Abstract
Intermittent demand appears at random, with many time periods having no demand. Manufacturers perceive the forecasting of intermittent data to be an important problem. In practice, the standard method of forecasting intermittent demand is single exponential smoothing, although some production management texts suggest the lesser-known alternative of Croston's method [Croston J.D., 1972, Forecasting and stock control for intermittent demands, Operational Research Quarterly, 23(3), 289-303]. We compared the two methods, using artificial data created to violate Croston's assumptions and real-world data from industrial sources. We conclude that Croston's method is robustly superior to exponential smoothing and could provide tangible benefits to manufacturers forecasting intermittent demand.
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