System and method for data-driven insight into stocking out-of-stock shelves

Patent metadata

Field	Value
Publication number	US-2018285902-A1
Application number	US-201815940295-A
Country	US
Kind code	A1
Filing date	Mar 29, 2018
Priority date	Mar 31, 2017
Publication date	Oct 4, 2018
Grant date	—

Abstract

Systems, methods, and non-transitory computer-readable storage media for generating rate-of-sales models for products based on historical data of when products are being sold, then generating, in real-time, schedules for restocking items on store shelfs such that the amount of items on the shelf maintain desired rates of sales per the prediction models. This is accomplished used historical data in combination with real-time data regarding the current shelf inventory, as well as updating machine-learning models to improve estimates over time.

First claim

We claim:

1 . A method comprising:
receiving, at a server and from a database, a current shelf inventory of a product on a shelf in a store; generating, at the server and based on the current shelf inventory of the product, and using a rate of sales model specific to the product, a schedule for restocking the product on the shelf; receiving, at the server, a real-time notification that at least one unit of the product has been removed from the shelf; modifying the current shelf inventory based on the notification, to yield an updated shelf inventory; modifying the rate of sales model based on the sale, to yield an updated rate of sales model specific to the product; generating, using the updated shelf inventory and the updated rate of sales model, an updated schedule for restocking the product on the shelf; and sending a request to restock the product on the shelf to an autonomous vehicle.

2 . The method of claim 1 , further comprising:
identifying, within one of the rate of sales model and the updated rate of sales model, an amount of inventory corresponding to an apex rate of sales of the product.

3 . The method of claim 2 , wherein at least one of the schedule and the updated schedule are generated to maintain shelf inventory of the product within a threshold range of the amount of inventory corresponding to the apex rate of sales.

4 . The method of claim 1 , wherein the modifying of the rate of sales model is performed using machine learning, wherein the machine learning uses a machine learning model which is updated on a periodic basis, the updates to the machine learning model being based on predictive ability of the machine learning model regarding sales of the product.

5 . The method of claim 1 , wherein the rate of sales model identifies distinct rates of sales of the product when the shelf is full and when the shelf is half-full.

6 . The method of claim 1 , wherein the rates of sales model identifies an amount of inventory for the shelf below which a rate of sales of the product decreases.

7 . The method of claim 1 , wherein the modifying of the current shelf inventory and the modifying of the rate of sales model occur in real-time after each sale of the product.

8 . The method of claim 1 , wherein the real-time notification is generated at a point of sale of the at least one unit.

9 . The method of claim 1 , wherein the real-time notification is generated based on a video feed analysis identifying a change in the current shelf inventory.

10 . The method of claim 1 , wherein the real-time notification is generated based on weight sensor associated with the shelf identifying a change in shelf weight associated with the current shelf inventory.

11 . A system comprising:
a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising:
receiving, from a database, a current shelf inventory of a product on a shelf in a store;
generating, based on the current shelf inventory of the product and using a rate of sales model specific to the product, a schedule for restocking the product on the shelf, wherein the rate of sales model has calculated rates of sale based on shelf inventory;
receiving a real-time notification from a camera monitoring the shelf that at least one unit of the product has been removed from the shelf;
modifying the current shelf inventory based on the notification, to yield an updated shelf inventory;
modifying the rate of sales model based on the sale, to yield an updated rate of sales model specific to the product; and
generating, using the updated shelf inventory and the updated rate of sales model, an updated schedule for restocking the product on the shelf.

12 . The system of claim 11 ,
identifying, within one of the rate of sales model and the updated rate of sales model, an amount of inventory corresponding to an apex rate of sales of the product.

13 . The system of claim 12 , wherein at least one of the schedule and the updated schedule are generated to maintain shelf inventory of the product within a threshold range of the amount of inventory corresponding to the apex rate of sales.

14 . The system of claim 11 , wherein the modifying of the rate of sales model is performed using machine learning, wherein the machine learning uses a machine learning model which is updated on a periodic basis, the updates to the machine learning model being based on predictive ability of the machine learning model regarding sales of the product.

15 . The system of claim 11 , wherein the rate of sales model identifies distinct rates of sales of the product when the shelf is full and when the shelf is half-full.

16 . The system of claim 11 , wherein the rates of sales model identifies an amount of inventory for the shelf below which a rate of sales of the product decreases.

17 . A non-transitory computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising:
receiving, from a database, a current shelf inventory of a product on a shelf in a store; generating, based on the current shelf inventory of the product and using a rate of sales model specific to the product, a schedule for restocking the product on the shelf, wherein the rate of sales model has calculated rates of sale based on shelf inventory; receiving a real-time notification from a camera monitoring the shelf that at least one unit of the product has been removed from the shelf; modifying the current shelf inventory based on the notification, to yield an updated shelf inventory; modifying the rate of sales model based on the sale, to yield an updated rate of sales model specific to the product; and generating, using the updated shelf inventory and the updated rate of sales model, an updated schedule for restocking the product on the shelf.

18 . The non-transitory computer-readable storage device of claim 17 ,
identifying, within one of the rate of sales model and the updated rate of sales model, an amount of inventory corresponding to an apex rate of sales of the product.

19 . The non-transitory computer-readable storage device of claim 18 , wherein at least one of the schedule and the updated schedule are generated to maintain shelf inventory of the product within a threshold range of the amount of inventory corresponding to the apex rate of sales.

Assignees

• Walmart Apollo Llc

Inventors

• Nazarian Ehsan
• Nemati Behzad

Classifications

• G06Q10/087

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

• G06F15/18

  Physics · mapped topic

• G06Q30/0202 Primary

  Market predictions or forecasting for commercial activities · CPC title

• G06F15/76

  Architectures of general purpose stored program computers (with program plugboard G06F15/08; multicomputers G06F15/16) · CPC title

• G06N20/00

  Machine learning · CPC title