Evaluation of forecasting error measurements and techniques for intermittent demand

This paper is a part and contribution to develop flexible and robust supply chain forecasting systems under changing market demands. It suggests new tools and models to evaluate forecasting error measurements. The paper especially studies slow moving or intermittent demand, when for items the forecasting time periods often have zero demand. For the difficult to forecast intermittent demand the Croston forecasting technique is mostly regarded as a better choice than single exponential smoothing. These two methods, Croston and single exponential smoothing, together with two modifications of the Croston method, are discussed and evaluated with real intermittent data. The apprehended performance of a forecasting technique is dependent of the chosen measurement of forecast errors. The main purpose is to examine and evaluate different forecasting error measurements. Traditional measurements of forecast errors are studied, mean absolute deviation (MAD), mean square error (MSE), etc. together with new suggested error and bias measurements: "periods in stock" (PIS) and "number of shortages" (NOS). PIS considers the time aspect of the forecast error, NOS considers the development of cumulated forecast error (CFE). A complementary measure for descriptive statistics of time series is also suggested, mean average change. The conclusion, through principal components analysis (PCA), is that a single measure of forecast errors does not present the total different dimensions of the errors; complementary error measures must be used.