A Threshold Inventory Rationing Policy for Service-Differentiated Demand Classes

Vinayak Deshpande (), Morris A. Cohen () and Karen Donohue ()
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Vinayak Deshpande: Krannert School of Management, Purdue University, West Lafayette, Indiana 47907
Morris A. Cohen: Department of Operations and Information Management, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6366
Karen Donohue: Department of Operations and Management Science, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455

Management Science, 2003, vol. 49, issue 6, 683-703

Abstract: Motivated by a study of the logistics systems used to manage consumable service parts for the U.S. military, we consider a static threshold-based rationing policy that is useful when pooling inventory across two demand classes characterized by different arrival rates and shortage (stockout and delay) costs. The scheme operates as a (Q, r) policy with the following feature. Demands from both classes are filled on a first-come-first-serve basis as long as on-hand inventory lies above a threshold levelK. Once on-hand inventory falls below this level, low-priority (i.e., low shortage cost) demand is backordered while high-priority demand continues to be filled. We analyze this static policy first under the assumption that backorders are filled according to a special threshold clearing mechanism. Structural results for the key performance measures are established to enable an efficient solution algorithm for computing stock control and rationing parameters (i.e.,Q, r, andK). Numerical results confirm that the solution under this special threshold clearing mechanism closely approximates that of the priority clearing policy. We next highlight conditions where our policy offers significant savings over traditional Üround-upÝ and Üseparate stockÝ policies encountered in the military and elsewhere. Finally, we develop a lower bound on the cost of the optimal rationing policy. Numerical results show that the performance gap between our static threshold policy and the optimal policy is small in environments typical of the military and high-technology industries.

Keywords: Inventory Rationing; Service Differentiation; Military Application (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (86)

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