Intermittent demand patterns are very difficult to forecast and subsequently manage regarding stock and they are, most commonly, associated with spare parts’ requirements. The purpose of this paper is to assess the empirical “stock control” performance of intermittent demand estimation procedures. The forecasting methods considered are the simple exponential smoothing, simple moving average, Croston’s method and a new method recently developed by
the authors of this paper. We first discuss the nature of the empirical demand data set (3,000 Stock Keeping Units, Automotive Industry) and we specify the stock control model to be used for experimentation purposes. Technical simulation details are then presented to allow for replication of our results and performance measures are carefully selected to report Customer Service Level and stock volume differences. The empirical results demonstrate the superior stock control performance of the new intermittent demand forecasting method