Spare parts inventory management: New evidence from distribution fitting

Spare parts are necessary for ensuring the functioning of the critical equipment of many companies, and as such, they play a central role in these companies' operations. Inventory control of spare parts is particularly challenging due to the nature of their demand, which is usually slow-moving, erratic and lumpy. As inventory policies rely on the forecasted lead-time demand distribution and this choice impacts the performance of the system, an ill-suited hypothesized distribution may result in high preventable costs. In this study, we contribute to the empirical literature by analyzing what distributions best fit spare parts demand. We use the Kolmogorov Smirnov (K-S) goodness-of-fit test to find the best-fitting distributions to our data and compare our results to those in the literature. Furthermore, we implement a slightly modified K-S test that places greater emphasis on differences in the right tail of the distribution, mirroring real-world inventory applications, and less emphasis on the left tail. Finally, we link the goodness-of-fit of the distributions to their inventory performance. Our first dataset comes from the German renewable energy industry and is composed of the weekly demand for more than 4000 items over the period 2011-2013. The second dataset comes from the Royal Air Force. It is composed of monthly demand for 5000 items over the period 1996-2002. (C) 2017 Published by Elsevier B.V.