Improved Inventory Targets in the Presence of Limited Historical Demand Data

Alp Akcay (), Bahar Biller () and Sridhar Tayur ()
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Alp Akcay: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Bahar Biller: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Sridhar Tayur: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Manufacturing & Service Operations Management, 2011, vol. 13, issue 3, 297-309

Abstract: Most of the literature on inventory management assumes that the demand distribution and the values of its parameters are known with certainty. In this paper, we consider a repeated newsvendor setting where this is not the case and study the problem of setting inventory targets when there is a limited amount of historical demand data. Consequently, we achieve the following objectives: (1) to quantify the inaccuracy in the inventory-target estimation as a function of the length of the historical demand data, the critical fractile, and the shape parameters of the demand distribution; and (2) to determine the inventory target that minimizes the expected cost and accounts for the uncertainty around the demand parameters estimated from limited historical data. We achieve these objectives by using the concept of expected total operating cost and representing the demand distribution with the highly flexible Johnson translation system. Our procedures require no restrictive assumptions about the first four moments of the demand random variables, and they can be easily implemented in practical settings with reduced expected total operating costs.

Keywords: expected total operating cost; inventory management; Johnson translation system; maximum likelihood policy; statistical estimation (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:13:y:2011:i:3:p:297-309

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