Item substitution techniques for assortment optimization and product fulfillment

Patent metadata

Field	Value
Publication number	US-12468992-B2
Application number	US-202117174667-A
Country	US
Kind code	B2
Filing date	Feb 12, 2021
Priority date	Feb 1, 2019
Publication date	Nov 11, 2025
Grant date	Nov 11, 2025

Abstract

Methods and systems for optimizing a product assortment, and managing product fulfillment, are disclosed. One method includes utilizing a model trained on item data to identify item substitution pairs within an item category of an item assortment, the item substitution pairs being identified as having a substitutability score above a predetermined threshold. The method further includes applying an assortment optimization model to generate an assortment recommendation for the item category at an identified retail location. A request for an item not included in the assortment recommendation may result in suggestion of an identified substitutable item from among the item substitution pairs.

First claim

The invention claimed is:

1 . An item assortment management system comprising:
a computing system comprising one or more processors communicatively coupled to a memory subsystem that stores instructions which, when executed, cause the one or more processors to: obtain transactional data regarding an overall item assortment of a retail enterprise from the transactional data including transactions across the retail enterprise; obtaining an initial item assortment and a set of constraints from a user computing device; for items within an item category, training a model comprising a graph convolutional network suitable for weighted graphs to learn embeddings for nodes representing potentially substitutable items, the model generating a graph having edge weights corresponding to a degree of substitutability between items and being based at least in part on the transaction data and item data describing items in the item assortment; identify one or more item substitution pairs within the item category based on the degree of substitutability being greater than a threshold; obtain location specific item assortment parameters for an item category at an identified retail location within the retail enterprise, the retail enterprise including a plurality of retail locations, the location specific item assortment parameters including mandatory items that must be stocked within a given item category, and a minimum number of items that should be stocked for the given item category; and generate an optimized item assortment for a retail location of the retailer, the retail location being sized to stock the optimized item assortment including fewer than all of the items in the overall item assortment, wherein the optimized item assortment is based at least in part on a set of items within the item category available at the identified retail location, a sales forecast for items in the set of items, a customer loyalty factor, the one or more item substitution pairs, and one or more physical constraints specific to the retail location; receive a request for an item in the initial item assortment from the user computing device that is excluded from the optimized item assortment, the request being for a same-day purchase of the item from the retail location; present, in a user interface of the user computing device, a display of a recommendation of an item within the optimized item assortment that is in stock at the retail location including at least one item excluded from the optimized item assortment, the recommendation of the item received from the graph convolutional network, by automatically displaying an element in the user interface proximate to the display of the at least one item; and in response to selection of the user interface element, displaying in a separate region of the user interface one or more items from among the assortment recommendation determined to be substitutable for the at least one item.

2 . The item assortment management system of claim 1 , wherein the item data includes item purchase data derived from online purchase transactions.

3 . The item assortment management system of claim 1 , wherein the computing system applies item-to-item collaborative filtering to identify substitutable item pairs.

4 . The item assortment management system of claim 1 , wherein the graph convolutional network is trained based at least in part on online guest transactions.

5 . The item assortment management system of claim 4 , wherein the graph convolutional network is trained based at least in part on substitution subgraphs generated from portions of the item assortment.

6 . The item assortment management system of claim 1 , wherein the optimized item assortment is further based in part on one or more business rules including a rule defining items within the item category indicated as being mandatory to be stocked at the identified retail location.

7 . The item assortment management system of claim 1 , wherein the optimized item assortment maximizes sales volume of items across the item category subject to a business requirement of an item having an item loyalty greater than a predetermined threshold.

8 . The item assortment management system of claim 7 , wherein the optimized item assortment includes at least one substitute item from a substitutable item pair in place of another item within the substitutable item pair based at least in part on one or more of the constraints.

9 . The item assortment management system of claim 7 , wherein the optimized item assortment is determined according to:

max
i

⁢

=

∑

i
=
1

N

⁢

⁢

x
i

⁢

s
i

+

∑

i
=
1

N

⁢

⁢

∑

j
=
1

N

⁢

⁢

s
i

⁢

p
ij

⁢

x
j

⁡

(

1
-

x
i

)

wherein i is the item at issue in an item category, from among items 1 to N, x is a binary indicating whether an item is included in an assortment, s is a forecasted sales volume, j represents a substitute of item i, and p represents a proportion of customers who would substitute item i for item j, subject to constraints:

∑

i
=
1

Assignees

• Target Brands Inc

Inventors

• Pande Amit
• Das Gupta Aparupa
• Ni Kai
• Biswas Rahul
• Majumdar Sayon

Classifications

• G06Q30/0202

  Market predictions or forecasting for commercial activities · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title