Spare parts are known to be associated with intermittent demand patterns and such patterns cause considerable problems with regards to forecasting and stock control due to their compound nature that renders the normality assumption invalid. Compound distributions have been used to model intermittent demand patterns; there is however a lack of theoretical analysis and little relevant empirical evidence in support of these distributions. In this paper, we conduct a detailed empirical investigation on the goodness of fit of various compound Poisson distributions and we develop a distribution-based demand classification scheme the validity of which is also assessed in empirical terms. Our empirical investigation provides evidence in support of certain demand distributions and the work described in this paper should facilitate the task of selecting such distributions in a real world spare parts inventory context. An extensive discussion on parameter estimation related difficulties in this area is also provided.

Parametric approaches to stock control rely upon a lead-time demand distributional assumption and the employment of an appropriate forecasting procedure for estimating the moments of such a distribution. For the case of fast demand items the Normality assumption is typically sufficient. However, Stock Keeping Units (SKUs) often exhibit intermittent or irregular demand patterns that may not be represented by the normal distribution. This is perhaps not true when lead times are very long, in which case the Normality assumption may be plausible due to the Central Limit Theorem. This issue is further discussed later in this chapter. Intermittent demand appears at random, with some time periods having no demand at all. Moreover, demand, when it occurs, is not necessarily for a single unit or a constant demand size. In the academic literature, intermittent demand is often referred to as lumpy, sporadic or erratic demand. A conceptual framework that serves the purpose of distinguishing between such non-normal demand patterns has been discussed by Boylan et al. (2007). A demand classification framework has also been presented by Lengu and Syntetos (2009) and this is

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