FACTORS CONSTRAİNİNG OPERATİONAL EFFİCİENCY IN MARİTİME TRANSPORTATİON: A COMPARATİVE ANALYSİS OF QUANTİTATİVE DECİSİON SUPPORT MODELS AND FİELD REALİTİES

Bora Öçal

doi:10.17753/sosekev.1889137

TR EN

Öz

Denizyolu Taşımacılığında Operasyonel Verimliliği Kısıtlayan Faktörler: Sayısal Temelli Karar Destek Modelleri ve Saha Gerçeklerinin Karşılaştırmalı Analizi

Öz

Bu çalışma, uluslararası denizyolu taşımacılığında operasyonel verimliliği kısıtlayan temel risk faktörlerini belirlemeyi ve bu faktörler doğrultusunda ele alınan altı kıtanın performanslarını karşılaştırmalı olarak analiz etmeyi amaçlamaktadır. Araştırma, nitel saha gözlemleri ile nicel karar destek modellerini birleştiren hibrit bir araştırma deseni üzerine kurgulanmıştır. Çalışma kapsamında, liman sıkışıklığı, bürokratik engeller, güvenlik/korsanlık, teknik ve yakıt sorunları, personel sorunları, çevresel koşullar ve jeopolitik riskler olmak üzere yedi temel kriter ele alınmıştır. Bu kriterlere göre Avrupa, Kuzey Amerika, Güney Amerika, Asya, Afrika ve Okyanusya kıtaları operasyonel riskler bakımından karşılaştırılmıştır. Araştırmanın çalışma grubu, kümülatif olarak 60 yıllık sektörel tecrübeye sahip üç kıdemli gemi kaptanından oluşmaktadır. Veri toplama aracı olarak yarı yapılandırılmış görüşmeler ve ikili karşılaştırma matrisleri kullanılmıştır. Verilerin analizinde, kriter ağırlıklandırma için BWM (Best-Worst Method) ve kıta sıralaması için SAW (Simple Additive Weighting) yöntemi uygulanmış ve bulgular, AHP (Analytic Hierarchy Process) ve TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) yöntemleri ile doğrulanmıştır. Analiz sonuçlarına göre bürokratik engeller, deniz ticaretindeki en baskın operasyonel kısıt olarak belirlenmiştir. Bu bulgu, sektördeki temel aksamaların teknik yetersizliklerden ziyade "Marlboro Kanalı" gibi kavramlarla ifade edilen kayıt dışı işlem maliyetlerinden kaynaklandığını teyit etmektedir. Bölgesel analizlerde Avrupa, en yüksek operasyonel performansa sahip kıta olarak öne çıkarken, Asya ve Afrika kıtaları manuel kayıt sistemleri ve güvenlik rantı gibi faktörler nedeniyle risklerin en yoğun yaşandığı bölgeler olarak saptanmıştır. Araştırma sonuçları ışığında, özellikle gelişmekte olan limanlarda yolsuzluk riskini minimize edecek "Tek Pencere" (Single Window) dijital beyan sistemlerinin yaygınlaştırılması ve güvenlik protokollerinin şeffaflaştırılması önerilmektedir.

Anahtar Kelimeler

Denizyolu taşımacılığı, Operasyonel verimlilik, Uzman görüşü, Best-Worst Method (BWM), Çok Kriterli Karar Verme (ÇKKV)

FACTORS CONSTRAİNİNG OPERATİONAL EFFİCİENCY IN MARİTİME TRANSPORTATİON: A COMPARATİVE ANALYSİS OF QUANTİTATİVE DECİSİON SUPPORT MODELS AND FİELD REALİTİES

