Optimal Inventory Policies When the Demand Distribution is Not Known

20 Pages Posted: 9 Mar 2001

See all articles by Sunil Sharma

Sunil Sharma

George Washington University - Elliott School of International Affairs

C. Erik Larson

Promontory Financial Group

Lars J. Olson

University of Maryland - Department of Agricultural & Resource Economics

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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

Sharma, Sunil and Larson, C. Erik and Olson, Lars J., Optimal Inventory Policies When the Demand Distribution is Not Known. Available at SSRN: https://ssrn.com/abstract=259028 or http://dx.doi.org/10.2139/ssrn.259028

Sunil Sharma (Contact Author)

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

Institute for International Economic Policy
1957 E Street NW
Washington, DC 20052
United States

C. Erik Larson

Promontory Financial Group ( email )

1201 Pennsylvania Avenue, NW
Suite 617
Washington, DC 20004
United States
202-384-1200 (Phone)

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

Lars J. Olson

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

2200 Symons Hall
College Park, MD 20742-5535
United States
301-405-7180 (Phone)

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