The Accuracy of Non‐traditional versus Traditional Methods of Forecasting Lumpy Demand

Somnath Mukhopadhyay · Adriano O. Solis · Rafael S. Gutierrez

50 citations0/0 files storedJournal of Forecasting

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Forecasting Techniques and Applications Supply Chain and Inventory Management Innovation Diffusion and Forecasting

Abstract

ABSTRACT Forecasting for inventory items with lumpy demand is difficult because of infrequent nonzero demands with high variability. This article developed two methods to forecast lumpy demand: an optimally weighted moving average method and an intelligent pattern‐seeking method. We compare them with a number of well‐referenced methods typically applied over the last 30 years in forecasting intermittent or lumpy demand. The comparison is conducted over about 200,000 forecasts (using 1‐day‐ahead and 5‐day‐ahead review periods) for 24 series of actual product demands across four different error measures. One of the most important findings of our study is that the two non‐traditional methods perform better overall than the traditional methods. We summarize results and discuss managerial implications. Copyright © 2011 John Wiley & Sons, Ltd.

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1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition 1992Error measures for generalizing about forecasting methods: Empirical comparisons 1972Forecasting and Stock Control for Intermittent Demands 2004The accuracy of intermittent demand estimates 1996Neural Network Models for Time Series Forecasts 2004On the categorization of demand patterns 2001On the bias of intermittent demand estimates 1992The evaluation of extrapolative forecasting methods

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