Öz

This study aims to identify the key risk factors that constrain operational efficiency in international maritime transport and to conduct a comparative analysis of the performance of the six continents addressed in line with these factors. The research is based on a hybrid research design that combines qualitative field observations with quantitative decision support models. Within the scope of the study, seven key criteria were considered: port congestion, bureaucratic barriers, security/piracy, technical and fuel issues, personnel issues, environmental conditions, and geopolitical risks. Based on these criteria, Europe, North America, South America, Asia, Africa, and Oceania were compared in terms of operational risks. The research working group consisted of three senior ship captains with a cumulative 60 years of sectoral experience. Semi-structured interviews and pairwise comparison matrices were used as data collection tools. In the analysis of the data, the Best-Worst Method (BWM) was applied for criterion weighting and the Simple Additive Weighting (SAW) method was applied for continent ranking, and the findings were validated using the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. According to the analysis results, bureaucratic barriers have been identified as the most dominant operational constraint in maritime trade. This finding indicates that the fundamental disruptions in the sector stem from the costs of informal transactions, expressed through concepts such as the "Marlboro Canal," rather than technical inadequacies. In regional analyses, Europe was found to be the continent with the highest operational performance, while Asia and Africa were identified as the regions with the highest risk intensity. In light of the research findings, it is recommended that "Single Window" digital declaration systems, which minimise the risk of corruption, be widely implemented, particularly in developing ports, and that security protocols be made more transparent.

Anahtar Kelimeler

Maritime transportation, Operational efficiency, Expert judgment, Best-Worst Method (BWM), Multi-Criteria Decision Making (MCDM)

Kaynakça

Akhtar, M. J., & Utne, I. B. (2014). Human fatigue’s effect on the risk of maritime groundings – A Bayesian Network modeling approach. Safety Science, 62, 427–440. https://doi.org/10.1016/j.ssci.2013.10.002
Arıcan, O. H. (2025). Understanding the core determinants of bunker supply vessel selection for maritime industry: An integrated fuzzy-AHP and fuzzy-TOPSIS approach. Maritime Business Review. https://doi.org/10.1108/MABR-03-2025-0031
Bai, J., Yan, Y., & Bai, X. (2025). A comprehensive review of ship emission reduction technologies for sustainable maritime transport. Frontiers in Marine Science, 12, 1576661. https://doi.org/10.3389/fmars.2025.1576661
Bai, X., Jia, H., & Xu, M. (2022b). Identifying port congestion and evaluating its impact on maritime logistics. Maritime Policy & Management, 51, 345–362. https://doi.org/10.1080/03088839.2022.2135036
Bai, X., Wang, T., & Beloborodko, A. (2022a). Port congestion and its influence on maritime logistics: A case study of the container terminal. Journal of Shipping and Trade, 7(1), 1-22. https://doi.org/10.1186/s41072-022-00122-z
Brooks, M. R., Cullinane, K. P., & Pallis, A. A. (2017). Revisiting port governance and port reform: A multi-country examination. Research in Transportation Business & Management, 22, 1–10. https://doi.org/10.1016/j.rtbm.2017.02.005
Chang, C. C., & Jhang, C. W. (2016). Reducing speed and fuel transfer of the green flag incentive program in Kaohsiung Port Taiwan. Transportation Research Part D: Transport and Environment, 46, 1–10. https://doi.org/10.1016/j.trd.2016.03.007
Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1–9. https://doi.org/10.1016/S0165-0114(99)00058-1
Chen, J., Huang, T., Xie, X., Lee, P. T.-W., & Hua, C. (2019). Constructing governance framework of a green and smart port. Journal of Marine Science and Engineering, 7(4), 83.
Chowdhury, M. M. H., Askari, H. R., Bushra, R. T., & Ullah, T. R. (2024). Revisiting seafarers’ skills in the twenty-first century: A modified Delphi-BWM approach. Australian Journal of Maritime & Ocean Affairs, 16(2), 141-161. https://doi.org/10.1080/18366503.2023.2214429

