Inventory Sharing and Rationing in Decentralized Dealer Networks

The management of a service parts supply chain is especially challenging in environments characterized by low-demand rates, expensive parts and high service requirements. As a result, an increasing number of manufacturers have begun pursuing a strategy that promotes inventory sharing among the dealers in their distribution network. Hence, there is a need for decision models that enable the analysis of the dealer inventory sharing problem, particularly in decentralized dealer networks, i.e., networks in which the dealers are independent of the manufacturer. In this paper we develop a framework for modeling and analyzing inventory sharing problems in decentralized dealer networks. We first analyze an individual dealer problem under inventory sharing in a make-to-stock setting. We use dynamic programming to characterize the structure of the optimal policy for an individual dealer. A queueing approach is then used to characterize the performance measures for this optimal inventory replenishment and sharing policy. We then consider a decentralized, multi-dealer setting to study the impact of inventory sharing among independent dealers. A game theoretic approach is used to characterize the equilibrium behavior of the system.