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International audience; Spare parts are often associated with intermittent demand patterns that render their forecasting a challenging task. Forecasting of spare parts demand has been researched through both parametric and non-parametric approaches. However, little has been contributed in this area from a Bayesian perspective, and most of such research is built around the Poisson demand distributional assumption. However, the Poisson distribution is known to have certain limitations and, further, empirical evidence on the inventory performance of Bayesian methods is lacking. In this paper, we propose a new Bayesian method based on compound Poisson distributions. The proposed method is compared to the Poisson-based Bayesian method with a Gamma prior distribution as well as to a parametric frequentist method and to a non-parametric one. A numerical investigation (on 7400 theoretically generated series) is complemented by an empirical assessment on demand data from about 3000 stock keeping units in the automotive sector to analyse the performance of the four forecasting methods. We find that both Bayesian methods outperform the other methods with a higher inventory efficiency reported for the Poisson Bayesian method with a Gamma prior. This outperformance increases for higher demand variability. From a practical perspective, the outperformance of the proposed method is associated with some added complexity. We also find that the performance of the non-parametric method improves for longer lead-times and higher demand variability when compared to the parametric one.

Although intermittent demand items dominate service and repair parts inventories in many industries, research in forecasting such items has been limited. A critical research question is whether one should make point forecasts of the mean and variance of intermittent demand with a simple parametric method such as simple exponential smoothing or else employ some form of bootstrapping to simulate an entire distribution of demand during lead time. The aim of this work is to answer that question by evaluating the effects of forecasting on stock control performance in more than 7,000 demand series. Tradeoffs between inventory investment and customer service show that simple parametric methods perform well, and it is questionable whether bootstrapping is worth the added complexity.

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.