Transforming Freight Transport with Business Intelligence: A Case Study in the Peruvian Logistics Sector.

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Freight transport by road plays a crucial role in the economy of any nation by enabling the efficient distribution of materials and goods across its territory. However, this sector has faced challenges in decision-making, driving companies to explore methods that optimize this process through effect...

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Detalles Bibliográficos
Autores: Barrientos-Aguilar, A., Gamboa-Cruzado, J., López-De-Montoya, R.L., Céliz, N.M.O., Huaman, L.A., Sinche Crispin, F.S., Ríos-Toledo, G.
Formato: artículo
Fecha de Publicación:2025
Institución:Universidad Nacional de Cajamarca
Repositorio:UNC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.unc.edu.pe:20.500.14074/9904
Enlace del recurso:http://hdl.handle.net/20.500.14074/9904
https://doi.org/10.13053/cys-29-1-5544
Nivel de acceso:acceso abierto
Materia:Business intelligence
Hefesto
freight transport
OLTP
ETL
power BI
https://purl.org/pe-repo/ocde/ford#1.02.02
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spelling Barrientos-Aguilar, A.Gamboa-Cruzado, J.López-De-Montoya, R.L.Céliz, N.M.O.Huaman, L.A.Sinche Crispin, F.S.Ríos-Toledo, G.2026-02-25T17:44:23Z2026-02-25T17:44:23Z2025http://hdl.handle.net/20.500.14074/9904https://doi.org/10.13053/cys-29-1-5544Freight transport by road plays a crucial role in the economy of any nation by enabling the efficient distribution of materials and goods across its territory. However, this sector has faced challenges in decision-making, driving companies to explore methods that optimize this process through effective information management. This research aims to implement Business Intelligence (BI) to optimize freight transport and analyze its impact on improving operational efficiency and service quality. The study was based on a sample of 30 freight transport processes, individually evaluated to determine relevant indicators. To achieve this, the Hefesto methodology was applied, complemented by the development of dashboards using Power BI. The results showed a significant improvement in delivery punctuality, optimized distribution times, and increased customer satisfaction. Additionally, it is recommended that future research incorporate predictive models or data mining techniques to enhance analysis and decision-making.application/pdfengInstituto Politecnico Nacional.https://www.scopus.com/pages/publications/105003642154urn:issn:14055546Comput. Sist. 2025; 29(1): 511 - 528info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Business intelligenceHefestofreight transportOLTPETLpower BIhttps://purl.org/pe-repo/ocde/ford#1.02.02Transforming Freight Transport with Business Intelligence: A Case Study in the Peruvian Logistics Sector.info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:UNC-Institucionalinstname:Universidad Nacional de Cajamarcainstacron:UNCORIGINAL2007-9737-cys-29-01-511.pdf2007-9737-cys-29-01-511.pdfapplication/pdf1255351http://repositorio.unc.edu.pe/bitstream/20.500.14074/9904/1/2007-9737-cys-29-01-511.pdfe16b11d6b2984a98149c4866e2a19f0aMD5120.500.14074/9904oai:repositorio.unc.edu.pe:20.500.14074/99042026-02-26 11:05:38.223Universidad Nacional de Cajamarcarepositorio@unc.edu.pe
dc.title.es_PE.fl_str_mv Transforming Freight Transport with Business Intelligence: A Case Study in the Peruvian Logistics Sector.
title Transforming Freight Transport with Business Intelligence: A Case Study in the Peruvian Logistics Sector.
spellingShingle Transforming Freight Transport with Business Intelligence: A Case Study in the Peruvian Logistics Sector.
Barrientos-Aguilar, A.
Business intelligence
Hefesto
freight transport
OLTP
ETL
power BI
https://purl.org/pe-repo/ocde/ford#1.02.02
title_short Transforming Freight Transport with Business Intelligence: A Case Study in the Peruvian Logistics Sector.
title_full Transforming Freight Transport with Business Intelligence: A Case Study in the Peruvian Logistics Sector.
title_fullStr Transforming Freight Transport with Business Intelligence: A Case Study in the Peruvian Logistics Sector.
title_full_unstemmed Transforming Freight Transport with Business Intelligence: A Case Study in the Peruvian Logistics Sector.
title_sort Transforming Freight Transport with Business Intelligence: A Case Study in the Peruvian Logistics Sector.
author Barrientos-Aguilar, A.
author_facet Barrientos-Aguilar, A.
Gamboa-Cruzado, J.
López-De-Montoya, R.L.
Céliz, N.M.O.
Huaman, L.A.
Sinche Crispin, F.S.
Ríos-Toledo, G.
author_role author
author2 Gamboa-Cruzado, J.
López-De-Montoya, R.L.
Céliz, N.M.O.
Huaman, L.A.
Sinche Crispin, F.S.
Ríos-Toledo, G.
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Barrientos-Aguilar, A.
Gamboa-Cruzado, J.
López-De-Montoya, R.L.
Céliz, N.M.O.
Huaman, L.A.
Sinche Crispin, F.S.
Ríos-Toledo, G.
dc.subject.es_PE.fl_str_mv Business intelligence
Hefesto
freight transport
OLTP
ETL
power BI
topic Business intelligence
Hefesto
freight transport
OLTP
ETL
power BI
https://purl.org/pe-repo/ocde/ford#1.02.02
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#1.02.02
description Freight transport by road plays a crucial role in the economy of any nation by enabling the efficient distribution of materials and goods across its territory. However, this sector has faced challenges in decision-making, driving companies to explore methods that optimize this process through effective information management. This research aims to implement Business Intelligence (BI) to optimize freight transport and analyze its impact on improving operational efficiency and service quality. The study was based on a sample of 30 freight transport processes, individually evaluated to determine relevant indicators. To achieve this, the Hefesto methodology was applied, complemented by the development of dashboards using Power BI. The results showed a significant improvement in delivery punctuality, optimized distribution times, and increased customer satisfaction. Additionally, it is recommended that future research incorporate predictive models or data mining techniques to enhance analysis and decision-making.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2026-02-25T17:44:23Z
dc.date.available.none.fl_str_mv 2026-02-25T17:44:23Z
dc.date.issued.fl_str_mv 2025
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dc.identifier.doi.es_PE.fl_str_mv https://doi.org/10.13053/cys-29-1-5544
url http://hdl.handle.net/20.500.14074/9904
https://doi.org/10.13053/cys-29-1-5544
dc.language.iso.es_PE.fl_str_mv eng
language eng
dc.relation.ispartof.es_PE.fl_str_mv https://www.scopus.com/pages/publications/105003642154
urn:issn:14055546
Comput. Sist. 2025; 29(1): 511 - 528
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dc.publisher.es_PE.fl_str_mv Instituto Politecnico Nacional.
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