Spare parts demand forecasting: a review on bootstrapping methods

Accurate demand forecasts are essential to the inventory control of spare parts. There is a plethora of statistical methods developed in the academic literature to deal with the forecasting of spare parts demand. These methods belong to the parametric and the non-parametric approaches. Within the second approach, the bootstrapping methods are the most considered ones. Despite that bootstrapping methods have shown a good empirical performance in comparison with their parametric counterparts, none of the available studies highlight the necessity to bring together its related state of knowledge and critically review the relevant research advancements. The present paper bridges this gap by reviewing the literature that deals with the bootstrapping approach and by discussing some of its statistical properties. This yields a better understanding of its framework, and hence, retrieves more robust explanations of the observed mixed-performances of the available bootstrap-based forecasting methods. This paper reviews as well the service level models associated with the bootstrapping approach with an emphasis on the fill rate models.