N. Kourentzes, 2012, The 32nd Annual international Symposium on Forecasting, Boston.
Intermittent demand time series involve items that are requested infrequently, resulting in sporadic demand. That makes intermittent demand forecasting challenging and forecast errors can be costly in terms of unmet demand or obsolescent stock. In the literature such forecasting problems have been addressed using Croston’s method and its variants, which have a single smoothing parameter alpha. Although the literature provides suggestions on the effective range of the parameter, it does not provide guidelines how to select it. This is crucial, particularly since growing evidence in the literature points against the use of accuracy error metrics for model evaluation and hence parameter selection in intermittent demand time series. This leaves no valid methods how to best set the smoothing parameter of Croston’s method. This study proposes a novel optimisation framework that is based directly on inventory metrics instead of accuracy measures. Models optimised this way are found to outperform Croston’s models with fixed or conventionally optimised model parameters. Furthermore, this work finds that employing different parameters for smoothing the non-zero demand and the inter-demand intervals of Croston’s, instead of a single parameter as the literature suggests, provides further performance improvements.
A paper on intermittent demand optimisation and model selection, following a simpler approach, can be found here.