Methods and apparatus for generating planograms

Patent metadata

Field	Value
Publication number	US-2023274225-A1
Application number	US-202217589740-A
Country	US
Kind code	A1
Filing date	Jan 31, 2022
Priority date	Jan 31, 2022
Publication date	Aug 31, 2023
Grant date	—

Abstract

In some examples, a system may configured to execute the instructions to, based on the modular data of a peg modular, the item data and the draw strategy data, implement a set of modular placement optimization operations that generate a first modular dataset. In some examples, the set of modular placement optimization operations includes, determining, from a group of items, a combination of items to place onto the peg modular and, for each item of the combination of items, a placement position on the peg modular, and a number of facings. Moreover, the set of modular placement optimization operations includes generating the first modular dataset. Further, the system may be configured to execute the instructions to, based at least on the modular data the first modular dataset, implement a set of operations that determine whether to add additional facings.

First claim

What is claimed is:

1 . A system comprising:
a memory resource storing instructions; and one or more processors coupled to the memory resource, the one or more processors configured to execute the instructions to:
obtain modular data of a peg modular, item data of a group of items of an item type of the peg modular, and draw strategy data;
based on the modular data, the item data and the draw strategy data, implement a set of modular placement optimization operations that generate a first modular dataset, the set of modular placement optimization operations includes:
determining, from the group of items, a combination of items to place onto the peg modular and, for each item of the combination of items, a placement position on the peg modular, and a number of facings;
generating the first modular dataset, the first modular dataset identifying the combination of items and characterizing the corresponding placement position of each of the combination of items and the number of facings for each of the combination of items; and

based on the modular data, the item data, the first modular dataset, implement a set of operations that determine whether to add additional facings of one or more items of the combination of items.

2 . The system of claim 1 , wherein the one or more processors are configured to execute the instructions further to:
based on the modular data and the item data of the group of items, determine whether the peg modular can accommodate all items of the group of items.

3 . The system of claim 2 , wherein one or more processors are configured to execute the instructions further to:
based on determining the peg modular cannot accommodate all of the items of the group of items, implement a first set of operations that generate block data, wherein the first set of operations that generate the block data are based on the modular data and the item data of the group of items.

4 . The system of claim 3 , wherein the set of modular placement optimization operations is further based on the block data.

5 . The system of claim 2 , wherein the modular data includes data characterizing one or more peg flow areas, each of the one or more peg flow areas including a set of peg holes and each of the set of peg holes being configured to couple with one of a plurality of fixtures.

6 . The system of claim 5 , wherein the modular data includes information associated with one or more fixtures associated with the peg modular.

7 . The system of claim 6 , wherein the information associated with the one or more fixtures includes a fixture type, the fixture type including at least one of a snap rail fixture, a hook fixture, a bar fixture and a t-rack system fixture.

8 . The system of claim 1 , wherein the item data and the modular data is associated with a cluster of stores.

9 . The system of claim 1 , wherein the draw strategy data characterizes one or more draw strategy parameters.

10 . The system of claim 9 , wherein the one or more draw strategy parameters includes a blockiness parameter, blockiness elasticity parameter, striping parameter, and sequencing elasticity parameter.

11 . The system of claim 1 , wherein the combination of items is associated with a particular attribute.

12 . The system of claim 1 , wherein the item data identifies the group of items and characterizes, for each of the group of items, a corresponding rank, a corresponding sequence, and corresponding set of attributes.

13 . The system of claim 1 , wherein the one or more processors are configured to execute the instructions further to:
obtain constraint data, the set of modular placement optimization operations being further based on the constraint data.

14 . The system of claim 13 , wherein the constraint data includes data characterizing one or more user constraint parameters.

15 . The system of claim 1 , further comprising:
a communications interface configured to communicate with a computing device operated by a user, the one or more processors being further coupled to the communications interface; and
wherein the one or more processors are further coupled to the communications interface, the one or more processors being configured to execute the instructions further to:
obtain, from the computing device, user input, the user input including data characterizing the draw strategy data.

16 . The system of claim 15 , wherein each item of the group of items is associated with particular identifier of a plurality of identifiers, and wherein the one or more processors are configured to execute the instructions further to:
obtain, from the computing device, data indicating including multiple different items of the group of items together onto at least one fixture of the peg modular; and based on the obtained data, generate an aggregate identifier representing each identifier of the multiple different items; and wherein the one or more processors utilize the aggregate identifier when implementing the set of modular placement optimization operations.

17 . The system of claim 16 , wherein the first modular dataset includes the aggregate identifier instead of each identifier of each item of the multiple different items, and wherein one or more processors are configured to execute the instructions further to:
update the first modular dataset by replacing the aggregate identifier with each identifier of each item of the multiple different items.

18 . A computer-implemented method comprising:
obtaining, by a processor, modular data of a peg modular, item data of a group of items of an item type of the peg modular, and draw strategy data; based on the modular data, the item data and the draw strategy data, implementing, by the processor, a set of modular placement optimization operations that generate a first modular dataset, the set of modular placement optimization operations includes:
determining, from the group of items, a combination of items to place onto the peg modular and, for each item of the combination of items, a placement position on the peg modular, and a number of facings;
generating the first modular dataset, the first modular dataset identifying the combination of items and characterizing the corresponding placement position of each of the combination of items and the number of facings for each of the combination of items; and
based on the modular data, the item data, the first modular dataset, implementing, by the processor, a set of operations that determine whether to add additional facings of one or more items of the combination of items.

19 . The computer-implemented method of claim 18 , further comprising:
based on the modular data and the item data of the group of items, determining whether the peg modular can accommodate all items of the group of items.

20 . A non-transitory computer-readable medium storing instructions, that when executed by a processor, causes the processor to:
obtain modular data of a peg modular, item data of a group of items of an item type of the peg modular, and draw strategy data; based on the modular data, the item data and the draw strategy data, implement a set of modular placement optimization operations that generate a first modular dataset, the set of modular placement optimization operations includes:
determining, from the group of items, a combination of items to place onto the peg modular and, for each item of the combination of items, a placement position on the peg modular, and a number of facings;
generating the first modular da

Assignees

• Walmart Apollo Llc

Inventors

• Wang Jiewen
• Batham Mohit

Classifications

• G06Q30/0639 Primary

  Locating goods or services, e.g. based on physical position of the goods or services within a shopping facility · CPC title

• G06Q30/0641

  utilising user interfaces specially adapted for shopping · CPC title

• G06Q10/0637

  Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals · CPC title

• G06Q10/087 Primary

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title

• G06F30/20

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title