Demand prediction using a soft-computing approach : a case study of automotive industry
Author(s)
Salais-Fierro, Tomás Eloy
Saucedo-Martínez, Jania
Vela-Haro, Jose Manuel
Date Issued
2020
Type
journal article
Volume
10
Issue
3
ISSN
2076-3417
Abstract
According to the literature review performed, there are few methods focused on the study of qualitative and quantitative variables when making demand projections by using fuzzy logic and artificial neural networks. The purpose of this research is to build a hybrid method for integrating demand forecasts generated from expert judgements and historical data and application in the automotive industry. Demand forecasts through the integration of variables; expert judgements and historical data using fuzzy logic and neural network. The methodology includes the integration of expert and historical data applying the Delphi method as a means of collecting fuzzy date. The result according to proposed methodology shows how fuzzy logic and neural networks is an alternative for demand planning activity. Machine learning techniques are techniques that generate alternatives for the tools development for demand forecasting. In this study, qualitative and quantitative variables are integrated through the implementation of fuzzy logic and time series artificial neural networks. The study aims to focus in manufacturing industry factors in conjunction time series data. © 2019 by the authors.
Sponsor(s)
Facultad de Ciencias Económicas y Empresariales - CampCM