METHOD AND SYSTEM FOR SEPARATING DEMAND MODELS AND DEMAND FORECASTS INTO CAUSAL COMPONENTS

• US 20120310705A1
• Filed: 06/06/2011
• Published: 12/06/2012
• Est. Priority Date: 06/06/2011
• Status: Abandoned Application

• Current Assignee: SAP SE
• Original Assignee: SAP SE
• Inventors: Ileri, Yetkin, Mneimneh, Mohamed, May, Brent Joseph, Castro, Gustavo Ayres de, Patel, Kautilya, Hutton, Geoffrey
• Application Number:
  US13/153,973
• Publication Number:
  US 20120310705A1
• 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

Abstract

According to some embodiments, a method and system provides separating demand models and forecasts into demand components thereof. The methods include receiving a demand model to forecast a demand for a product or service; decomposing the demand model into a plurality of distinct demand components, each of the demand components associated with a causal contribution to a demand for the product or service; and generating a demand forecast including an indication of the plurality of demand components associated with the causal contributions contributing to the demand forecast for the product or service. Some embodiments include a method to determine demand components of a demand model for a product or service based on historical data.

Claims

1. A computer-implemented method, the method comprising:

receiving a demand model to forecast a demand for a product or service;

decomposing, by a processor, the demand model into a plurality of distinct demand components, each of the demand components associated with a causal contribution to a demand for the product or service;

generating, by the processor, a demand forecast including a plurality of demand components associated with the causal contributions contributing to the demand forecast for the product or service; and

providing a output of the generated demand forecast including the plurality of demand components.

2. The method of claim 1, wherein the plurality of demand components are either additive contributions to the forecast demand or multiplicative contributions to the demand forecast.

3. The method of claim 2, wherein the plurality of demand components are one of:

all additive contributions to the demand forecast, all multiplicative contributions to the demand forecast, and a combination of additive contributions and multiplicative contributions to the demand forecast.

4. The method of claim 1, wherein the received demand model is linear comprising a base demand component and separable additive parameters, and the plurality of demand components are multiplicative contributions to the demand forecast derived from the linear demand model.

5. The method of claim 1, wherein the received demand model is non-linear.

6. The method of claim 5, wherein the non-linear demand model is multiplicative comprising a base demand component and separable multiplicative demand parameters, and the plurality of demand components are additive contributions to the demand forecast derived from the multiplicative demand model.

7. The method of claim 6, wherein the additive contributions are derived in a specific sequence from the multiplicative demand parameters of the demand model.

8. The method of claim 6, wherein the additive contributions are concurrently derived from the multiplicative demand parameters of the demand model.

9. The method of claim 6, wherein at least one of the additive contributions is derived in a specific sequence from the multiplicative demand parameters of the demand model and at least one of the additive contributions is concurrently derived from the multiplicative demand parameters of the demand model.

10. The method of claim 6, further comprising:

determining whether to derive the additive contributions from the multiplicative demand parameters of the demand model concurrently, in a specific sequence, or a combination of concurrently and specific sequence.

11. A computer-implemented method, the method comprising:

receiving historical sales data associated with a product or service;

receiving a demand model for the product or service, the demand model providing a modeled demand for the product or service;

mapping, by a processor, a plurality of distinct demand components of the demand model to the historical data, each of the demand components associated with a causal contribution to a demand for the product or service;

adjusting, by the processor, at least one of the mapped plurality of demand components to account for a difference between an actual demand for the product or service based on the historical sales data and the modeled demand; and

providing an output of the adjusted demand model.

12. The method of claim 11, further comprising decomposing, by the processor, the demand model into the plurality of distinct demand components.

13. The method of claim 11, wherein at least one of the mapped plurality of demand components is adjusted to account for a difference between the actual demand for the product or service based on the historical sales data and the modeled demand and remaining others of the plurality of demand components are not adjusted.

14. The method of claim 11, wherein the plurality of demand components includes a base demand component that is not adjusted.

15. A system, comprising:

a memory having program instructions stored thereon; and

a processor in communication with the memory, the processor being operative to;

receive a demand model to forecast a demand for a product or service;

decompose the demand model into a plurality of distinct demand components, each of the demand components associated with a causal contribution to a demand for the product or service; and

generate a demand forecast including an indication of the plurality of demand components associated with the causal contributions contributing to the demand forecast for the product or service.

16. The system of claim 15, wherein the plurality of demand components are either additive contributions to the forecast demand or multiplicative contributions to the demand forecast.

17. The system of claim 16 wherein the plurality of demand components are one of:

all additive contributions to the demand forecast, all multiplicative contributions to the demand forecast, and a combination of additive contributions and multiplicative contributions to the demand forecast.

18. The system of claim 15, wherein the received demand model is linear comprising a base demand component and separable additive parameters, and the plurality of demand components are multiplicative contributions to the demand forecast derived from the linear demand model.

19. The system of claim 15, wherein the received demand model is non-linear.

20. The system of claim 19, wherein the non-linear demand model is multiplicative comprising a base demand component and separable multiplicative demand parameters, and the plurality of demand components are additive contributions to the demand forecast derived from the multiplicative demand model.

21. The system of claim 20, wherein the additive contributions are derived in a specific sequence from the multiplicative demand parameters of the demand model.

22. The system of claim 20, wherein the additive contributions are concurrently derived from the multiplicative demand parameters of the demand model.

23. The system of claim 20, wherein at least one of the additive contributions is derive in a specific sequence from the multiplicative demand parameters of the demand model and at least one of the additive contributions is concurrently derived from the multiplicative demand parameters of the demand model.

24. The system of claim 20, wherein the processor is further operative to:

determine whether to derive the additive contributions from the multiplicative demand parameters of the demand model concurrently, in a specific sequence, or a combination of concurrently and specific sequence.