A stochastic programming model for jointly optimizing maintenance and spare parts inventory for IoT applications

Service supply chain models typically use conservative maintenance and spare part management policies that result in significant losses due to redundancies. Conservatism without an improved understanding of risks, however, does not cushion against unexpected consequences. Risk scenarios associated with asset failure and inventory shortage are frequently observed in practice. Advances in Internet of Things (IoT) technology is unlocking new methods that attain significant prediction accuracy for these risk factors. IoT-enabled predictions on asset state of health can drive dynamic decision models that conduct maintenance and replenishment actions more efficiently while reducing risk. In this study, we propose a unified framework that utilizes IoT 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 introduce a new reformulation that is efficient for solving large-scale instances of the proposed model. The framework presented herein is applied to real world degradation data to demonstrate the benefits of our methodology in terms of cost and reliability.