Applies timeseries forecasting, a traditional operations analysismethodology, to develop a forecasting procedure and ordering policy fora naturalgas customer of Columbia Gas of Ohio, USA. Evaluates sixtimeseries methods and four operating policies against four commonlyused measures of error and the cost consequences of error to thecustomer. Demonstrates that timeseries forecasting and decision theorydeveloped by operations and applied in an actual industrial situationcan become a powerful marketing technique. Provides further insightsinto evaluating forecasting models and ordering policies, demonstratingthat introducing optimal planned bias is a robustdecisionmakingforecasting approach within services. There are threeparts to the study. The first is a straightforward testing offorecasting methods, using the forecasts as the naturalgas orderingpolicy. Results vary depending upon how well forecasts are fitted to thedata. For example, one inaccurate forecast with a poor fit incurs apenalty cost of 179,270, while the best forecast results in apenalty cost of 27,081. The second part evaluates two additionalcomplex ordering rules with the same forecasting methods, furtherreducing the lowest cost to 17,709. The third part is atechnical analysis reflecting a redesign of the study, demonstrating thedifficulty of generalizing when characteristics of the underlying demandchange. Concludes that the best forecasting modeloperating policy is touse the very basic forecasting model of simple moving average or theequivalent, firstorder exponential smoothing combined with an optimalplanned bias ordering policy, i.e. with the planned introduction ofbias.
International Journal of Operations & Production Management – Emerald Publishing
Published: May 1, 1992
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