Abstract
The aim of the article was to analyze the impact of outliers on the accuracy of intermittent demand forecasting. The hypothesis was verified that eliminating the influence of outliers leads to more accurate forecasts. A data set containing monthly sales time series of over 12 000 products was analyzed. Three outliers rejection strategies were analyzed (replacing outliers, replacing extreme values, isolation forest). Intermittent demand forecasts were determined using the Croston, SBA, TSB and SES methods, with optimization of smoothing constants and initial values. The accuracy of the forecasts was assessed from the point of view of the inventories value and the value of lost sales (lost demand). The conducted research shows that eliminating the influence of outliers significantly reduces the inventory level value, with only a slight increase in the value of lost sales. Eliminating the influence of outliers therefore provides an opportunity to improve the financial results of enterprises selling slow – moving products through a significant reduction in inventory levels.
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