Inventory Policy with Parametric Demand: Operational Statistics, Linear Correction, and Regression

In this paper, we consider data‐driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known u...

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Veröffentlicht in:Production and operations management 2012-03, Vol.21 (2), p.291-308
Hauptverfasser: Ramamurthy, Vivek, George Shanthikumar, J., Shen, Zuo-Jun Max
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creator Ramamurthy, Vivek
George Shanthikumar, J.
Shen, Zuo-Jun Max
description In this paper, we consider data‐driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known up to the location and scale parameters. For the case of the unknown shape parameter, we first suggest a heuristic approach based on operational statistics to obtain improved ordering policies and illustrate the same for the case of a Pareto demand distribution. In more general cases where the heuristic is not applicable, we suggest linear correction and support vector regression approaches to better estimate ordering policies, and illustrate these using a Gamma demand distribution. In certain cases, our proposed approaches are found to yield significant improvements.
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source Wiley Journals; Business Source Complete; SAGE Complete A-Z List
subjects demand ambiguity
Estimates
Expected values
Inventory
Inventory control
Inventory management
Linear programming
model uncertainty
newsvendor model
operational statistics
Order quantity
Regression analysis
Standard deviation
Studies
title Inventory Policy with Parametric Demand: Operational Statistics, Linear Correction, and Regression
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