in view of the retail region data and the first metric of each item included in the selection.

[0016]In another embodiment, a method in a computer system for generating an efficient item assortment associated with a plurality of items includes obtaining a first plurality of parameters, where each of the first plurality of parameters includes a benefit metric of a respective one of the plurality of items, obtaining a constraint parameter associated with the item assortment, generating a plurality of facing combinations, each including one or more facings of one or more of the plurality of items, generating a second plurality of parameters using the first plurality of parameters, where each of the second plurality of parameters includes a benefit metric of a respective one of the plurality of facing combinations, applying a function of the second plurality of parameters, subject to a limitation associated with the constraint parameter, to generate an optimization result, and generating an item selection based on the optimization result. In one operational mode consistent with this embodiment, the constraint parameter is a particular value. In another operational mode, the constraint parameter is a range of values. Further, in some operational modes, applying the function includes maximizing the function to identify the maximum value for the obtained constraint parameter. In another mode, applying the function includes generating multiple values consistent with the constraint parameter, and the optimization result accordingly includes multiple values through which an item selection can be generated.

[0017]In another embodiment, a method in a computer system for generating an efficient item assortment associated with a plurality of items, to be disposed in a retail area having a plurality of regions, includes obtaining a first plurality of parameters, where each of the first plurality of parameters is associated with a respective one of the plurality of items and includes a benefit metric of the respective one of the plurality of items indicative of a financial benefit associated with a sale of one unit of the respective one of the plurality of items, and a spatial metric of the respective one of the plurality of items specifying one of length, width, or height of the respective one of the plurality of items, obtaining a plurality of spatial metrics, each of the plurality of spatial metrics corresponding to a respective one of the plurality of regions of the retail area, obtaining choice set data specifying a multiplicity of choice sets associated with the plurality of items, generating a plurality of facing combinations in accordance with the choice set data, where each of the plurality of facing combinations includes one or more facings of one or more items of a respective one of the multiplicity of choice sets, generating a second plurality of parameters using the first plurality of parameters, where each of the second plurality of parameters corresponds to a respective one of the plurality of facing combinations and includes a benefit metric of the respective one of the plurality of facing combinations indicative of an expected financial benefit associated with including the respective one of the plurality of facing combinations in the item assortment, and a spatial metric of the respective one of the plurality of facing combinations specifying one of length, width, or height of the respective one of the plurality of facing combinations, maximizing a function of the benefit metrics of the second plurality of parameters, subject to a limitation associated with the plurality of spatial metrics, to generate an optimization result, and generating an item selection based on the optimization result.

Problems solved by technology

While a relatively effective planogram for two or three items may be generated manually by simply trying various combinations and relying on subjective judgment, developing a planogram for tens or hundreds of items is a highly complicated task.

Generally speaking, planogram development is difficult due to numerous factors that affect the profitability of a particular arrangement of items.

For example, shelf or floor space is typically limited, items usually have different physical dimensions, shelves often vary in width and depth, the sale of different items generates different profits, etc.

Further, when a customer cannot find the desired item on the shelf, he or she may purchase a substitute, while another customer will not accept a substitute and will not make a purchase at all.

For these reasons, developing an efficient planogram that satisfies every relevant rule, generates a high profit, and addresses customers' needs remains very complicated.

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