Demand forecasting method using artificial neural networks
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 Net...
- Autores:
-
amelec, viloria
Arrieta Matos, Fernanda
Gaitán, Mercedes
Hernández Palma, Hugo
Flórez Guzman, Yasmin
CABAS VASQUEZ, LUIS CARLOS
Vargas Mercado, Carlos
Pineda Lezama, Omar Bonerge
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/5869
- Acceso en línea:
- http://hdl.handle.net/11323/5869
https://repositorio.cuc.edu.co/
- Palabra clave:
- Forecast
Artificial Neural Networks
Big Data
Demand
Pronóstico
Redes neuronales artificiales
Big Data
Demanda
- Rights
- openAccess
- License
- http://creativecommons.org/publicdomain/zero/1.0/
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| dc.title.spa.fl_str_mv |
Demand forecasting method using artificial neural networks |
| dc.title.translated.spa.fl_str_mv |
Método de pronóstico de la demanda utilizando redes neuronales artificiales |
| title |
Demand forecasting method using artificial neural networks |
| spellingShingle |
Demand forecasting method using artificial neural networks Forecast Artificial Neural Networks Big Data Demand Pronóstico Redes neuronales artificiales Big Data Demanda |
| title_short |
Demand forecasting method using artificial neural networks |
| title_full |
Demand forecasting method using artificial neural networks |
| title_fullStr |
Demand forecasting method using artificial neural networks |
| title_full_unstemmed |
Demand forecasting method using artificial neural networks |
| title_sort |
Demand forecasting method using artificial neural networks |
| dc.creator.fl_str_mv |
amelec, viloria Arrieta Matos, Fernanda Gaitán, Mercedes Hernández Palma, Hugo Flórez Guzman, Yasmin CABAS VASQUEZ, LUIS CARLOS Vargas Mercado, Carlos Pineda Lezama, Omar Bonerge |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Arrieta Matos, Fernanda Gaitán, Mercedes Hernández Palma, Hugo Flórez Guzman, Yasmin CABAS VASQUEZ, LUIS CARLOS Vargas Mercado, Carlos Pineda Lezama, Omar Bonerge |
| dc.subject.spa.fl_str_mv |
Forecast Artificial Neural Networks Big Data Demand Pronóstico Redes neuronales artificiales Big Data Demanda |
| topic |
Forecast Artificial Neural Networks Big Data Demand Pronóstico Redes neuronales artificiales Big Data Demanda |
| description |
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 |
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2019 |
| dc.date.issued.none.fl_str_mv |
2019 |
| dc.date.accessioned.none.fl_str_mv |
2020-01-17T19:42:45Z |
| dc.date.available.none.fl_str_mv |
2020-01-17T19:42:45Z |
| dc.type.spa.fl_str_mv |
Pre-Publicación |
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http://purl.org/coar/resource_type/c_816b |
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http://hdl.handle.net/11323/5869 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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http://hdl.handle.net/11323/5869 https://repositorio.cuc.edu.co/ |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
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http://creativecommons.org/publicdomain/zero/1.0/ |
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openAccess |
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Universidad de la Costa |
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Corporación Universidad de la Costa |
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amelec, viloriaArrieta Matos, FernandaGaitán, MercedesHernández Palma, HugoFlórez Guzman, YasminCABAS VASQUEZ, LUIS CARLOSVargas Mercado, CarlosPineda Lezama, Omar Bonerge2020-01-17T19:42:45Z2020-01-17T19:42:45Z2019http://hdl.handle.net/11323/5869Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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 selectedEn 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.Amelec, Viloria-will be generated-orcid-0000-0003-2673-6350-600Arrieta Matos, FernandaGaitán, MercedesHernández Palma, HugoFlórez Guzman, Yasmin-will be generated-orcid-0000-0002-1114-8356-600Cabas Vásquez, Luis Carlos-will be generated-orcid-0000-0003-0524-7945-600Vargas Mercado, Carlos-will be generated-orcid-0000-0002-5436-0568-600Pineda Lezama, Omar BonergeengUniversidad de la Costahttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2ForecastArtificial Neural NetworksBig DataDemandPronósticoRedes neuronales artificialesBig DataDemandaDemand forecasting method using artificial neural networksMétodo de pronóstico de la demanda utilizando redes neuronales artificialesPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/5c4b8016-61e4-4b8f-b61e-fd63000159f0/download8a4605be74aa9ea9d79846c1fba20a33MD55CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/6405069f-19d9-4c3c-9ac9-ff82e3a9face/download42fd4ad1e89814f5e4a476b409eb708cMD52ORIGINALDEMAND FORECASTING METHOD USING ARTIFICIAL NEURAL NETWORKS.pdfDEMAND FORECASTING METHOD USING ARTIFICIAL NEURAL NETWORKS.pdfapplication/pdf5927https://repositorio.cuc.edu.co/bitstreams/eb0beba6-8184-4977-84c8-f40f6e169e04/downloada13d6cca99a7d40a4fcca1592b036bf9MD54THUMBNAILDEMAND FORECASTING METHOD USING ARTIFICIAL NEURAL NETWORKS.pdf.jpgDEMAND FORECASTING METHOD USING ARTIFICIAL NEURAL NETWORKS.pdf.jpgimage/jpeg32923https://repositorio.cuc.edu.co/bitstreams/dc107021-d4d6-416f-a84a-ac730ede9a94/download2def158c0be6a345d2760f2d32ce6adbMD57TEXTDEMAND FORECASTING METHOD USING ARTIFICIAL NEURAL NETWORKS.pdf.txtDEMAND FORECASTING METHOD USING ARTIFICIAL NEURAL NETWORKS.pdf.txttext/plain804https://repositorio.cuc.edu.co/bitstreams/3304f297-825c-46f3-aebb-387224afabd1/download719d7248c2f281be51a62cd314b61fe4MD5811323/5869oai:repositorio.cuc.edu.co:11323/58692024-09-16 16:40:38.175http://creativecommons.org/publicdomain/zero/1.0/open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |