An inventory control policy for maintenance networks

Many industries rely on maintenance networks to maintain their key assets, and a key characteristic of such maintenance services is the wide use of rotable parts. In view of today´s time-based competition, efficiently managing the rotable inventory becomes imperative for a maintenance network to achieve short turn-around-times and low costs. This, however, is difficult in view of complicated and uncertain maintenance processes, and hybrid inventory replenishments from both new and refurbished parts. As the information on demands and replenishments is highly dependent on maintenance processes, and can be obtained, utilizing this information opens a new way to improve the inventory operational efficiency. This paper presents a new approach for rotable inventory control with the demand and replenishment information. Key characteristics of maintenance processes and hybrid replenishments are abstracted to form a novel model within the context of stochastic optimal control, where the demand and replenishment information is incorporated in the system state. Comparing to traditional approaches with a large-size augmented state, an aggregated state variable is defined based on inventory dynamics to reduce the number of required state components. A solution methodology based on stochastic dynamic programming (SDP) is developed, with stage-wise costs obtained in terms of aggregated state variables. Steady state solutions are computed offline, and are stored as inventory policies to be implemented by table lookup. Simulation results demonstrate the effectiveness of the approach on reducing inventory costs with the demand and replenishment information.