Inventory rationing for a system with heterogeneous customer classes

Inventory rationing for a system with heterogeneous customer classes

By: jacobsadm

June 5, 2013

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  • Author(s):
    Enders, P., Adan, I.J.B.F., Scheller-Wolf, A. & Houtum, G.J.J.A.N. van
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  • Appeared In: Flexible Services and Manufacturing Journal, in press
  • Volume: , 2013
  • Issue:
  • Pages:

Abstract

  • Many retailers find it useful to partition customers into multiple classes based on certain characteristics. We consider the case in which customers are primarily distinguished by whether they are willing to wait for backordered demand. A firm that faces demand from customers that are differentiated in this way may want to adopt an inventory management policy that takes advantage of this differentiation. We propose doing so by imposing a critical level (CL) policy: when inventory is at or below the critical level demand from those customers that are willing to wait is backordered, while demand from customers unwilling to wait will still be served as long as there is any inventory available. This policy reserves inventory for possible future demands from impatient customers by having other, patient, customers wait. We model a system that operates a continuous review replenishment policy, in which a base stock policy is used for replenishments. Demands as well as lead times are stochastic. We develop an exact and efficient procedure to determine the average infinite horizon performance of a given CL policy. Leveraging this procedure we develop an efficient algorithm to determine the optimal CL policy parameters. Then, in a numerical study we compare the cost of the optimal CL policy to the globally optimal state-dependent policy along with two alternative, more na├»ve, policies. The CL policy is slightly over 2 % from optimal, whereas the alternative policies are 7 and 27 % from optimal. We also study the sensitivity of our policy to the coefficient of variation of the lead time distribution, and find that the optimal CL policy is fairly insensitive, which is not the case for the globally optimal policy.