dc.contributor.author | amelec, viloria | spa |
dc.contributor.author | Arrieta Matos, Fernanda | spa |
dc.contributor.author | Gaitán, Mercedes | spa |
dc.contributor.author | Hernández Palma, Hugo | spa |
dc.contributor.author | Flórez Guzman, Yasmin | spa |
dc.contributor.author | CABAS VASQUEZ, LUIS CARLOS | spa |
dc.contributor.author | Vargas Mercado, Carlos | spa |
dc.contributor.author | Pineda Lezama, Omar Bonerge | spa |
dc.date.accessioned | 2020-01-17T19:42:45Z | |
dc.date.available | 2020-01-17T19:42:45Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | http://hdl.handle.net/11323/5869 | spa |
dc.description.abstract | Based on a forecast, the decision maker can determine the capacity required to meet a certain forecast demand, as well as carry out in advance the balance of capacities in order to avoid underusing or bottlenecks. This article proposes a procedure for forecasting demand through Artificial Neural Networks. In order to carry out the validation, the procedure proposed was applied in a Soda Trading and Distribution Company where three types of products were selected | spa |
dc.description.abstract | En función de un pronóstico, el responsable de la toma de decisiones puede determinar la capacidad requerida para satisfacer una determinada demanda de pronóstico, así como llevar a cabo de antemano el equilibrio de capacidades para evitar subutilizaciones o cuellos de botella. Este artículo propone un procedimiento para pronosticar la demanda a través de redes neuronales artificiales. Para llevar a cabo la validación, el procedimiento propuesto se aplicó en una empresa de distribución y comercialización de refrescos donde se seleccionaron tres tipos de productos. | spa |
dc.language.iso | eng | |
dc.publisher | Universidad de la Costa | spa |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | spa |
dc.subject | Forecast | spa |
dc.subject | Artificial Neural Networks | spa |
dc.subject | Big Data | spa |
dc.subject | Demand | spa |
dc.subject | Pronóstico | spa |
dc.subject | Redes neuronales artificiales | spa |
dc.subject | Big Data | spa |
dc.subject | Demanda | spa |
dc.title | Demand forecasting method using artificial neural networks | spa |
dc.type | Pre-Publicación | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.identifier.instname | Corporación Universidad de la Costa | spa |
dc.identifier.reponame | REDICUC - Repositorio CUC | spa |
dc.identifier.repourl | https://repositorio.cuc.edu.co/ | spa |
dc.title.translated | Método de pronóstico de la demanda utilizando redes neuronales artificiales | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_816b | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/preprint | spa |
dc.type.hasversion | info:eu-repo/semantics/draft | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ARTOTR | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | spa |