Forecasting returns for retail demand planning

Patent metadata

Field	Value
Publication number	US-10318921-B1
Application number	US-201815868540-A
Country	US
Kind code	B1
Filing date	Jan 11, 2018
Priority date	Jan 12, 2017
Publication date	Jun 11, 2019
Grant date	Jun 11, 2019

Abstract

A system and method are disclosed for returns forecasting for a retail store inventory of one or more supply chain entities. Embodiments include receiving a current state of items in a supply chain network, receiving a sales time series and a returns time series, computing a returns forecast comprising an expected quantity of a particular product to be returned for a future time period using a sales forecast a and a transfer function estimated from the sales time series and the returns time series.

First claim

What is claimed is:

1. A computer-implemented method for returns forecasting for a retail store inventory of one or more supply chain entities, comprising:
receiving, by a computer, a current state of items in a supply chain network comprising one or more supply chain entities, wherein an inventory of the one or more supply chain entities is used to store one or more items, and a state of the items comprises a quantity and ordered flow among the inventory of the one or more supply chain entities;
receiving, by the computer, a sales time series and a returns time series, the sales time series comprising a sequence of sales quantities of a particular product for at least two steps and a returns time series comprising a sequence of returns quantities of the particular product for at least two time steps;
computing, by the computer, a returns forecast comprising an expected quantity of the particular product to be returned for a future time period, the returns forecast computed using a sales forecast and a transfer function, the transfer function estimated from the sales time series and the returns time series and comprising one or more weights, each of the one or more weights associated with a time period and comprising a probability that the particular product will be returned during the associated time period;
transporting, by a transportation network, at least one item of the particular product based, at least in part, on the sales forecast and the returns forecast.

2. The computer-implemented method of claim 1 , further comprising:
recording, by a point of sale system, one or more transactions of one or more items of the particular product by scanning an identifier associated with each of the one or more items and identifying the particular product based, at least in part, on the scan;
tabulating, by the computer, the one or more transactions of the one or more items of the particular product; and
generating, by the computer, the sales time series and the returns time series based, at least in part, on the tabulated one or more transactions.

3. The computer-implemented method of claim 2 , wherein the one or more transactions comprise one or more of a sales transaction and a returns transaction.

4. The computer-implemented method of claim 3 , wherein the computer computes the weights of the transfer function by minimizing the error between an estimated returns quantity and an actual returns quantity using a normalized least mean square method.

5. The computer-implemented method of claim 4 , further comprising:
updating, by the computer, the weights of the transfer function by computing an error between a quantity of items of the particular product predicted by the transfer function for a time step and the actual quantity of items returned.

6. The computer-implemented method of claim 5 , wherein the returns forecast comprises an expected quantity of returned items from a customer segment, the customer segment identified based, at least in part, on a customer behavior the influences the likelihood of a customer in the customer segment to return an item at a particular location.

7. The computer-implemented method of claim 6 , wherein the returns time series comprises an aggregation of a quantity of returned items from a second product.

8. A system of returns forecasting for a retail store inventory of one or more supply chain entities, comprising:
a computer, comprising a processor and a memory, and configured to:
receive a current state of items in a supply chain network comprising one or more supply chain entities, wherein an inventory of the one or more supply chain entities is used to store one or more items, and a state of the items comprises a quantity and ordered flow among the inventory of the one or more supply chain entities;
receive a sales time series and a returns time series, the sales time series comprising a sequence of sales quantities of a particular product for at least two steps and a returns time series comprising a sequence of returns quantities of the particular product for at least two time steps; and
compute a returns forecast comprising an expected quantity of the particular product to be returned for a future time period, the returns forecast computed using a sales forecast and a transfer function, the transfer function estimated from the sales time series and the returns time series and comprising one or more weights, each of the one or more weights associated with a time period and comprising a probability that the particular product will be returned during the associated time period; and

a transportation network, wherein the transportation network transports at least one item of the particular product based, at least in part, on the sales forecast and the returns forecast.

9. The system of claim 8 , further comprising:
a point of sale system, wherein the point of sale system records one or more transactions of one or more items of the particular product by scanning an identifier associated with each of the one or more items and identifying the particular product based, at least in part, on the scan.

10. The system of claim 9 , wherein the computer is further configured to:
tabulate the one or more transactions of the one or more items of the particular product; and
generate the sales time series and the returns time series based, at least in part, on the tabulated one or more transactions.

11. The system of claim 10 , wherein the one or more transactions comprise one or more of a sales transaction and a returns transaction.

12. The system of claim 11 , wherein the computer computes the weights of the transfer function by minimizing the error between an estimated returns quantity and an actual returns quantity using a normalized least mean square method.

13. The system of claim 12 , wherein the computer is further configured to:
update the weights of the transfer function by computing an error between a quantity of items of the particular product predicted by the transfer function for a time step and the actual quantity of items returned.

14. The system of claim 13 , wherein the returns forecast comprises an expected quantity of returned items from a customer segment, the customer segment identified based, at least in part, on a customer behavior the influences the likelihood of a customer in the customer segment to return an item at a particular location.

15. A non-transitory computer-readable medium embodied with software, the software when executed configured to forecast returns for a retail store inventory of one or more supply chain entities by:
receiving a current state of items in a supply chain network comprising one or more supply chain entities, wherein an inventory of the one or more supply chain entities is used to store one or more items, and a state of the items comprises a quantity and ordered flow among the inventory of the one or more supply chain entities;
receiving a sales time series and a returns time series, the sales time series comprising a sequence of sales quantities of a particular product for at least two steps and a returns time series comprising a sequence of returns quantities of the particular product for at least two time steps;
computing a returns forecast comprising an expected quantity of the particular product to be returned for a future time period, the returns forecast computed using a sales forecast and a transfer function, the transfer function estimated from the sales time series and the returns time series and comprising one or more weights, each of the one or more weights associated with a time period and comprising a probability tha

Assignees

• Jda Software Group Inc

Inventors

• Tilly Philippe Jean-Marc

Classifications

• G07F7/06

  by returnable containers {, i.e. reverse vending systems in which a user is rewarded for returning a container that serves as a token of value}, e.g. bottles · CPC title

• G06Q20/407

  Cancellation of a transaction · CPC title

• G06Q30/0202

  Market predictions or forecasting for commercial activities · CPC title

• G06Q10/087 Primary

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

• G06Q20/203

  Inventory monitoring · CPC title