Predictive model based on machine learning for raw material purchasing management in the retail sector.

Descripción del Articulo

Making raw material purchase forecasts for companies is very difficult and, if inadequately controlled, can affect the company's decision making and profitability. Currently, there are optimized systems or mathematical models to try to predict the demands and solve this problem. In this study,...

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Detalles Bibliográficos
Autores: Antunez, Julio C., Salazar, Johnny D., Castañeda, Pedro S.
Formato: artículo
Fecha de Publicación:2024
Institución:Universidad Peruana de Ciencias Aplicadas
Repositorio:UPC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorioacademico.upc.edu.pe:10757/675912
Enlace del recurso:http://hdl.handle.net/10757/675912
Nivel de acceso:acceso embargado
Materia:Inventory management
Model interpretation
SMEs
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dc.title.es_PE.fl_str_mv Predictive model based on machine learning for raw material purchasing management in the retail sector.
title Predictive model based on machine learning for raw material purchasing management in the retail sector.
spellingShingle Predictive model based on machine learning for raw material purchasing management in the retail sector.
Antunez, Julio C.
Inventory management
Model interpretation
SMEs
title_short Predictive model based on machine learning for raw material purchasing management in the retail sector.
title_full Predictive model based on machine learning for raw material purchasing management in the retail sector.
title_fullStr Predictive model based on machine learning for raw material purchasing management in the retail sector.
title_full_unstemmed Predictive model based on machine learning for raw material purchasing management in the retail sector.
title_sort Predictive model based on machine learning for raw material purchasing management in the retail sector.
author Antunez, Julio C.
author_facet Antunez, Julio C.
Salazar, Johnny D.
Castañeda, Pedro S.
author_role author
author2 Salazar, Johnny D.
Castañeda, Pedro S.
author2_role author
author
dc.contributor.author.fl_str_mv Antunez, Julio C.
Salazar, Johnny D.
Castañeda, Pedro S.
dc.subject.es_PE.fl_str_mv Inventory management
Model interpretation
SMEs
topic Inventory management
Model interpretation
SMEs
description Making raw material purchase forecasts for companies is very difficult and, if inadequately controlled, can affect the company's decision making and profitability. Currently, there are optimized systems or mathematical models to try to predict the demands and solve this problem. In this study, a raw material purchase prediction model is proposed that uses the Elastic Net algorithm to analyze historical sales and inventory data. The model is used to improve prediction accuracy, allowing SMEs to optimize inventories, reduce costs and improve efficiency. Experimental results indicate that the proposed model obtains better results in the MAE, RMSE and R2 indicators.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-09-29T18:38:03Z
dc.date.available.none.fl_str_mv 2024-09-29T18:38:03Z
dc.date.issued.fl_str_mv 2024-06-28
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.doi.none.fl_str_mv 10.1145/3677454.3677456
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10757/675912
dc.identifier.journal.es_PE.fl_str_mv ACM International Conference Proceeding Series
dc.identifier.eid.none.fl_str_mv 2-s2.0-85202845935
dc.identifier.scopusid.none.fl_str_mv SCOPUS_ID:85202845935
dc.identifier.isni.none.fl_str_mv 0000 0001 2196 144X
identifier_str_mv 10.1145/3677454.3677456
ACM International Conference Proceeding Series
2-s2.0-85202845935
SCOPUS_ID:85202845935
0000 0001 2196 144X
url http://hdl.handle.net/10757/675912
dc.language.iso.es_PE.fl_str_mv eng
language eng
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dc.publisher.es_PE.fl_str_mv Association for Computing Machinery
dc.source.es_PE.fl_str_mv Repositorio Academico - UPC
Universidad Peruana de Ciencias Aplicadas (UPC)
dc.source.none.fl_str_mv reponame:UPC-Institucional
instname:Universidad Peruana de Ciencias Aplicadas
instacron:UPC
instname_str Universidad Peruana de Ciencias Aplicadas
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institution UPC
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dc.source.journaltitle.none.fl_str_mv ACM International Conference Proceeding Series
dc.source.beginpage.none.fl_str_mv 6
dc.source.endpage.none.fl_str_mv 11
bitstream.url.fl_str_mv https://repositorioacademico.upc.edu.pe/bitstream/10757/675912/1/license.txt
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spelling 70812d48147d03cf013db6a81ef990a53008f23439d87cd8b0aa0f28f19d6b440d5300b7cfe215892bab35c654f4515f1faa16300Antunez, Julio C.Salazar, Johnny D.Castañeda, Pedro S.2024-09-29T18:38:03Z2024-09-29T18:38:03Z2024-06-2810.1145/3677454.3677456http://hdl.handle.net/10757/675912ACM International Conference Proceeding Series2-s2.0-85202845935SCOPUS_ID:852028459350000 0001 2196 144XMaking raw material purchase forecasts for companies is very difficult and, if inadequately controlled, can affect the company's decision making and profitability. Currently, there are optimized systems or mathematical models to try to predict the demands and solve this problem. In this study, a raw material purchase prediction model is proposed that uses the Elastic Net algorithm to analyze historical sales and inventory data. The model is used to improve prediction accuracy, allowing SMEs to optimize inventories, reduce costs and improve efficiency. Experimental results indicate that the proposed model obtains better results in the MAE, RMSE and R2 indicators.Revisión por paresapplication/htmlengAssociation for Computing Machineryinfo:eu-repo/semantics/embargoedAccessRepositorio Academico - UPCUniversidad Peruana de Ciencias Aplicadas (UPC)ACM International Conference Proceeding Series611reponame:UPC-Institucionalinstname:Universidad Peruana de Ciencias Aplicadasinstacron:UPCInventory managementModel interpretationSMEsPredictive model based on machine learning for raw material purchasing management in the retail sector.info:eu-repo/semantics/articleLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorioacademico.upc.edu.pe/bitstream/10757/675912/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51false10757/675912oai:repositorioacademico.upc.edu.pe:10757/6759122024-09-29 18:38:06.141Repositorio académico upcupc@openrepository.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Predictive model based on machine learning for raw material purchasing management in the retail sector.
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