A Digital Twin-Enabled Approach to Optimize Freight Fleet Operations in a Peruvian Transportation Company

The study evaluates the impact of a system enabled by Digital Twin on the optimization of the operations of a cargo fleet belonging to a transport company in Lima, Peru, during the year 2025. The proposed solution integrates OBD-II sensors, a Python processing engine, and a MongoDB-based data layer to build a synchronized virtual representation of 25 operating vehicles. An experimental, pre-experimental design with measurements before and after the intervention was applied to analyze changes in the frequency of accidents, the use of load capacity, and the monthly number of trips. The results show significant improvements: the frequency of accidents decreased from an average value of 0.08 to zero; the use of load capacity increased from 31 to 35 units; and the number of trips required to transport equivalent volumes decreased. These findings suggest that Digital Twin–based systems can support safer, more efficient, and data-driven operations in emerging logistics environments.

@article{Cabezas2026,
  title     = {A Digital Twin-Enabled Approach to Optimize Freight Fleet Operations in a Peruvian Transportation Company},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {4},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Deyber Flores Cabezas and Liset S. Rodriguez-Baca},
  doi       = {10.14569/IJACSA.2026.0170468},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170468}
}