IMPROVED METHODS AND SYSTEMS FOR FORECASTING PRODUCT DEMAND USING PRICE ELASTICITY

• US 20080133313A1
• Filed: 12/04/2006
• Published: 06/05/2008
• Est. Priority Date: 12/04/2006
• Status: Abandoned Application

• Current Assignee: Teradata US, Inc. (Teradata Corporation)
• Original Assignee: Teradata US, Inc. (Teradata Corporation)
• Inventors: Vorsanger, Jean-Philipe, Bateni, Arash, Kim, Edward, Liew, Philip
• Application Number:
  US11/566,357
• Publication Number:
  US 20080133313A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation

Abstract

An improved method for forecasting and modeling product demand for a product. The forecasting methodology blends information about the future price of a product with historical sales data to better forecast the future product demand. This forecasting methodoloy takes into account three main parameters that may affect the future demand for a product: seasonality (using seasonal factors), recent sales trends (through average rate of sale analysis) and the product price (by estimating the price driven demand).

Claims

1. A method for forecasting product demand for a product, the method comprising the steps of:

maintaining a database of historical product demand information;

analyzing said historical product demand information for said product to determine a price driven demand for said product;

analyzing said historical product demand information for said product to determine an average rate of sale for said product; and

blending said price driven demand for said product with said average rate of sale for said product to determine a product demand forecast for said product.

2. The method for forecasting product demand for a product in accordance with claim 1, further comprising the steps of:

analyzing said historical product demand information for said product to determine seasonal factors for said product; and

modifying said product demand forecast for said product through application of said seasonal factors.

3. The method for forecasting product demand for a product in accordance with claim 1, wherein said step of blending said price driven demand for said product (Q^p_i+1) with said average rate of sale for said product (ARS) to determine a product demand forecast for said product (Q_i+1) comprises the step of:

determining said product demand forecast (Q_i+1) in accordance with the equation;

Q_i+1=(β

.ARS_i+1+(1−

β

).Q^p_i+1), where β

(beta) is a blending factor which determines the relative importance of the average rate of sale for said product (ARS) versus the price driven demand for said product (Q^p_i+1) in future demand.

4. The method for forecasting product demand for a product in accordance with claim 3, wherein:

β

(beta) is determined through a parameter estimation process to minimize forecast error in said product demand forecast.

5. The method for forecasting product demand for a product in accordance with claim 2, wherein said product demand forecast (Q_i+1) is determined in accordance with the equation:

Q_i+1=(β

.ARS_i+1+(1−

β

).Q^p_i+1)SF_i+1, where β

(beta) is a blending factor which determines the relative importance of the average rate of sale for said product (ARS) versus the price driven demand for said product (Q^p_i+1) in future demand, and SF_i+1 is said seasonal factor.

6. The method for forecasting product demand for a product in accordance with claim 5, wherein:

β

(beta) is determined through a parameter estimation process to minimize forecast error in said product demand forecast.