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

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  • Víctor Leiva
  • Manoel Santos‐Neto
  • Francisco José A. Cysneiros
  • Michelli Barros

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. Copyright © 2015 John Wiley & Sons, Ltd.

Suggested Citation

  • Víctor Leiva & Manoel Santos‐Neto & Francisco José A. Cysneiros & Michelli Barros, 2016. "A methodology for stochastic inventory models based on a zero‐adjusted Birnbaum‐Saunders distribution," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 32(1), pages 74-89, January.
    • Handle: RePEc:wly:apsmbi:v:32:y:2016:i:1:p:74-89
    DOI: 10.1002/asmb.2124
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    Cited by:

    1. Tomazella, Vera & Pereira, Gustavo H.A. & Nobre, Juvêncio S. & Santos-Neto, Manoel, 2019. "Zero-adjusted reparameterized Birnbaum–Saunders regression model," Statistics & Probability Letters, Elsevier, vol. 149(C), pages 142-145.
    2. Luis Sánchez & Víctor Leiva & Manuel Galea & Helton Saulo, 2020. "Birnbaum-Saunders Quantile Regression Models with Application to Spatial Data," Mathematics, MDPI, vol. 8(6), pages 1-17, June.

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