# US 2020/0401967 A1 · Improved resource need forecasting tool > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401967 A1 - Document ID: US20200401967A1 - Status: Published application - Kind: A1 - Application number: 16/547,563 - Filed: 2019-08-22 - Published: 2020-12-24 - Earliest claimed: 2019-06-24 - Assignees: Walmart Apollo, LLC - Applicants: Walmart Apollo, LLC - Inventors: Akash Verma; Harshavardhan Yeruva; Kosha Modi - Web page: https://patentgrep.com/patent/US20200401967A1 ## CPC - G06Q 10/06315: {Needs-based resource requirements planning or analysis} - G06N 20/00: Machine learning - G06Q 10/087: Inventory or stock management, e.g. order filling, procurement or balancing against orders - 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 - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract An improved resource need forecasting tool enables more accurate forecasts by leveraging models (e.g., machine learning models) of short-term phenomena such as rapid growth that, if not properly accounted for, can distort forecasts. Improved forecasting provides more reliable labor need projections, and includes receiving activity data for a facility; producing normalized activity data adjusted for the short-term phenomena; forecasting, based at least on the normalized activity data, predicted activity data; adjusting, based at least on an activity cannibalization index, the predicted activity data for the first facility to produce adjusted predicted activity data; adjusting, the adjusted predicted activity data based at least on the short-term phenomena; and generating a resource need report. ## Claims ### Claim 1 A system for resource need forecasting, the system comprising: a data acquisition sensor located at a first facility; a processor; and a computer-readable medium storing instructions that are operative when executed by the processor to: receive activity data for the first facility from the data acquisition sensor; de-seasonalize, based at least on a seasonality index, the activity data for the first facility to produce normalized activity data for the first facility; forecast, based at least on the normalized activity data for the first facility, predicted activity data for the first facility; adjust, based at least on an activity cannibalization index, the predicted activity data for the first facility to produce adjusted predicted activity data for the first facility; adjust, based at least on the seasonality index, the adjusted predicted activity data for the first facility to produce re-seasonalized predicted activity data for the first facility; and generate, based at least on the re-seasonalized predicted activity data for the first facility, a resource need report. ### Claim 2 (depends on 1) The system of claim 1 wherein the data acquisition sensor comprises at least one sensor selected from the list consisting of: a sales transaction register, an instrumented e-commerce website, a container sensor, and an automobile drive-up sensor. ### Claim 3 (depends on 1) The system of claim 1 wherein the activity is an online order pickup activity. ### Claim 4 (depends on 1) The system of claim 1 wherein the resource need is at least one need at the first facility selected from the list consisting of: a labor need and an inventory need. ### Claim 5 (depends on 1) The system of claim 1 wherein the instructions are further operative to: determine the seasonality index, based at least on historical data collected from a plurality of facilities. ### Claim 6 (depends on 5) The system of claim 5 wherein the plurality of facilities excludes the first facility. ### Claim 7 (depends on 5) The system of claim 5 wherein the instructions are further operative to: collect the historical data from the plurality of facilities; determine, for each facility within the plurality of facilities, an expected activity; determine, for each facility within the plurality of facilities, a variation between actual activity and the expected activity; and determine, based at least on the variations for the plurality of facilities, the seasonality index. ### Claim 8 (depends on 1) The system of claim 1 further comprising at least one machine learning (ML) model selected from the list consisting of: a forecasting ML model, a seasonality index generation ML model, and an activity cannibalization index generation ML model. ### Claim 9 (depends on 8) The system of claim 8 wherein the instructions are further operative to: train the at least one ML model, based at least on historical data collected from a plurality of facilities. ### Claim 10 A method of resource need forecasting, the method comprising: receiving activity data for a first facility from a data acquisition sensor; de-seasonalizing, based at least on a seasonality index, the activity data for the first facility to produce normalized activity data for the first facility; forecasting, based at least on the normalized activity data for the first facility, predicted activity data for the first facility; adjusting, based at least on an activity cannibalization index, the predicted activity data for the first facility to produce adjusted predicted activity data for the first facility; adjusting, based at least on the seasonality index, the adjusted predicted activity data for the first facility to produce re-seasonalized predicted activity data for the first facility; and generating, based at least on the re-seasonalized predicted activity data for the first facility, a resource need report. ### Claim 11 (depends on 10) The method of claim 10 wherein the data acquisition sensor comprises at least one sensor selected from the list consisting of: a sales transaction register, an instrumented e-commerce website, a container sensor, and an automobile drive-up sensor. ### Claim 12 (depends on 10) The method of claim 10 wherein the activity is an online order pickup activity. ### Claim 13 (depends on 10) The method of claim 10 wherein the resource need is at least one need at the first facility selected from the list consisting of: a labor need and an inventory need. ### Claim 14 (depends on 10) The method of claim 10 further comprising: determining the seasonality index, based at least on historical data collected from a plurality of facilities. ### Claim 15 (depends on 14) The method of claim 14 wherein the plurality of facilities excludes the first facility. ### Claim 16 (depends on 14) The method of claim 14 wherein determining the seasonality index comprises: collecting the historical data from the plurality of facilities; determining, for each facility within the plurality of facilities, an expected activity; determining, for each facility within the plurality of facilities, a variation between actual activity and the expected activity; and determining, based at least on the variations for the plurality of facilities, the seasonality index. ### Claim 17 (depends on 10) The method of claim 10 further comprising: using at least one machine learning (ML) model to perform a task selected from the list consisting of: forecasting predicted activity data for the first facility, determining the seasonality index, and determining the activity cannibalization index. ### Claim 18 (depends on 17) The method of claim 17 further comprising: training the at least one ML model, based at least on historical data collected from a plurality of facilities. ### Claim 19 One or more computer storage devices having computer-executable instructions stored thereon for resource need forecasting, which, on execution by a computer, cause the computer to perform operations comprising: collecting historical data from a plurality of facilities; determining, for each facility within the plurality of facilities, an expected activity; determining, for each facility within the plurality of facilities, a variation between actual activity and the expected activity; determining, based at least on the variations for the plurality of facilities, a seasonality index; receiving activity data for a first facility from a data acquisition sensor, wherein the first facility is not within the plurality of facilities, and wherein the data acquisition sensor comprises at least one sensor selected from the list consisting of: a sales transaction register, an instrumented e-commerce website, a container sensor, and an automobile drive-up sensor; de-seasonalizing, based at least on the seasonality index, the activity data for the first facility to produce normalized activity data for the first facility; forecasting, based at least on the normalized activity data for the first facility, predicted activity data for the first facility; adjusting, based at least on an activity cannibalization index, the predicted activity data for the first facility to produce adjusted predicted activity data for the first facility; adjusting, based at least on the seasonality index, the adjusted predicted activity data for the first facility to produce re-seasonalized predicted activity data for the first facility; generating, based at least on the re-seasonalized predicted activity data for the first facility, a resource need report; using at least one machine learning (ML) model to perform a task selected from the list consisting of: forecasting predicted activity data for the first facility, determining the seasonality index, and determining the activity cannibalization index; and training the at least one ML model, based at least on the historical data collected from the plurality of facilities. ### Claim 20 (depends on 19) The one or more computer storage devices of claim 19 wherein the activity is an online order pickup activity, and wherein the resource need is at least one need at the first facility selected from the list consisting of: a labor need and an inventory need.