Case study: Probabilistic estimates in the application of inventory models for perishable products in SMEs

Lorenzo Cevallos-Torres, Miguel Botto-Tobar

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    1 Citation (Scopus)

    Abstract

    The goal of this study is to create an inventory management model that will be able to estimate the control of the perishable products of a business by using probabilistic distributions. The problem arises since the stores or mini markets owners have not defined a clear concept in how to maintain an inventory in optimal conditions, especially regarding perishable products because they only have a maximum time of a week to be sold them. To solve this problem, we used specific algorithms that will help us in the handling of large amounts of data such as Monte Carlo simulation, so that we were able to use probabilistic distributions to determine the economic order quantity (EOQ) of perishable products based on weekly demand. As a result, we obtained an inventory management model, which is based on the maximum and minimum quantity of products to be ordered by the company, and also a model EOQ with an adjustment in the reorder point which it was verified a small increment in business sales by 5% during the first 11 days.

    Original languageEnglish
    Title of host publicationProblem-based learning: a didactic strategy in the teaching of system simulation
    EditorsLorenzo Cevallos-Torres, Miguel Botto-Tobar
    PublisherSpringer
    Chapter8
    Pages123-132
    Number of pages10
    ISBN (Electronic)978-3-030-13393-1
    ISBN (Print)978-3-030-13392-4
    DOIs
    Publication statusPublished - 1 Jan 2019

    Publication series

    NameStudies in Computational Intelligence
    Volume824
    ISSN (Print)1860-949X

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