Open AccessDecision making in inventory control by using artificial neural networks
Author(s) -
Lorenzo Cevallos-Torres,
Miguel Botto-Tobar,
Angela Díaz Cadena,
Oscar León-Granizo
Publication year - 2022
Publication title -
sustainable engineering and innovation
Language(s) - English
Resource type - Journals
ISSN - 2712-0562
DOI - 10.37868/sei.v4i1.id150
Subject(s) - artificial neural network , control (management) , computer science , operations research , inventory control , work (physics) , product (mathematics) , monte carlo method , stock (firearms) , maximization , artificial intelligence , economics , engineering , statistics , mathematics , microeconomics , mechanical engineering , geometry
The purpose of this work is to increase the sales of a store devoted to the purchase and sale of soft drinks, even though the store's inventory is overstocked. This occurs as a result of the business's lack of an effective management system that controls product ordering. Additionally, there is no analysis of future sales owing to the variations that may occur because of unforeseen occurrences. The main criterion was that the proprietors of the business submit monthly records from 2017 to July 2019. To accomplish this objective completely, we used the Monte Carlo simulation method to obtain data from August to December 2019; and neural networks to obtain data for all monthly periods in the years 2020, 2021, and 2022, which enabled us to generate records of demand and stock for each of the products. Finally, it was shown that the application of neural networks enables the solution of vehicle control issues, resulting in a maximization of more than 22% of sales, thus achieving the goal and giving an optimum solution to the company.