TY - JOUR
T1 - Transforming Freight Transport with Business Intelligence
T2 - A Case Study in the Peruvian Logistics Sector
AU - Barrientos-Aguilar, Alexis
AU - Gamboa-Cruzado, Javier
AU - de Montoya, Reyna L.
AU - Céliz, N. Mercedes Ortiz
AU - Huaman, Leonidas Asto
AU - Crispin, Fernando Sinche
AU - Ríos-Toledo, Germán
N1 - Publisher Copyright:
© 2025 Instituto Politecnico Nacional. All rights reserved.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Business intelligence
KW - ETL
KW - freight transport
KW - Hefesto
KW - OLTP
KW - power BI
UR - http://www.scopus.com/inward/record.url?scp=105003642154&partnerID=8YFLogxK
U2 - 10.13053/CyS-29-1-5544
DO - 10.13053/CyS-29-1-5544
M3 - Original Article
AN - SCOPUS:105003642154
SN - 1405-5546
VL - 29
SP - 511
EP - 528
JO - Computacion y Sistemas
JF - Computacion y Sistemas
IS - 1
ER -