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Applying Contemporary Forecasting and Computer Technology for Competitive Advantage in Service Operations


Author : Lee, T. S.; Feller, S.; Adam, E. E., Jr.
Category : Journal Article
Department : Supply Chain and Information Management
Year / Month : 1992
ISSN: 0144-3577
Source : International Journal of Operations and Production Management, Vol.12, No.5, pp.28-42.

Abstract

    Applies time-series forecasting, a traditional operations analysis methodology, to develop a forecasting procedure and ordering policy for a natural-gas customer of Columbia Gas of Ohio, USA. Evaluates six time-series methods and four operating policies against four commonly used measures of error and the cost consequences of error to the customer. Demonstrates that time-series forecasting and decision theory developed by operations and applied in an actual industrial situation can become a powerful marketing technique. Provides further insights into evaluating forecasting models and ordering policies, demonstrating that introducing optimal planned bias is a robust decision-making/forecasting approach within services. There are three parts to the study. The first is a straightforward testing of forecasting methods, using the forecasts as the natural-gas ordering policy. Results vary depending upon how well forecasts are fitted to the data. For example, one inaccurate forecast with a poor fit incurs a penalty cost of $179,270, while the best forecast results in a penalty cost of $27,081. The second part evaluates two additional complex ordering rules with the same forecasting methods, further reducing the lowest cost to $17,709. The third part is a technical analysis reflecting a redesign of the study, demonstrating the difficulty of generalizing when characteristics of the underlying demand change. Concludes that the best forecasting model/operating policy is to use the very basic forecasting model of simple moving average (or the equivalent, first-order exponential smoothing) combined with an optimal planned bias ordering policy, i.e. with the planned introduction of bias.