Performance Analysis and Classification of the Ports in Gulf Countries Using Data Envelopment Analysis


Performance Analysis and Classification of the Ports in Gulf Countries Using Data Envelopment Analysis


Amin S. Hamdi

Department of Civil Engineering, King Abdulaziz University, Jeddah, Saudi Arabia


The study assesses relative efficiency and performance characteristics of the major ports in gulf countries using data envelopment analysis (DEA). Ports play key role in economic activities of these countries. The study reveal that technical efficiency of the ports in the region varies widely with mean constant and variable return to scale of 67.5 and 91 percent, respectively. Port Jabel Ali in UAE is identified as the benchmark for most of the inefficient ports indicating that the inefficient ports of the region should follow operational strategies of Port Jabel Ali. Also, all the inefficient ports of the region demonstrate increasing return to scale implying that their performance may improve with the scale of operation. Policy makers may consider investment to enhance operational scope of these ports.


Keywords: Ports; Data Envelopment; Analysis Efficiency; Scale

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How to cite this article:

Amin S. Hamdi. Performance Analysis and Classification of the Ports in Gulf Countries Using Data Envelopment Analysis. American Journal of Transportation and Logistics, 2019,2:13. DOI:10.28933/ajtl-2019-09-1805


References:

1. Al-Eraki, A.S., Mustafa, A. and Khader, A.T. (2010), An extended DEA window analysis: Middle East and East African seaports, Journal of Economic Studies, 37, 208-218.
2. Alam J.B., S.H. Sikder and K.G. Goulias, (2004), Role of transportation in regional economic efficiency in Bangladesh: A Data Envelopment Analysis. Transportation Research Record 1864, Journal of Transportation Research Board, USA.
3. Almawsheki, E.S. and Shah, M.Z. (2015), Technical Efficiency Analysis of Container Terminals in the Middle Eastern Region, The Asian Journal of Shipping and Logistics, 31(4): 477-486
4. Banker, R.D., Charnes, A. and Cooper, W.W., (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078–1092.
5. Charnes, A., Cooper, W. W., & Rhodes, E. (1978), Measuring the Efficiency Inefficiency of Decision Making Units, European journal of operational research, 2(6), 429-444.
6. Cook, W.D., E. Tone and J. Zhu (2014), Data Envelopment Analysis: Prior to Choosing a Model, Omega 44, 1-4.
7. Cullinane, K., Wang, T. F., Song, D. W., &Ji, P. (2006). The Technical Efficiency of Container ports: Comparing Data Envelopment Analysis and Stochastic Frontier Analysis, Transportation Journal of Operation Research Part A: Policy and Practice, 40(4), 354-3742, pp. 429 – 444.
8. Debreu, G. (1951) The Coefficient of Resource Utilization. Econometrica, 19, 273-292.
9. Demirel, B., Cullinane, K. and Haralambides, H. (2012), Container terminal efficiency and private sector participation, The Blackwell Companion to Maritime Economics, 571-598.
10. Ding, Z.Y., Jo, G.S., Wang, Y. and Yo, G.T. (2015) The relative efficiency of container terminals in small and medium sized ports in China, Asian Journal of Shipping and Logistics, 31, 231-251.
11. Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, Series A (General), 120(3), 253-290.
12. Hatami-Marbini, A., A. Emrouznejad and M. Tavana (2011), A Taxonomy and Review of the Fuzzy Data Envelopment Analysis Literature: Two Decades in the Making, European Journal of Operational Research 214(3): 457-472
13. Hung, S.W., Lu, W.M. and Wang, T.P. (2010), Benchmarking the operating efficiency of Asian container ports, European Journal of Operational Research, 203, 706-713.
14. Jiang, B. and Li, J. (2009), DEA-based performance measurement of seaports in northeast Asia: Radial and non-radial approach, Asian Journal of Shipping and Logistics, 25, 219-236.
15. Kutin, N., nguyen, T.T, and Vallee, T. (2017), Relative Efficiency of ASEAN Container Ports based on Data Envelopment Analysis, Asian Journal of Shipping and Logistics, 33(2): 67-77.
16. Leon, T., Liern, V., Ruiz, J. and Sirvent, I. (2003) A Possibilistic Programming Approach to the Assessment of Efficiency with DEA Models, Fuzzy Set and Systems, 139: 407-419.
17. Liu, C.C. (2008), Evaluating the operational efficiency of major ports in Asia Pacific region using data envelopment analysis, Applied Economics, 40, 1737-1743.
18. Medda, F. and Liu, Q. (2013) Determinants and Strategies for the Development of container terminal, Journal of Productivity Analysis, 40, 83-98.
19. Panayides, P/M., Maxoulis, C.N., Wang, T.F. and Ng, K.Y.A. ( 2009) A critical analysis of DEA application to seaport economic efficiency measurement, Transport Review, 29, 183-206.
20. R Core Team (2019), R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/
21. Sarriera, J.M., Araya, G., Serebrisky, T., Briceno-Garmendia, C. and Schwartz, J. (2013), Benchmarking container port technical efficiency in Latin America and the Caribbean, Inter-American Development Bank.
22. Schoyen, H. and Odeck, J. (2013), The Technical efficiency of Norwagian container port: A comparison to some Nordic and UK container ports using data envelopment analysis (DEA), Maritime Economics and Logistics, 15, 197-221.
23. Wu, Y.C.J. and Goh, M. (2010), Container Port Efficiency in Emerging and More Advanced Markets, Transportation Research Part E, 46:1030-1042.
24. Xu, Y. and Ishiguro, K. (2019), Measures of efficiency of Automated Container Terminals in China and Korea, Asian Transport Studies, Volume 5, Issue 4, pp 584-599.
25. Zahran, S.Z., Alam, J.B., Al-Zahrani, A.H., Smirlis, Y., Papadidimitriou, S. and Tsioumas, V. (2015), Analysis of port authority efficiency using data envelopment analysis, Maritime Economics and Logistics, 19(3).
26. Zahran, S.Z., Alam, J., Al-Zahrani, A.H., Smirlis, Y., Papadimitriou, S., & Tsioumas, V. (2017). Analysis of port efficiency using imprecise and incomplete data. Operational Research, 1-28.