A spare parts inventory control model based on Prognostics and Health monitoring data under a fill rate constraint

The performance of any inventory control model depends on the quality of future demand forecasting. The better the accuracy of future demand predictions is, the lower is the safety inventory level needed to fulfill demands and meet fill rate requirements. In most real-world applications, historical demand profiles are commonly used to predict future demands. In the specific case of spare parts inventory, one way to improve the accuracy of demand prediction is to use information obtained by monitoring the degradation level of components. This approach may be especially important when demand behavior can vary over time, for instance, due to a change in the operational conditions. Thus, this paper aims at presenting a novel spare parts inventory control model for non-repairable items with periodic review. In the proposed model, Remaining Useful Life (RUL) predictions of monitored components obtained from a Prognostics and Health Monitoring (PHM) system are used to predict future demands for spare parts. It allows the reorder point, s, and the order-up-to level, S, to be dynamically adjusted as new PHM data become available. It is assumed that PHM data are updated periodically, with period R. The proposed model minimizes the total inventory cost subject to a fill rate constraint. Numerical experiments are carried out to compare the performance of the proposed [R, s, S] model with the classical [s, S] model in terms of average total cost per period and average inventory level. The results show that the proposed model yields a reduction in both the average total cost per period and the average inventory level.