The Accuracy of Non-traditional versus Traditional Methods of Forecasting Lumpy Demand
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.