Socialization of the Operational Efficiency Program Vehicles at PT. Trans Continent Jakarta Distribution Hub

Imam Ozali; Yuliantini Yuliantini; Charles AN; Yana Tatiana; Rudy Max Damara Gugat

doi:10.55927/jpmf.v4i5.83

Authors

Imam Ozali Institut Transport Logistik Trisakti
Yuliantini Yuliantini Institut Transport Logistik Trisakti
Charles AN Institut Transport Logistik Trisakti
Yana Tatiana Institut Transport Logistik Trisakti
Rudy Max Damara Gugat Institut Transport Logistik Trisakti

DOI:

https://doi.org/10.55927/jpmf.v4i5.83

Keywords:

Operational, Vehicle, Productivity, Efficiency, Fuel Management

Abstract

This Community Service Program (PKM) aims to increase the understanding and awareness of drivers and operational staff of PT. The methods used in this activity are socialization through material presentations, interactive discussions, and simple simulations related to vehicle operational data. The materials presented include fuel saving strategies, the implementation of eco-driving, the importance of regular maintenance, and the use of the kilometer per liter (KM/L) ratio as a performance indicator. Participants in the activity consisted of drivers, operational staff, and management representatives who play a direct role in distribution activities. The expected results of this activity are an increased understanding of the participants regarding the concept of operational efficiency, a growing awareness to reduce waste, and the emergence of company initiatives in implementing monitoring technologies such as GPS tracking and fuel management systems. Thus, this activity is expected to provide a real contribution in reducing operational costs, increasing vehicle productivity, and strengthening the company's competitiveness in the national logistics industry.

References

Barbado, A., Corcho, Ó., Sánchez, D., & others. (2021). Reliable vehicle operating condition optimization under real world driving conditions: An explainable artificial intelligence approach. arXiv preprint. https://arxiv.org/abs/2107.06031

Choiril Hidayat, M., & Kinoro, I. (2023). Comparative efficiency of fleet management system versus transportation management system on transportation vehicle tracking system efficiency. The Management Journal of BINANIAGA, 8(2), 171–180. https://www.researchgate.net/publication/376998412

Hilgers, M. (2023). Fuel consumption and consumption optimization. Springer. https://link.springer.com/book/10.1007/978-3-662-66449-0

Mahalana, A., & Yang, Z. (2021). Overview of vehicle fuel efficiency and electrification policies in Indonesia. The International Council on Clean Transportation (ICCT). https://theicct.org/publication/overview-of-vehicle-fuel-efficiency-and-electrification-policies-in-indonesia

Romero, C. A. (2024). Strategies for reducing automobile fuel consumption. Applied Sciences, 14(2), 910. https://doi.org/10.3390/app14020910

Sembiring, M. T., Zuya, N., Laksmana, M. R. A., & Hadi, M. Z. (2025). Optimization of delivery allocation for enhanced fleet utilization and trip minimization: A case study from an Indonesian manufacturing company. Engineering Proceedings, 97(1), 37. https://doi.org/10.3390/engproc2025097037

Su, M., Su, Z., Cao, S., Park, K.-S., & Bae, S.-H. (2023). Fuel consumption prediction and optimization model for pure car/truck transport ships. Journal of Marine Science and Engineering, 11(6), 1231. https://doi.org/10.3390/jmse11061231

Xu, B., Wang, Y., Li, S., & Li, Q. (2019). Double-layer speed optimization for reducing fuel consumption. Transportation Research Part D: Transport and Environment, 67, 705–717. https://doi.org/10.1016/j.trd.2019.01.010

Yoo, S., Kim, J., Park, J., & Kang, S. (2025). Machine learning vehicle fuel efficiency prediction. PLoS ONE, 20(3), e0283745. https://doi.org/10.1371/journal.pone.0283745

Zhang, H., Chen, Y., & Wang, H. (2022). Fleet fuel efficiency evaluation using telematics data and real-world driving behavior. Journal of Advanced Transportation, 2022, Article 8861356. https://doi.org/10.1155/2022/886135