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

Víctor Leiva, Manoel Santos-Neto, Francisco José A. Cysneiros, Michelli Barros

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)74-89
Number of pages16
JournalApplied Stochastic Models in Business and Industry
Volume32
Issue number1
DOIs
StatePublished - 1 Jan 2016
Externally publishedYes

Keywords

  • Monte Carlo simulation
  • R software
  • demand data
  • financial indicators
  • maximum likelihood method
  • mixture distributions

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