Demand planning with event-based forecasting

• US 20050288993A1
• Filed: 06/27/2005
• Published: 12/29/2005
• Est. Priority Date: 06/28/2004
• Status: Abandoned Application

• Current Assignee: Truedemand Software
• Original Assignee: Truedemand Software
• Inventors: Chen, Li, Weng, Jie, Lee, Hau, Blanchard, Raymond
• Application Number:
  US11/168,755
• Publication Number:
  US 20050288993A1
• Time in Patent Office: Days
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/08355 Routing methods
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

Methods and apparatus, including computer program products, are provided that include techniques for forecasting demand. One method includes identifying event data associated with a demand forecast. The method further includes determining a first portion of a demand forecast using the event data. The method further includes determining a second portion of the demand forecast using at least order history data. The method further includes identifying weights to be applied to the first and second portions. The method further includes determining an aggregate demand forecast including applying respective identified weights to and combining the first and second portions of the demand forecast.

Claims

1. A method for demand forecasting in a supply chain comprising:

identifying event data associated with a demand forecast;

determining a first portion of a demand forecast using the event data;

determining a second portion of the demand forecast using at least order history data;

identifying weights to be applied to the first and second portions; and

determining an aggregate demand forecast including applying respective identified weights to and combining the first and second portions of the demand forecast.

2. The method of claim 1, wherein the event data is real-time event data.

3. The method of claim 1, wherein the event data is downstream event data.

4. The method of claim 1, wherein the event data is RFD) data.

5. The method of claim 4, wherein the RFID data includes EPC data.

6. The method of claim 1, wherein the method is performed at a manufacturer distribution center for orders received from one or more retail distribution centers.

7. The method of claim 1, wherein the first portion is determined using event data corresponding to shipments made from a retail distribution center.

8. The method of claim 1, wherein determining the first portion includes determining a relationship between the event data and prior orders.

9. The method of claim 8, wherein determining a relationship includes determining a one-to-one relationship.

10. The method of claim 8, wherein determining a relationship includes determining a one-to-one relationship between shipment information from a retail distribution center to subsequent orders received therefrom.

11. The method of claim 1, wherein determining the second portion includes using historical order data from a respective downstream element in the supply chain to calculate a partial demand.

12. The method of claim 1, wherein determining weights includes determining predetermined weights.

13. The method of claim 1, wherein determining weights includes determining estimated weights.

14. The method of claim 13, wherein determining estimated weights includes determining estimates based on historical data associated with an associated product.

15. The method of claim 1, wherein determining the weights includes estimating the weights, the estimating including determining a correlation associated with the aggregate forecast using other data.

16. The method of claim 15, wherein the other data is historical data related to a demand forecast for a product.

17. The method of claim 15, wherein the other data is data related to another product.

18. The method of claim 15, wherein the other data is related to another downstream element.

19. The method of claim 1, wherein the aggregate demand forecast is associated with a product.

20. The method of claim 1, wherein the aggregate demand forecast is associated with a service.

21. The method of claim 1, wherein the aggregate demand forecast is associated with a product and the history data is associated with previous orders for the product received from a downstream element in a supply chain for the product.

22. A method for demand forecasting in a supply chain comprising:

identifying event data downstream from a location in a supply chain where a demand forecast is to be determined;

identifying a relationship of the event data with the demand forecast;

using the relationship to determine estimates for weights to be used in determining an associated aggregate demand forecast;

determining a first portion of a demand forecast using the event data;

determining a second portion of the demand forecast using at least order history data;

applying the estimated weights to the respective first and second portions; and

determining an aggregate demand forecast including combining the first and second portions of the demand forecast.

23. A method for demand forecasting in a supply chain comprising:

identifying event data associated with a first product downstream from a location in a supply chain where a demand forecast is to be determined;

determining a correlation between event data and order forecast in the supply chain;

using the correlation to determine estimates for weights to be used in determining an associated aggregate demand forecast for the second product;

determining a first portion of a demand forecast using the event data;

determining a second portion of the demand forecast using at least order history data;

applying the estimated weights to the respective first and second portions; and

determining an aggregate demand forecast for the second product including combining the first and second portions of the demand forecast.