A Censored-Data Multiperiod Inventory Problem with Newsvendor Demand Distributions

Arnab Bisi (), Maqbool Dada () and Surya Tokdar ()
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Arnab Bisi: Krannert School of Management, Purdue University, West Lafayette, Indiana 47907
Maqbool Dada: Johns Hopkins Carey Business School, Baltimore, Maryland 21202
Surya Tokdar: Department of Statistical Science, Duke University, Durham, North Carolina 27708

Manufacturing & Service Operations Management, 2011, vol. 13, issue 4, 525-533

Abstract: We study the stochastic multiperiod inventory problem in which demand in excess of available inventory is lost and unobserved so that demand data are censored. A Bayesian scheme is employed to dynamically update the demand distribution for the problem with storable or perishable inventory and with exogenous or endogenous price. We show that the Weibull is the only newsvendor distribution for which the optimal solution can be expressed in scalable form. Moreover, for Weibull demand the cost function is not convex in general. Nevertheless, in all but the storable case, sufficient structure can be discerned so that the optimal solution can be easily computed. Specifically, for the perishable inventory case, the optimal policy can be found by solving simple recursions, whereas the perishable case with pricing requires solutions to more complex one-step look-ahead recursions. Interestingly, for the special case of exponential demand the cost function is convex, so that for the storable inventory case, the optimal policy can be found using simple one-step look-ahead recursions whereas for the perishable case the optimal policy can be expressed by exact closed-form formulas.

Keywords: inventory; stochastic demand; lost sales; scalability; optimal policy (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (13)

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