Churchman, C. W., & Ackoff, R. L. (1954). An approximate measure of value. Journal of the Operations Research Society of America, 2(2), 172-187.
Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.
Fu, X., Ng, A. K. Y., & Lau, Y.-Y. (2010). The impacts of maritime piracy on global economic development: The case of Somalia. Maritime Policy & Management, 37(7), 677–697.
Haralambides, H. E. (2024). The Red Sea crisis and chokepoints to trade and international shipping. Maritime Economics and Logistics. https://doi.org/10.1057/s41278-024-00296-y
Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer-Verlag.
Jiang, M., & Lu, J. (2020). The analysis of maritime piracy occurred in Southeast Asia by using Bayesian network. Transportation Research Part E: Logistics and Transportation Review, 139, 101965. https://doi.org/10.1016/j.tre.2020.101965
Jiang, M., Lu, J., Yang, Z., & Li, J. (2020). Risk analysis of maritime accidents along the main route of the Maritime Silk Road: A Bayesian network approach. Maritime Policy & Management, 47(6), 815-832. https://doi.org/10.1080/03088839.2020.1730010
Koray, M., Kaya, E., & Keskin, M. H. (2025). Determining logistical strategies to mitigate supply chain disruptions in maritime shipping for a resilient and sustainable global economy. Sustainability, 17, 5261. https://doi.org/10.3390/su17125261
Lirn, T. C., Thanopoulou, H. A., Beynon, M. J., & Beresford, A. K. C. (2004). An application of AHP on transhipment port selection: A global perspective. Maritime Economics & Logistics, 6, 70-91. https://doi.org/10.1057/palgrave.mel.9100093
Liu, Q., Wang, Y., Zhang, R., Yan, H., Xu, J., & Guo, Y. (2023). Arctic weather routing: A review of ship performance models and ice routing algorithms. Frontiers in Marine Science, 10, 1190164. https://doi.org/10.3389/fmars.2023.1190164
Luo, M., & Shin, S.-H. (2019). Half-century research developments in maritime accidents: Future directions. Accident Analysis & Prevention, 123, 448–460. https://doi.org/10.1016/j.aap.2016.04.010
Mallam, S., Nazir, S., & Sharma, A. (2019). The human element in future Maritime Operations—Perceived impact of autonomous shipping. Ergonomics, 63, 334–345. https://doi.org/10.1080/00140139.2019.1659995
Markkula, J. S. K. (2022). The ship. History and Anthropology, 33(2), 188–195. https://doi.org/10.1080/02757206.2022.2066097
Mi, X., Tang, M., Liao, H., Shen, W., & Lev, B. (2019). The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what’s next? Omega, 87, 205-225. https://doi.org/10.1016/j.omega.2019.01.009
Milewska, B., & Milewski, D. (2022). Implications of increasing fuel costs for supply chain strategy. Energies, 15(19), 6934. https://doi.org/10.3390/en15196934
Monios, J., & Wilmsmeier, G. (2022). Maritime governance after COVID-19: How responses to market developments and environmental challenges lead towards degrowth. Maritime Economics & Logistics, 24(4), 699–722.
Munim, Z. H., & Schramm, H. J. (2018). The impacts of logistics performance on world trade. Production Planning & Control, 29(1), 1-11.
Nektarios, M., & Konstantinos, M. (2025). Measuring the impact of port congestion on containership freight rates. Maritime Transport Research, 8, 100130. https://doi.org/10.1016/j.martra.2025.100130
Notteboom, T., & Haralambides, H. (2025). Seaports in a tense geopolitical environment: Key agents or sitting ducks? Maritime Economics & Logistics, 27(1), 1–24.
