Method and system for combination of independent demand data streams

• US 20070016467A1
• Filed: 07/13/2005
• Published: 01/18/2007
• Est. Priority Date: 07/13/2005
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP SE
• Inventors: John, Thomas, Traeger, Konrad, Eftekhari, Newsha
• Granted Patent: US 9,747,560 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

A system and method of combining independent demand forecast streams. Demand data is extracted from each of the demand forecast streams. The extracted data is combined based on one or more criteria to yield a single resultant demand data set. The resultant demand data set is released for supply network planning rather than any original forecast data stream.

Claims

1. A method comprising:

extracting first demand data from a first forecast demand data stream;

extracting second demand data from a second forecast demand data stream;

combining the first and second demand data based on at least one criteria to form a resultant demand data set; and

releasing the resultant data set for supply network planning.

2. The method of claim 1 wherein combining comprises:

calculating an error measurement for the first and the second demand data; and

selecting demand data from the demand data having a least error as part of the resultant demand data set.

3. The method of claim 2 wherein calculating comprises:

determining one of absolute percentage error (APE) and weighted absolute percentage error (WAPE).

4. The method of claim 1 wherein combining comprises:

applying a first weighting factor to the first demand data to obtain a first weighted demand data;

applying a second weighting factor to the second demand data to obtain a second weighted demand data; and

summing the first weighted demand data with the second weighted demand data.

5. The method of claim 1 further comprising:

normalizing the first and second demand data to correspond to a common time interval.

6. The method of claim 1 further comprising:

comparing one of the first demand data and the second demand to previous demand data from a corresponding one of the first forecast demand data stream and the second forecast demand data stream; and

updating the combining if the comparing shows a difference exceeding a threshold.

7. The method of claim 1 wherein combining comprises:

traversing a decision tree.

8. The method of claim 7 wherein traversing comprises:

evaluating one or more binary conditions to identify an activity; and

performing the activity to generate a member of the resultant data set.

9. A system comprising:

a first extractor to extract first demand data from a first forecast demand data stream;

a second extractor to extract second demand data from a second forecast demand data stream;

a database to retain historical demand data; and

a combination module to combine the first and second demand data into a single resultant data set based, at least in part, on the historical demand data.

10. The system of claim 9 further comprising:

a demand planning module to provide the first forecast demand data stream; and

a responsive replenishment module to provide the second forecast demand data stream.

11. The system of claim 9 wherein the analysis module comprises:

a rule engine to evaluate the first and second demand data against a set of rules for combination.

12. The system of claim 11 further comprising:

a graphical user interface to permit modification of the set of rules.

13. The system of claim 9 wherein the analysis module comprises:

an error calculation component.

14. A machine-accessible medium containing instructions that when executed cause a machine to:

extract first demand data from a first forecast demand data stream;

extract second demand data from a second forecast demand data stream;

combine the first and second demand data based on at least one criteria to form a result and demand data set; and

release the resultant data set for supply network planning.

15. The machine accessible median of claim 14, wherein instructions causing the machine to combine, cause the machine to:

calculate an error measurement for the first and the second forecast demand data stream; and

select the demand data from the forecast demand data stream having a least error as part of the resultant demand data set.

16. The machine accessible median of claim 15 wherein instructions causing the machine to calculate, cause the machine to:

determine one of absolute percentage error (APE) and weighted absolute percentage error (WAPE).

17. The machine accessible median of claim 14, wherein instructions causing the machine to combine, cause the machine to:

apply a first weighting factor to the first demand data to obtain a first weighted demand data;

apply a second weighting factor to the second demand data to obtain a second weighted demand data; and

summarize the first weighted demand data with the second weighted demand data.

18. The machine accessible median of claim 14, wherein instructions further cause the machine to:

normalize the first and second demand data to correspond to a common time interval.

19. The machine accessible median of claim 14, wherein instructions further cause the machine to:

compare one of the first demand data and the second demand to previous demand data from a corresponding one the first forecast demand data stream and the second forecast demand data stream; and

update the combining if the comparing shows a difference exceeding a threshold.

20. The machine accessible median of claim 14, wherein instructions causing the machine to combine, cause the machine to:

traverse a decision tree.

21. The machine accessible median of claim 20, wherein instructions causing the machine to traverse cause the machine to:

evaluate one or more binary conditions to identify an activity; and

perform the activity to generate a member of the resultant data set.