A Comparison of Maximum Likelihood Exponential Smoothing and Bayes Forecasting Procedures in Inventory Modeling
GEORGE WASHINGTON UNIV WASHINGTON DC PROGRAM IN LOGISTICS
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The paper compares four major schemes used for forecasting mean demand to be used as input into an inventory model so that optimum stockage levels can be obtained. The inventory model is the classical order up to S , infinite horizon model with carry-over from period to period and complete backordering. Maximum likelihood, exponential smoothing, standard Bayes and adaptive Bayes schemes are used and results, via Monte Carlo simulation, are obtained for the total sum of discounted costs for stationary demand, long term trend and shock changes in mean demand.
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