Peng, P., Xie, X. W., Claramunt, C., Lu, F., Gong, F. Z., & Yan, R. (2025). Bibliometric analysis of maritime cybersecurity: research status, focus, and perspectives. Transportation Research Part E: Logistics and Transportation Review, 195, 103971. https://doi.org/10.1016/j.tre.2025.103971
Puisa, R., Lin, L., Bolbot, V., & Vassalos, D. (2018). Unravelling causal factors of maritime incidents and accidents. Safety Science, 110, 124–141. https://doi.org/10.1016/j.ssci.2018.08.001
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009
Rezaei, J., & Davoodi, S. (2019). Applications of Best-Worst Method in supply chain and logistics. Decision Science Letters, 8(1), 123–136. https://doi.org/10.5267/j.dsl.2019.1.002
Rezaei, J., van Wulfften Palthe, L., Tavasszy, L., Wiegmans, B., & van der Laan, F. (2019). Port performance measurement in the context of port choice: An MCDA approach. Management Decision, 57(2), 396-417. https://doi.org/10.1108/MD-04-2018-0482
Saaty, T. L. (1980). The Analytic Hierarchy Process: Planning, priority setting, resource allocation. McGraw-Hill.
Shamshol Bahri, M. S., Shariff, N. S. F. A., & Yahya, M. S. (2022). Comparative analysis on decision criteria for port personnel using hybrid Analytical Hierarchy Process (H-AHP). International Journal of the Analytic Hierarchy Process, 14(3). https://doi.org/10.13033/ijahp.v14i3.974
Shigunov, V. (2023). Extrapolation of failure rate over wave height. Ocean Engineering, 272, 113801. https://doi.org/10.1016/j.oceaneng.2023.113801
Soner, O., Celik, E., & Akyuz, E. (2022). A fuzzy best–worst method (BWM) to assess the potential environmental impacts of the process of ship recycling. Maritime Policy & Management, 49(3), 396-409. https://doi.org/10.1080/03088839.2021.1889066
Taherdoost, H. (2023). Analysis of Simple Additive Weighting Method (SAW) as a Multi-Attribute Decision Making Technique: A step-by-step guide. Journal of Management Science & Engineering Research, 6(1), 21-24. https://doi.org/10.30564/jmser.v6i1.5400
Trepte, K., & Rice, J. B. (2014). An initial exploration of port capacity bottlenecks in the USA port system and the implications on resilience. International Journal of Shipping and Transport Logistics, 6(3), 339–355.
Tsang, Y. P., Fan, Y., Feng, Z. P., & Li, Y. (2024). Examining supply chain vulnerability via an analysis of ESG-Prioritized firms amid the Russian-Ukrainian conflict. Journal of Cleaner Production, 434, 139754. https://doi.org/10.1016/j.jclepro.2023.139754
Ugboma, C., Ugboma, O., & Ogwude, I. C. (2006). An Analytic Hierarchy Process (AHP) approach to port selection decisions – Empirical evidence from Nigerian ports. Maritime Economics & Logistics, 8, 251–266. https://doi.org/10.1057/palgrave.mel.9100160
Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1–29.
Vettor, R., & Guedes Soares, C. (2016). Development of a ship weather routing system. Ocean Engineering, 123, 1–14. https://doi.org/10.1016/j.oceaneng.2016.06.035
Wan, C., Yuan, J., Cao, D., Wang, T., & Ng, A. K. (2024). A fuzzy evidential reasoning-based model for evaluating resilience of ports to typhoons. Transportation Research Part D: Transport and Environment, 133, 104228. https://doi.org/10.1016/j.trd.2024.104228
Zhou, X. Y., Liu, Z. J., Wang, F. W., Wu, Z. L., & Cui, R. D. (2020). Towards applicability evaluation of hazard analysis methods for autonomous ships. Ocean Engineering, 214, 107773. https://doi.org/10.1016/j.oceaneng.2020.107773

