A significant part of the existing literature in spare parts logistics focuses on evaluating optimum reorder points and reorder quantities. However, spare parts inventory is unique because it require a tight coupling with maintenance management and reliability. Joint maintenance and spare parts inventory for stochastically failing components was first introduced in 1968 by C. H. Falkner. Since then, this has been an active research area with most of the focus being directed towards joint optimization of inventory and preventive or age-replacement maintenance policies. The industrial internet of things (IIoT) has given rise to new opportunities that warrant revisiting this topic with a more modern perspective that leverages the integration of predictive analytics.
In this study, we propose a unified framework that utilizes equipment condition monitoring data to jointly optimize condition-based maintenance and inventory decisions. We formulate our problem as a stochastic mixed integer program that accounts for the interplay between maintenance, spare parts inventory, and asset reliability. We present extensive computational experiment the demonstrate the benefits of our proposed approach in terms of cost and reliability.