ABSTRACT
Industrial manufacturing equipment and systems are becoming more complex; this complexity introduces additional interdependencies between components and systems. To cope with this, new maintenance policies like condition monitoring and prognostics are developed to predict the Remaining Useful Life (RUL) of components. However, decision making based on these predictions is still an underexplored area of maintenance management. Furthermore, maintenance relies on the availability of spare parts in order to reduce failure downtime and costs. Accurate predictions of component failure times can be used to improve both maintenance and inventory decisions. During the past decades, several joint maintenance and inventory optimization systems have been studied in literature. Compared to the separate optimization of both models, these publications reported a remarkable improvement on total cost due to joint optimization. However, the inclusion of RUL in joint maintenance and inventory models for multi-component systems has not been considered before. The objective of this paper is to quantify the added value of predictive information (RUL) in joint maintenance and inventory decision making for multi-component systems considering different levels of inter-component dependence (i.e. economic, structural and stochastic). A dynamic predictive maintenance policy is developed, which optimizes both maintenance and inventory parameters while minimizing the long-term average maintenance and inventory cost per unit time.