Ayrıntılar

Birincil Dil

İngilizce

Konular

Strateji, Yönetim ve Örgütsel Davranış (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Bora Öçal ^*
0000-0003-0654-0131
Türkiye

Erken Görünüm Tarihi

8 Ağustos 2026

Yayımlanma Tarihi

-

Gönderilme Tarihi

14 Şubat 2026

Kabul Tarihi

5 Temmuz 2026

Yayımlandığı Sayı

Yıl 2026 Sayı: Advanced Online Publication

DOI

https://doi.org/10.17753/sosekev.1889137

IZ

https://izlik.org/JA89EB38LM

APA

Öçal, B. (2026). FACTORS CONSTRAİNİNG OPERATİONAL EFFİCİENCY IN MARİTİME TRANSPORTATİON: A COMPARATİVE ANALYSİS OF QUANTİTATİVE DECİSİON SUPPORT MODELS AND FİELD REALİTİES. EKEV Akademi Dergisi, Advanced Online Publication, 369-393. https://doi.org/10.17753/sosekev.1889137

AMA

1.Öçal B. FACTORS CONSTRAİNİNG OPERATİONAL EFFİCİENCY IN MARİTİME TRANSPORTATİON: A COMPARATİVE ANALYSİS OF QUANTİTATİVE DECİSİON SUPPORT MODELS AND FİELD REALİTİES. EKEV Akademi Dergisi. 2026;(Advanced Online Publication):369-393. doi:10.17753/sosekev.1889137

Chicago

Öçal, Bora. 2026. “FACTORS CONSTRAİNİNG OPERATİONAL EFFİCİENCY IN MARİTİME TRANSPORTATİON: A COMPARATİVE ANALYSİS OF QUANTİTATİVE DECİSİON SUPPORT MODELS AND FİELD REALİTİES”. EKEV Akademi Dergisi, sy Advanced Online Publication: 369-93. https://doi.org/10.17753/sosekev.1889137.

EndNote

Öçal B (01 Ağustos 2026) FACTORS CONSTRAİNİNG OPERATİONAL EFFİCİENCY IN MARİTİME TRANSPORTATİON: A COMPARATİVE ANALYSİS OF QUANTİTATİVE DECİSİON SUPPORT MODELS AND FİELD REALİTİES. EKEV Akademi Dergisi Advanced Online Publication 369–393.

IEEE

[1]B. Öçal, “FACTORS CONSTRAİNİNG OPERATİONAL EFFİCİENCY IN MARİTİME TRANSPORTATİON: A COMPARATİVE ANALYSİS OF QUANTİTATİVE DECİSİON SUPPORT MODELS AND FİELD REALİTİES”, EKEV Akademi Dergisi, sy Advanced Online Publication, ss. 369–393, Ağu. 2026, doi: 10.17753/sosekev.1889137.

ISNAD

Öçal, Bora. “FACTORS CONSTRAİNİNG OPERATİONAL EFFİCİENCY IN MARİTİME TRANSPORTATİON: A COMPARATİVE ANALYSİS OF QUANTİTATİVE DECİSİON SUPPORT MODELS AND FİELD REALİTİES”. EKEV Akademi Dergisi. Advanced Online Publication (01 Ağustos 2026): 369-393. https://doi.org/10.17753/sosekev.1889137.

JAMA

1.Öçal B. FACTORS CONSTRAİNİNG OPERATİONAL EFFİCİENCY IN MARİTİME TRANSPORTATİON: A COMPARATİVE ANALYSİS OF QUANTİTATİVE DECİSİON SUPPORT MODELS AND FİELD REALİTİES. EKEV Akademi Dergisi. 2026;:369–393.

MLA

Öçal, Bora. “FACTORS CONSTRAİNİNG OPERATİONAL EFFİCİENCY IN MARİTİME TRANSPORTATİON: A COMPARATİVE ANALYSİS OF QUANTİTATİVE DECİSİON SUPPORT MODELS AND FİELD REALİTİES”. EKEV Akademi Dergisi, sy Advanced Online Publication, Ağustos 2026, ss. 369-93, doi:10.17753/sosekev.1889137.

Vancouver

1.Bora Öçal. FACTORS CONSTRAİNİNG OPERATİONAL EFFİCİENCY IN MARİTİME TRANSPORTATİON: A COMPARATİVE ANALYSİS OF QUANTİTATİVE DECİSİON SUPPORT MODELS AND FİELD REALİTİES. EKEV Akademi Dergisi. 01 Ağustos 2026;(Advanced Online Publication):369-93. doi:10.17753/sosekev.1889137

Öz

Denizyolu Taşımacılığında Operasyonel Verimliliği Kısıtlayan Faktörler: Sayısal Temelli Karar Destek Modelleri ve Saha Gerçeklerinin Karşılaştırmalı Analizi

Öz

Anahtar Kelimeler

FACTORS CONSTRAİNİNG OPERATİONAL EFFİCİENCY IN MARİTİME TRANSPORTATİON: A COMPARATİVE ANALYSİS OF QUANTİTATİVE DECİSİON SUPPORT MODELS AND FİELD REALİTİES

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Erken Görünüm Tarihi

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

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