Energy Supply Chain Optimization: Design of a Transportation Vendor Assessment System Using the Simple Additive Weighting Method
DOI:
https://doi.org/10.15294/edukom.v12i1.36054Keywords:
Decision Support System, SAW MethodAbstract
In the energy logistics sector, which demands high speed and efficiency, fuel transportation vendor selection is a strategic decision that significantly impacts operational smoothness. To transform the cumbersome manual selection process into digital precision, a study developed a Vendor Management Information System based on the Simple Additive Weighting (SAW) method. This system is designed to provide objective decision-making support by analyzing 2024 performance data through eight key evaluation criteria, including service quality, price, and fleet availability. After going through a normalization and weighting process in the decision matrix, the system determined Vendor A1 (PT. X) as the best provider with the highest score. The data is descriptive quantitative in nature, where the data collection process involved respondents from three departments within the company who are experts in the field of procurement, with proof of ownership of procurement certification for goods and services. A total of 23 respondents served as the basis for SAW data processing, and 5 people served as references for creating criteria for weighting in the method. This automation logic was then technically mapped through Data Flow Diagrams (DFDs) and Entity-Relationship Diagrams (ERDs) to ensure an integrated workflow. The implementation of this system marks a significant shift towards digital efficiency, which not only minimizes human error and increases transparency but also lays a strong foundation for the adoption of more sophisticated decision-making technologies in the future.
References
Astuti, D., et al. (2021). Performance Evaluation of Logistics Using SAW and SMART Methods. Journal of Logistics and Supply Chain Management, 3(2), 58–64.
Biswanghri, & Das, S. (2023). Application of MCDM Approach for Vendor Selection in LED Light Industry. In Computational Intelligence in Data Science (pp. 521–535). Springer.
Boakai, S., & Samanlioglu, F. (2023). An MCDM approach to third party logistics provider selection. International Journal of Logistics Systems and Management, 44(3), 351–370.
Chakraborty, S., & Mateen, A. (2025). Guest editorial: Global supply chain transformation using digital technologies in the post-pandemic era. Journal of Global Operations and Strategic Sourcing.
Evcioglu, M., & Kabak, Ö. (2023). Supplier selection in supply chain network using MCDM methods. Sigma Journal of Engineering and Natural Sciences, 41(4), 1082–1097.
Fadilla, A. N., Rosyidi, C. N., & Jauhari, W. A. (2022). Supplier selection and order allocation in a pharmaceutical wholesaler using BWM, TOPSIS, and MOLP. Indonesian Journal of Industrial Engineering, 23(2), 101–112.
Khan, S. A. R., et al. (2020). A hybrid MCDM approach for sustainable supplier selection: Evidence from the automotive industry. Sustainability, 12(4), 1528.
Key Innovations Shaping Digital Supply Chains in 2025. (2025). Jusda Global. Retrieved from https://www.jusdaglobal.com/en/article/key-innovations-digital-supply-chains-2025
Putranto, I. D., & Maulina, D. (2023). Sistem Pendukung Keputusan dengan Metode SMART untuk Menentukan Guru Terbaik. JACIS: Journal Automation Computer Information System, 3(2), 92–102.
Sarhir, O. (2025). Enhancing Supply Chain Resilience with Metaverse and ChatGPT Technologies. arXiv preprint arXiv:2501.14777.
Silaen, A. M., & Sibuea, M. L. (2020). Penerapan Data Flow Diagram (DFD) dalam Perancangan Sistem Informasi Akademik. Jurnal Teknologi dan Sistem Informasi, 1(2), 60–68.
Sustainable Supplier Selection through Multi-Criteria Decision Making (MCDM) Approach: A Bibliometric Analysis. (2023). Machines, 11(4), 96.
Zhang, J., Brintrup, A., Calinescu, A., Kosasih, E., & Sharma, A. (2021). Supply Chain Digital Twin Framework Design: An Approach of SCOR Model and System of Systems. arXiv preprint arXiv:2107.09485.
Zuhud, A., et al. (2025). A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization. arXiv preprint arXiv:2504.03692.
Key Innovations Shaping Digital Supply Chains in 2025. (2025). Jusda Global. Retrieved from https://www.jusdaglobal.com/en/article/key-innovations-digital-supply-chains-2025
