An aircraft manufacturer faces the problem of allocating inventory to a set of distributed warehouses in response to random, nonstationary demands. There is particular interest in managing high value, low volume spare parts which must be available to respond to low‐frequency demands in the form of random failures of major components. The aircraft fleet is young and in expansion. In addition, high‐value parts can be repaired, implying that they reenter the system after they are removed from an aircraft and refurbished. This paper aims to present a model and a solution approach to the problem of determining the inventory levels at each warehouse.
The problem is solved using approximate dynamic programming (ADP), but this requires developing new methods for approximating value functions in the presence of low‐frequency observations.
The model and solution approach have been implemented, tested and validated internally at the manufacturer through the analysis of the inventory policy recommendations in different network scenarios and for different pools of parts. The results seem promising and compelling.
The uniqueness of this research is in the use of ADP for the modeling and solution of a distributed inventory problem. Its main value resides on the incorporation of the issue of spatial substitution in demand satisfaction within the problem of determining inventory levels in a distributed warehouse network.
