Optimal Inventory Policies When the Demand Distribution is Not Known

24 Pages Posted: 1 Feb 2006

See all articles by C. Erik Larson

C. Erik Larson

Promontory Financial Group

Lars J. Olson

University of Maryland - Department of Agricultural & Resource Economics

Sunil Sharma

George Washington University - Elliott School of International Affairs

Multiple version iconThere are 3 versions of this paper

Date Written: November 2000

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: Inventory models, Non-parametric Bayesian learning, Dirichlet process

JEL Classification: C6, D8, L2

Suggested Citation

Larson, C. Erik and Olson, Lars J. and Sharma, Sunil, Optimal Inventory Policies When the Demand Distribution is Not Known (November 2000). IMF Working Paper No. 00/183, Available at SSRN: https://ssrn.com/abstract=880264

C. Erik Larson (Contact Author)

Promontory Financial Group ( email )

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HOME PAGE: http://www.ceriklarson.com

Lars J. Olson

University of Maryland - Department of Agricultural & Resource Economics ( email )

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301-405-7180 (Phone)

Sunil Sharma

George Washington University - Elliott School of International Affairs ( email )

Institute for International Economic Policy
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Washington, DC 20052
United States

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