A multi-item approach to repairable stocking and expediting in a fluctuating demand environment

We consider a single inventory location where multiple types of repairable spare parts are kept to service several different fleets of assets. Demand for each part is modeled by a Markov modulated Poisson process (MMPP). Each fleet has a target for the maximum expected mean number of assets down for lack of a spare part. The inventory manager can meet this target by stocking repairables and by expediting the repair of parts. Expedited repairs have a shorter lead time. There are multiple repair shops (or departments) that handle the repair of parts and the mean number of expedited repairs that can be requested per time unit is constrained per repair shop. A dual-index policy makes stocking and expediting decisions that depend on demand fluctuations for each spare part type. We formulate the above problem as a non-linear non-convex integer programming problem and provide an algorithm based on column generation to compute feasible solutions and tight lower bounds. We show how to use the MMPP to model demand fluctuations in this and other settings, including a moment fitting algorithm. We quantify the value of lead time flexibility and show that effective use of this flexibility can yield cost reductions of around 25%.