A methodology for stochastic inventory models based on a zero-adjusted Birnbaum-Saunders distribution

Leiva Victor; Santos-Neto Manoel; Cysneiros Francisco Jose A.Barros Michelli

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A methodology for stochastic inventory models based on a zero-adjusted Birnbaum-Saunders distribution

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The Birnbaum-Saunders (BS) distribution is receiving considerable attention. We propose a methodology for inventory logistics that allows demand data with zeros to be modeled by means of a new discrete-continuous mixture distribution, which is constructed by using a probability mass at zero and a continuous component related to the BS distribution. We obtain some properties of the new mixture distribution and conduct a simulation study to evaluate the performance of the estimators of its parameters. The methodology for stochastic inventory models considers also financial indicators. We illustrate the proposed methodology with two real-world demand data sets. It shows its potential, highlighting the convenience of using it by improving the contribution margins of a Chilean food industry. Copyright (c) 2015 John Wiley Sons, Ltd.

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来源
《Applied stochastic models in business and industry》 |2016年第1期|74-89|共16页
作者
Leiva Victor; Santos-Neto Manoel; Cysneiros Francisco Jose A.Barros Michelli;
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作者单位

Univ Valparaiso, Inst Stat, Valparaiso, Chile;

Univ Fed Campina Grande, Dept Stat, Campina Grande, Brazil;

Univ Fed Pernambuco, Dept Stat, Recife, PE, Brazil;

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原文格式 PDF
正文语种英语
中图分类应用数学;
关键词
demand data; financial indicators; maximum likelihood method; mixture distributions; Monte Carlo simulation; R software;

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A methodology for stochastic inventory models based on a zero-adjusted Birnbaum-Saunders distribution

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