METHODS AND SYSTEMS FOR FORECASTING PRODUCT DEMAND USING A CAUSAL METHODOLOGY

• US 20080154693A1
• Filed: 12/20/2006
• Published: 06/26/2008
• Est. Priority Date: 12/20/2006
• Status: Abandoned Application

• Current Assignee: Teradata US, Inc. (Teradata Corporation)
• Original Assignee: Teradata US, Inc. (Teradata Corporation)
• Inventors: Kim, Edward, Bateni, Arash, Vorsanger, Jean-Philippe, Liew, Philip
• Application Number:
  US11/613,404
• Publication Number:
  US 20080154693A1
• Time in Patent Office: Days
• US Class Current: 705/10
• 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 30/0206 Price or cost determination based on market factors

Abstract

An improved method for forecasting and modeling product demand for a product. The forecasting methodology employs a causal methodology, based on multiple regression techniques, to model the effects of various factors on product demand, and hence better forecast future patterns and trends, improving the efficiency and reliability of the inventory management systems. The demand forecasting technique seeks to establish a cause-effect relationship between product demand and factors influencing product demand in a market environment. Such factors may include current and recent product sales rates, seasonality of demand, product price changes, promotional activities, weather forecasts, competitive information are examples of the other primary factors which can be modeled. A product demand forecast is generated by blending the various influencing factors in accordance with corresponding regression coefficients determined through the analysis of historical product demand and factor information.

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;

identifying a plurality of factors influencing demand for said product;

analyzing said historical product demand information for said product to determine a plurality of regression coefficients corresponding to said plurality of factors;

blending said plurality of regression coefficients and corresponding plurality of factors 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, wherein said database of historical product demand information includes information concerning product demand, product pricing, and product promotions.

3. The method for forecasting product demand for a product in accordance with claim 2, wherein said plurality of factors includes:

an average rate of sale (ARS) value for said product calculated for a current weekly forecast period;

a previous average rate of sale (ARS) value for said product calculated for a weekly forecast period preceding said current weekly forecast period;

a long term average rate of sale (ARS) value for said product; and

a price driven demand value for said product specifying a relationship between the price of said product and the demand for said product.

4. The method for forecasting product demand for a product in accordance with claim 3, wherein said plurality of factors further includes:

a promotional factor value for said product specifying a relationship between promotions for said product and the demand for said product.

5. The method for forecasting product demand for a product in accordance with claim 2, wherein said step of blending said plurality of regression coefficients and corresponding plurality of factors for said product to determine a product demand forecast for said product comprises the step of:

determining said product demand forecast (D) in accordance with the equation;

D=α

.D<sub>−

1</sub>+β

.D<sub>−

2</sub>+γ

.D<sub>−

52</sub>+λ

.PRICE+δ

.PROMO+η

, where;

D<sub>−

1</sub> is an average rate of sale (ARS) value for said product calculated for a current weekly forecast period;

D<sub>−

2</sub> is a previous average rate of sale (ARS) value for said product calculated for a weekly forecast period preceding said current weekly forecast period;

D<sub>−

52</sub> a long term average rate of sale (ARS) value for said product;

PRICE is a price driven demand value for said product specifying a relationship between the price of said product and the demand for said product;

PROMO is a promotional factor value for said product specifying a relationship between promotions for said product and the demand for said product; and

α

, β

, ?, ?, and d are regression coefficients corresponding to factors D<sub>−

1</sub>, D<sub>−

2</sub>, D<sub>−

52</sub>, PRICE, and PROMO, respectively, which determine the relative importance of said factors in determining a product demand forecast for said product.

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

maintaining a database of historical product demand information;

determining at weekly intervals a current weekly average rate of sale (ARS) and a 52 week average rate of sale (ARS) for said product from said historical product demand information;

analyzing said historical product demand information for said product to determine a price driven demand value for said product specifying a relationship between the price of said product and the demand for said product.

analyzing said historical product demand information for said product to determine a promotional factor value for said product specifying a relationship between promotions for said product and the demand for said product; and

determining said product demand forecast (D) in accordance with the equation;

D=α

.D<sub>−

1</sub>+β

.D<sub>−

2</sub>+γ

.D<sub>−

52</sub>+λ

.PRICE+δ

.PROMO+η

, where;

D<sub>−

1</sub> is an average rate of sale (ARS) value for said product calculated for a current weekly forecast period;

D<sub>−

2</sub> is a previous average rate of sale (ARS) value for said product calculated for a weekly forecast period preceding said current weekly forecast period;

D<sub>−

52</sub> a long term average rate of sale (ARS) value for said product;

PRICE is a price driven demand value for said product specifying a relationship between the price of said product and the demand for said product;

PROMO is a promotional factor value for said product specifying a relationship between promotions for said product and the demand for said product; and

α

, β

, ?,?, and d are regression coefficients corresponding to factors D<sub>−

1</sub>, D<sub>−

2</sub>, D<sub>−

52</sub>, PRICE, and PROMO, respectively, which determine the relative importance of said factors in determining a product demand forecast for said product.

7. The method for forecasting product demand for a product in accordance with claim 6, further comprising the step of:

analyzing said historical product demand information for said product to determine values for said regression coefficients.