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This study aims to present an alternative technique of exponential smoothing to estimate the demand for items with intermittent and seasonal demand. The usual technique would aggregate demand periods (months in quarters, for instance) to calculate a seasonality factor for the set of periods. The estimate for the set would be divided by the number of periods comprising it to calculate the demand per period. This technique recalculates the basis and seasonality factor for each set, and provides equal estimates for all periods within the set. The alternative herein presented also recalculates seasonal factors for every set of periods, but recalculates the demand basis for each period, allowing better monitoring of demand behavior. Based on a real-life case, the results obtained by the two techniques mentioned above and by others that do not explicitly consider seasonality were compared: simple moving average, no-seasonality exponential smoothing, Croston’s, and Syntetos-Boylan. The latter two were developed specifically for intermittent demands without seasonality. The techniques that consider seasonality performed better for estimation errors. The suggested technique, in the example, showed less bias, although with somewhat lower accuracy than exponential smoothing with seasonality and period aggregation.

Exponential smoothing for intermittent demand with demand basis updated more frequently than seasonality factors