METHOD AND SYSTEM FOR GENERATING DISAGGREGATED DEMAND FORECASTS FROM ENSEMBLE DEMAND FORECASTS

• US 20200134641A1
• Filed: 10/26/2018
• Published: 04/30/2020
• Est. Priority Date: 10/26/2018
• Status: Active Grant

• Current Assignee: Target Brands Inc. (Target Corporation)
• Original Assignee: Target Brands Inc. (Target Corporation)
• Inventors: Morgan, Adam, Das, Anand, Shah, Samir H., Yelland, Phillip
• Granted Patent: US 11,295,324 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 3/04817 using icons graphical or visual programming using iconic symbols G06F8/34
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

Methods and systems for forecasting demand for a plurality of items are provided. In particular, the demand forecasting system and methods described herein are useful for predicting demand of products in a retail context. Forecast models are built and used to score incoming sales data to predict future demand for items. Forecast models are validated by evaluating actual demand against predicted demand and using that information to inform how future ensemble forecast will be generated. Forecasts may be broken down into smaller components to satisfy a variety of requests for data from client applications.

Claims

1. A system for forecasting demand for one or more items in a given time period and location, the system comprising:

an enterprise forecasting engine configured to generate aggregate demand forecasts for all sales of each of a plurality of items within a first time period;

a forecast data store configured to store the aggregate demand forecasts;

a server comprising an API and a disaggregation service stored in memory and executable thereon, the API causing the computing system, when executed, to;

receive and process a client request, the request comprising at least a selection of one or more items and a selection of one or more of a subset of sales or a second time period that is smaller than the first time period, the client requests comprising at least a selection of one or more items, and

request and receive an aggregate demand forecast;

the disaggregation service causing the computing system, when executed, to;

compute a broken down demand forecast from the aggregate demand forecasts based on the request, and

output the broken down demand forecast to the client.

2. The system of claim 1, wherein the disaggregation service computes the broken down demand forecast by:

calculating a forecasted ensemble mean;

determining an ensemble variance;

estimate sales intensities per store; and

determine a relative sales efficiency of a selected store compared to all stores in a retail chain for an item.

3. The system of claim 1, wherein the aggregate demand forecast is for one week of sales across all stores in a retail chain for one item.

4. The system of claim 3, wherein the request is for a single day forecast.

5. The system of claim 3, wherein the request is for a single store forecast.

6. The system of claim 1, wherein the server further comprises a CDF service configured to calculate a forecast distribution from an aggregate demand forecast.

7. A method of disaggregating an aggregate demand forecast, the method comprising:

receiving and processing a client request for a demand forecast;

requesting and receiving an aggregate demand forecast corresponding to the client request;

calculating a forecasted ensemble mean of the aggregate demand forecast;

determining an ensemble variance;

estimating sales intensities per store based on the ensemble mean and ensemble variance;

determining a relative sales efficiency of one or more stores corresponding to the request compared to all stores in a retail chain for an item; and

outputting a disaggregated demand forecast to the client.

8. The method of claim 7, wherein the request includes a time period, a location, and an item.

9. The method of claim 8, wherein the aggregate demand forecast comprises a forecast over an aggregate time period longer than the time period, and over a plurality of locations.

10. The method of claim 7, wherein the sales intensities are based, at least in part, on a Sales Intensity Function (SIF) provides a distribution of the expected values or average sales per unit time amongst all stores selling an item.

11. The method of claim 7, wherein the disaggregated demand forecast is derived at least in part on a fractional contribution of each of a plurality of individual store locations aggregated over time.

12. The method of claim 7, further comprising determining an instantaneous sales intensity for at least one store location from among a plurality of locations.

13. The method of claim 12, wherein determining the instantaneous sales intensity for the at least one store location comprises inferring the instantaneous sales intensity from a combination of an instantaneous value for a Sales Intensity Function (SIF) and a known rank-order of sales performance at least of the plurality of locations.

14. A non-transitory computer-readable storage medium comprising computer-executable instruction which, when executed by a computing system, cause the computing system to perform a method of generating a demand forecast for one or more items over a selected time period for a selected location, the method comprising:

receiving and processing a client request for a demand forecast;

requesting and receiving an aggregate demand forecast corresponding to the client request;

determining a relative sales efficiency of one or more stores corresponding to the request compared to all stores in a retail chain for an item; and

outputting a disaggregated demand forecast to the client.

15. The non-transitory computer-readable storage medium of claim 14, wherein the relative sales efficiency is determined by:

calculating a forecasted ensemble mean of the aggregate demand forecast;

determining an ensemble variance; and

estimating sales intensities per store based on the ensemble mean and ensemble variance.

16. The non-transitory computer-readable storage medium of claim 14, wherein the request includes a time period, a location, and an item.

17. The non-transitory computer-readable storage medium of claim 16, wherein the aggregate demand forecast comprises a forecast over an aggregate time period longer than the time period, and over a plurality of locations.

18. The non-transitory computer-readable storage medium of claim 14, wherein the aggregate demand forecast comprises an ensemble forecast generated according to the following equation:

19. The non-transitory computer-readable storage medium of claim 14, further comprising exposing an API via which the client request is received and the disaggregated demand forecast is output to the client.

20. The non-transitory computer-readable storage medium of claim 14, wherein the request is for a single store forecast on a single day.