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A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

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A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

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dc.contributor.author Lorente-Leyva, Leandro L. es_ES
dc.contributor.author Alemany Díaz, María Del Mar es_ES
dc.contributor.author Peluffo-Ordóñez, Diego H. es_ES
dc.contributor.author Herrera-Granda, Israel D. es_ES
dc.date.accessioned 2021-09-03T03:34:19Z
dc.date.available 2021-09-03T03:34:19Z
dc.date.issued 2021-01-07 es_ES
dc.identifier.issn 0302-9743 es_ES
dc.identifier.uri http://hdl.handle.net/10251/171329
dc.description.abstract [EN] This document presents a comparison of demand forecasting methods, with the aim of improving demand forecasting and with it, the production planning system of Ecuadorian textile industry. These industries present problems in providing a reliable estimate of future demand due to recent changes in the Ecuadorian context. The impact on demand for textile products has been observed in variables such as sales prices and manufacturing costs, manufacturing gross domestic product and the unemployment rate. Being indicators that determine to a great extent, the quality and accuracy of the forecast, generating also, uncertainty scenarios. For this reason, the aim of this work is focused on the demand forecasting for textile products by comparing a set of classic methods such as ARIMA, STL Decomposition, Holt-Winters and machine learning, Artificial Neural Networks, Bayesian Networks, Random Forest, Support Vector Machine, taking into consideration all the above mentioned, as an essential input for the production planning and sales of the textile industries. And as a support, when developing strategies for demand management and medium-term decision making of this sector under study. Finally, the effectiveness of the methods is demonstrated by comparing them with different indicators that evaluate the forecast error, with the Multi-layer Neural Networks having the best results with the least error and the best performance. es_ES
dc.description.sponsorship The authors are greatly grateful by the support given by the SDAS Research Group (https://sdas-group.com/). es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Lecture Notes in Computer Science es_ES
dc.relation.ispartof LOD 2020: Machine Learning, Optimization, and Data Science es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Demand forecasting methods es_ES
dc.subject Textile industry es_ES
dc.subject Machine learning es_ES
dc.subject Classical methods es_ES
dc.subject Forecast error es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry es_ES
dc.type Artículo es_ES
dc.type Comunicación en congreso es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-030-64580-9_11 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Lorente-Leyva, LL.; Alemany Díaz, MDM.; Peluffo-Ordóñez, DH.; Herrera-Granda, ID. (2021). A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry. Lecture Notes in Computer Science. 131-142. https://doi.org/10.1007/978-3-030-64580-9_11 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 6th International Conference on Machine Learning, Optimization, and Data Science (LOD 2020) es_ES
dc.relation.conferencedate Julio 19-23,2020 es_ES
dc.relation.conferenceplace Siena, Italy es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-64580-9_11 es_ES
dc.description.upvformatpinicio 131 es_ES
dc.description.upvformatpfin 142 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\428997 es_ES
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