Joint optimization of location, inventory, and condition-based replacement decisions in service parts logistics

We model, analyze and study the effects of considering condition-based replacement of parts within an integrated Service Parts Logistics (SPL) system, where geographically dispersed customers’ products are serviced with new parts from network facilities. Conventional SPL models consider replacing the parts upon failure. This is true even for the latest models in which facility locations and their part stock levels are jointly optimized. Taking advantage of the increasingly affordable, continuous, and accurate collection of part condition data (via sensors and Internet-of-Things devices), we develop a new integrated model in which optimal conditions to replace the parts are decided along with facility locations and stock levels. We capture the part degradation, replacement and failure process using a Continuous Time Markov Chain (CTMC) and embed this into the integrated location and inventory model. The resulting formulation is a mixed-integer optimization model with quadratic constraints and is solved with a state-of-the-art second-order cone programming solver. Our extensive comparison with the traditional failure-based replacement model shows that optimizing replacement conditions in this integrated framework can provide significant cost savings (network, inventory, transportation and downtime costs) leading to different facility location, allocation and inventory decisions. We also study the effects of several important parameters on the condition-based replacement model, including facility costs, shipment speeds, replacement costs, part degradation parameters, and holding costs.