Developments in Business Simulation and Experiential Learning, Volume 31, 2004
ONLINE SALES FORECASTING WITH THE MULTIPLE REGRESSION ANALYSIS DATA MATRICES PACKAGE
Aspy Palia University of Hawaii at Manoa aspy@cba.hawaii.edu
ABSTRACT
The Web-based Multiple Regression Analysis Data Matrices Package (developed jointly with Justin Yost) enables competing participant teams in the marketing simulation COMPETE to apply their knowledge of multiple regression analysis in sales forecasting. Participants with Web-access use this package to create nine data matrices (one data matrix for each strategic business unit) consisting of relevant predictor and response variables for each of the prior decision periods. Next, the data are screened for potential multicollinearity using correlation analysis. Then, the top two predictor variables that satisfy multiple regression analysis assumptions are used to build a linear unrestricted single-equation multiple regression model. The results are checked for potential heteroskedasticity.
SALES FORECASTING
A major responsibility of marketing is the preparation of sales forecasts. First, market opportunities are identified through marketing research. Then, the size, growth and profitability of each market opportunity are measured and/or forecasted. Sales forecasts are used (a) by finance to raise the needed cash for investment and operations, (b) by manufacturing to establish capacity and output levels, (c) by purchasing to acquire the necessary supplies, and (d) by human resources to hire the needed workers (Kotler 2003). Accurate sales forecasts facilitate effective and efficient allocation of scarce resources. Over-estimates of demand lead to several problems. First, excess inventory uses up valuable shelf space and leads to obsolescence. Next, scarce working capital blocked up in inventory carrying charges [funds used or borrowed (a) by manufacturers to produce goods, or (b) by retailers to purchase goods] cannot be used for other purposes such as R&D or promotional expenses. Third, storage charges are incurred to store excess inventory in public or private warehouses. Finally, margins are reduced when excess inventory is removed through end-of-year clearance sales. Under-estimates of demand lead to a different set of problems. First, stock-outs lead to wasted shelf space. Next, insufficient inventory leads to lost sales and consequent lost margins. Third, failure to keep up with customer demand may necessitate the use of limited and expensive overtime production leading to lower profitability. Finally, and most importantly, the firm may lose customers, when prospects facing an empty store shelf, try an alternative brand or go to an alternative store, and are satisfied by the competitive offering. Given the detrimental impact of inaccurate forecasts, marketers use a variety of sales forecasting techniques in order to forecast sales accurately.

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