# US 10,318,921 B1 · Forecasting returns for retail demand planning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,318,921 B1 - Document ID: US10318921B1 - Status: Granted - Kind: B1 - Application number: 15/868,540 - Filed: 2018-01-11 - Granted: 2019-06-11 - Earliest claimed: 2017-01-12 - Assignees: JDA Software Group, Inc. - Applicants: JDA Software Group, Inc. - Inventors: Philippe Jean-Marc Tilly - Web page: https://patentgrep.com/patent/US10318921B1 ## CPC - G06Q 10/087: Inventory or stock management, e.g. order filling, procurement or balancing against orders - G06Q 10/08728: {of returned inventory} - G06Q 20/203: {Inventory monitoring} - G06Q 20/208: {Input by product or record sensing, e.g. weighing or scanner processing} - G06Q 20/407: {Cancellation of a transaction} - G06Q 30/0202: Market predictions or forecasting for commercial activities - G07F 7/06: 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 - G06Q 10/083: Shipping - G06Q 10/08744: {by distributed inventory management} - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G07F: COIN-FREED OR LIKE APPARATUS (coin sorting G07D3/00; coin testing G07D5/00; {handling coins or paper currencies apart from payment activated apparatus G07D; payment architectures, schemes or protocols G06Q20/00}) ## 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. ## Claims ### Claim 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. ### Claim 2 (depends on 1) 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. ### Claim 3 (depends on 2) 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. ### Claim 4 (depends on 3) 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. ### Claim 5 (depends on 4) 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. ### Claim 6 (depends on 5) 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. ### Claim 7 (depends on 6) 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. ### Claim 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. ### Claim 9 (depends on 8) 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. ### Claim 10 (depends on 9) 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. ### Claim 11 (depends on 10) The system of claim 10, wherein the one or more transactions comprise one or more of a sales transaction and a returns transaction. ### Claim 12 (depends on 11) 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. ### Claim 13 (depends on 12) 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. ### Claim 14 (depends on 13) 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. ### Claim 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 that the particular product will be returned during the associated time period; and initiating the transport by a transportation network of at least one item of the particular product based, at least in part, on the sales forecast and the returns forecast. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the software when executed is further configured to: record one or more transactions of one or more items of the particular product by identifying the particular product based, at least in part, on a scan of an identifier associated with each of the one or more items. ### Claim 17 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein the software when executed 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. ### Claim 18 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the one or more transactions comprise one or more of a sales transaction and a returns transaction. ### Claim 19 (depends on 18) The non-transitory computer-readable medium of claim 18, wherein computing the weights of the transfer function o minimizing the error between an estimated returns quantity and an actual returns quantity using a normalized least mean square method. ### Claim 20 (depends on 19) The non-transitory computer-readable medium of claim 19, wherein the software 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.