Analysis of the Efficiency and Effectiveness of Public Service Obligation (PSO) Sea Transportation Routes in Indonesia: A Case Study of PT PELNI (Persero)
DOI:
https://doi.org/10.38035/dijefa.v7i3.7116Keywords:
Data Envelopment Analysis (DEA), Public Service Obligation (PSO), operational efficiency, shipping routes, PT PELNI (Persero), maritime transportationAbstract
Maritime transportation under the Public Service Obligation (PSO) scheme plays a strategic role in ensuring interregional connectivity, particularly in Indonesia's disadvantaged, frontier, outermost, and border (3TP) areas. However, variations in the operational efficiency of PT PELNI (Persero)'s passenger ship routes may reduce the effectiveness of government subsidy utilization. This study aims to measure the operational efficiency of PSO passenger shipping routes, identify efficient and inefficient routes, and formulate operational improvement recommendations. A quantitative approach was employed using the Input-Oriented CCR Data Envelopment Analysis (DEA) model on 26 Decision Making Units (DMUs) representing PT PELNI (Persero)'s PSO passenger ship routes. The input variables consisted of fuel consumption, operating costs, and the number of crew members, while the output variables included the number of passengers, cargo volume, and sailing frequency. The study findings indicate that 5 vessel routes are operating efficiently, while the remaining 21 routes are still inefficient. Based on the DEA projection results, there is potential to improve efficiency through a reduction in fuel consumption of 37,599,416 litres (19%), operational cost savings of Rp744.57 billion (18%), and the optimisation of 458 crew members (20%), without reducing passenger volumes, cargo volumes, or sailing frequency. The relatively high fuel consumption, operating costs, and crew requirements are closely related to PT PELNI's role as a Public Service Obligation (PSO) operator serving government-assigned routes. The fleet continues to operate to ensure national connectivity and support regional economic activities through its ship promotes trade function, particularly in remote, frontier, and outermost (3T) regions. Consequently, operational costs tend to be higher than those of purely commercial shipping services.
References
Angelopoulos, J., Chlomoudis, C. I., Christofas, P., & Papadimitriou, S. (2013). Cost assessment of sea and air transport PSO services: The case of Greece. International Journal of Maritime, Trade & Economic Issues, 1(2), 3–49.
Badan Pusat Statistik. (2025). Statistik transportasi nasional 2025. Badan Pusat Statistik.
Bartosiewicz, A. (2024). Efficiency analysis of maritime transport systems using DEA-SBM approach.
Budiman, H., & Kurniawan, A. (2023). Efficiency of maritime transportation subsidies in Indonesia: A DEA approach. Journal of Maritime Transportation.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chen, X., & Wang, S. (2021). Fare competition and capital shift among transportation options: A behavioral analysis. Transportation Research Part A: Policy and Practice, 147, 46–61.
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver software (2nd ed.). Springer.
Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Handbook on data envelopment analysis (2nd ed.). Springer. https://doi.org/10.1007/978-1-4419-6151-8
Duygun, M., et al. (2020). Port efficiency in emerging markets: A DEA approach.
Emrouznejad, A. (2025). Development and evolution of slacks-based measure models in Data Envelopment Analysis. Journal of Economic Surveys.
Farmakis, P., Chassiakos, A., & Karatzas, S. (2023). A multi-objective tri-level algorithm for hub-and-spoke network in short sea shipping transportation. Algorithms, 16(8), 379. https://doi.org/10.3390/a16080379
Guo, X., Xu, Y., & Liu, S. (2023). Factors affecting passenger ship load factors: Route design, pricing, and capital integration. Maritime Policy & Management, 50(4), 565–580.
Krmac, E., & Mansouri Kaleibar, M. (2023). A comprehensive review of Data Envelopment Analysis (DEA) methodology in port efficiency evaluation. Maritime Economics & Logistics, 25(4), 817–881. https://doi.org/10.1057/s41278-022-00239-5
Li, Z., & Hensher, D. A. (2022). Marketing strategies and pricing in multi-modal transport systems: Impacts on demand and occupancy. Transportation Research Part E: Logistics and Transportation Review, 163, 102784.
Liu, F. F. F., & Shih, S. C. (2023). Data Envelopment Analysis models or the virtual gap analysis model: Which should be used for identifying the best benchmark for each unit in a group?
Nong, N.-M. T. (2023). Performance efficiency assessment of Vietnamese ports: An application of Delphi with Kamet principles and DEA model. The Asian Journal of Shipping and Logistics, 39(1), 1–12. https://doi.org/10.1016/j.ajsl.2022.10.002
Nong, T. N.-M., & Ha, D.-S. (2023). Port efficiency evaluation: An application of AHP and Malmquist DEA model. Journal of Distribution Science, 21(11), 55–66.
OECD. (2024). OECD economic surveys: Indonesia 2024. OECD Publishing. https://doi.org/10.1787/de87555a-en
Paramita, R. (2023). Meninjau implementasi kebijakan Public Service Obligation di sektor transportasi. Jurnal Budget: Isu dan Masalah Keuangan Negara, 8(1), 52–70. https://doi.org/10.22212/jbudget.v8i1.146
Park, K. H. (2018). DEA and transportation efficiency. In Handbook of International Trade and Transportation.
PT Pelayaran Nasional Indonesia (Persero). (2024). Laporan tahunan PT PELNI 2024. PT Pelayaran Nasional Indonesia (Persero).
Rinaldi, Y., & Budiman, H. (2022). Efficiency analysis of maritime transportation subsidy routes. Indonesian Journal of Maritime Transportation.
Samosir, A. P. (2014). Analisis kebijakan subsidi (Public Service Obligation) angkutan laut kelas ekonomi 2014 & 2015. Warta Penelitian Perhubungan, 26(7), 409–419. https://doi.org/10.25104/warlit.v26i7.924
Tone, K. (2001). A slacks-based measure of efficiency in Data Envelopment Analysis.
Van de Velde, D. (2013). Public service obligations in public transport. Research in Transportation Economics, 39(1), 32–40.
Yan, R., Wang, S., & Psaraftis, H. N. (2021). Data analytics for fuel consumption management in maritime transportation: Status and perspectives. Transportation Research Part E: Logistics and Transportation Review, 155, 102489. https://doi.org/10.1016/j.tre.2021.102489
Zhang, Y., & Wang, Q. (2021). Government fare regulation and operational performance in public-transport services. Transport Policy, 109, 1–9.
Zhu, J. (2020). Data envelopment analysis: A handbook of empirical studies and applications. Springer.
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