FORECASTING DEMAND FOR GROUPS OF ITEMS, SUCH AS MOBILE PHONES

• US 20200134649A1
• Filed: 12/30/2019
• Published: 04/30/2020
• Est. Priority Date: 11/06/2015
• Status: Active Grant

• Current Assignee: T-Mobile USA, Inc. (Deutsche Telekom AG)
• Original Assignee: T-Mobile USA, Inc. (Deutsche Telekom AG)
• Inventors: Boese, Mike
• Granted Patent: US 11,205,185 B2
• Time in Patent Office: Days
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

In some embodiments, the systems and methods obtain parameters for a set of mobile devices (or, other items), such as volume information, price information, and perceived value information for each mobile device within the set of mobile devices. The set of mobile devices may be a tier of substitute mobile devices that each affect consumer demand of other mobile devices within the set of mobile devices. The systems and methods may then determine substitution factors for the obtained parameters associated with the set of mobile devices, generate a substitution forecast for the set of mobile devices that is based on the substitution factors determined for the obtained parameters associated with the mobile devices, and determine a consensus forecast for the set of mobile devices that adjusts a baseline forecast for the demand of the set of mobile devices using the substitution forecast.

Claims

1. A computerized method of improving accuracy and reducing processing time in forecasting demand for a set of items, comprising:

obtaining, by a computing device, parameters for a set of items;

wherein the obtained parameters include volume information, price information, and perceived value information for each item of the set of items;

determining, by the computing device, substitution factors for the obtained parameters associated with the items within the set of items,

wherein determining the substitution factors for the obtained parameters associated with the items within the set of items includes generating a combined substitution factor matrix that is based on a weighted combination of values within one or more matrices;

generating, by the computing device, a substitution forecast for the set of items that is based on the substitution factors determined for the obtained parameters associated with the items; and

determining, by the computing device, a consensus forecast for the set of items that is based on a combination of a baseline forecast for the set of items, a forecast adjustment to the baseline forecast based on promotion information for the set of items, and the substitution forecast for the set of items.

2. The computerized method of claim 1, wherein the one or more matrices includes a volume based substitution factor matrix that is based on the volume information for the set of items.

3. The computerized method of claim 2, wherein the set of items includes a first mobile device and multiple substitute mobile devices, wherein a substitute mobile device is a mobile device whose sales affects the substitution set sales of other mobile devices within the set of items, wherein the volume based substitution factor matrix has matrix values of volume based substitution factors, for each of the substitute mobile devices, determined by:

(volume amount of the substitute mobile device)/(total volume amount of set of mobile devices−

volume amount of first device).

4. The computerized method of claim 1, wherein the one or more matrices includes a perceived value based substitution factor matrix that is based on the perceived value information for the set of items.

5. The computerized method of claim 1, wherein the perceived value information is information indicating a rating of an item with respect to other items within the set of items.

6. The computerized method of claim 1, wherein generating the substitution forecast for the set of items includes applying the combined substitution factor matrix to a determined amount of cannibalized devices for the substitution set.

7. The computerized method of claim 1, the method further comprising adjusting, by the computing device, the baseline forecast based on the promotion information for the set of items.

8. The computerized method of claim 1, wherein the items are one of smart phones, tablet computing devices or mobile device accessories.

9. The computerized method of claim 1, wherein the set of items includes a tier of substitute items that each affect consumer demand of other items within the set of items.

10. At least one non-transitory computer-readable storage medium, carrying instructions, that when executed by at least one data processor cause the data processor to perform a method that improves accuracy and processing time in forecasting demand for a set of items, the method comprising:

obtaining parameters for a set of items;

wherein the obtained parameters include volume information, price information, and perceived value information for each item of the set of items;

determining substitution factors for the obtained parameters associated with the items within the set of items,

wherein determining the substitution factors for the obtained parameters associated with the items within the set of items includes generating a combined substitution factor matrix that is based on a weighted combination of values within one or more matrices;

generating a substitution forecast for the set of items that is based on the substitution factors determined for the obtained parameters associated with the items; and

determining a consensus forecast for the set of items that is based on a combination of a baseline forecast for the set of items, a forecast adjustment to the baseline forecast based on promotion information for the set of items, and the substitution forecast for the set of items.

11. The at least one non-transitory computer-readable storage medium of claim 10, wherein the one or more matrices includes a volume based substitution factor matrix that is based on the volume information for the set of items.

12. The at least one non-transitory computer-readable storage medium of claim 11, wherein the set of items includes a first mobile device and multiple substitute mobile devices, wherein a substitute mobile device is a mobile device whose sales affects the substitution set sales of other mobile devices within the set of items, wherein the volume based substitution factor matrix has matrix values of volume based substitution factors, for each of the substitute mobile devices, determined by:

(volume amount of the substitute mobile device)/(total volume amount of set of mobile devices−

volume amount of first device).

13. The at least one non-transitory computer-readable storage medium of claim 10, wherein the one or more matrices includes a perceived value based substitution factor matrix that is based on the perceived value information for the set of items.

14. The at least one non-transitory computer-readable storage medium of claim 10, wherein the perceived value information is information indicating a rating of an item with respect to other items within the set of items.

15. The at least one non-transitory computer-readable storage medium of claim 10, wherein generating the substitution forecast for the set of items includes applying the combined substitution factor matrix to a determined amount of cannibalized devices for the substitution set.

16. The at least one non-transitory computer-readable storage medium of claim 10, wherein the method further comprises adjusting the baseline forecast based on the promotion information for the set of items.

17. A system for improving accuracy and reducing processing time in forecasting demand of a set of items, the system comprising:

at least one hardware computer, wherein the computer is configured to execute software modules, including;

a substitution set module that obtains parameters for a set of items,

wherein the obtained parameters include volume information, price information, and perceived value information for each item in the set of items;

one or more matrix modules that determines substitution factors for the obtained parameters associated with the items within the set of items,

wherein determining the substitution factors for the obtained parameters associated with the items within the set of items includes generating a combined substitution factor matrix that is based on a weighted combination of values within one or more matrices;

a substitution forecast module that generates a substitution forecast for the set of items that is based on the substitution factors determined for the obtained parameters associated with the items; and

a consensus forecast module that determines a consensus forecast for the set of items that is based on a combination of a baseline forecast for the set of items, a forecast adjustment to the baseline forecast based on promotion information for the set of items, and the substitution forecast for the set of items.

18. The system of claim 17, wherein the software modules further include a volume matrix module that generates a volume based substitution factor matrix that is based on the volume information for the set of items.

19. The system of claim 17, wherein the software modules further include a value matrix module that generates a perceived value based substitution factor matrix that is based on the perceived value information for the set of items.

20. The system of claim 17, wherein the perceived value information is information indicating a rating of a mobile device with respect to other mobile devices within the set of items.