Accuracy of Intermittent Demand Forecasting Systems in the Enterprise

Purpose: The main purpose of this article is to find the best forecasting method for intermittent demand times series, from the company’s point of view. Design/Methodology/Approach: Intermittent demand forecasting systems were constructed based on the Croston’s, SBA, TSB, SES and MA methods. A real database from the warehouse center, containing over sixteen thousand items, was used. Accuracy measures were also discussed. Forecasting methods were compared for all products and for separate demand categories (intermittent, lumpy, erratic, smooth). Findings: It was determined that the TSB method outperforms other methods for all products. The worst procedures were found to be Croston’s and SBA, which performed even worse than SES or MA. The same conclusions were true for intermittent and lumpy categories. In case of erratic and smooth items different results were obtained. It was determined that the SBA method performed best, while the TSB method yielded the poorest results. Practical Implications: The main conclusion is that to judge accuracy of forecasting systems first the proper forecast error measures should be chosen. Based on obtained results, TSB method seems to be the best for intermittent demand times series and this method is recommended for enterprises dealing with intermittent demand. Originality/value: Since such error measures as MASE or scaled MAE favored an underestimated (or even zero) forecast, in the article a new error metric is proposed, which was named scaled Compound Error (sCE). It is a scaled error, and it considers forecast biasedness.