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Optimal Inventory Policies when the Demand Distribution is not Known

Erik W. Larson, Sunil Sharma () and Lars Olson ()

No 00/183, IMF Working Papers from International Monetary Fund

Abstract: This paper analyzes the stochastic inventory control problem when the demand distribution is not known. In contrast to previous Bayesian inventory models, this paper adopts a non-parametric Bayesian approach in which the firm’s prior information is characterized by a Dirichlet process prior. This provides considerable freedom in the specification of prior information about demand and it permits the accommodation of fixed order costs. As information on the demand distribution accumulates, optimal history-dependent (s,S) rules are shown to converge to an (s,S) rule that is optimal when the underlying demand distribution is known.

Keywords: Economic models; Demand; Inventory models, Non-parametric Bayesian learning, Dirichlet process, inventory, probability, statistics, equation, probabilities (search for similar items in EconPapers)
Date: 2000-11-01
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Related works:
Journal Article: Optimal Inventory Policies when the Demand Distribution Is Not Known (2001) Downloads
Working Paper: Optimal Inventory Policies when the Demand Distribution is not Known (1992)
Working Paper: Optimal Inventory Policies When The Demand Distribution Is Not Known# (1991) Downloads
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