Analysis of Perishable-Inventory Systems with Censored Demand Data

Xiangwen Lu (), Jing-Sheng Song () and Kaijie Zhu ()
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Xiangwen Lu: Cisco Systems, San Jose, California 95134
Jing-Sheng Song: Fuqua School of Business, Duke University, Durham, North Carolina 27708
Kaijie Zhu: Department of IELM, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

Operations Research, 2008, vol. 56, issue 4, 1034-1038

Abstract: We consider a multiperiod inventory system of a perishable product with unobservable lost sales. Demand distribution parameters are unknown and are updated periodically using the Bayesian approach based on the censored historical sales data. We develop an explicit expression of the first-order condition for optimality that demonstrates the key trade-off of the problem. The result generalizes partial characterizations of this trade-off in the literature. It shows that the myopic solution is a lower bound on the optimal inventory level. It also enables us to quantify the expected marginal value of information.

Keywords: stochastic inventory control; censored demand data; Bayesian models; exact analysis; sample-path approach (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (22)

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