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TÜRK KONTEYNER LİMANLARININ FİZİKSEL ÖZELLİKLERİ VE ETKİNLİK SKORLARINA GÖRE KÜMELENMESİ

Yıl 2020, Cilt 12, 1 - 14, 05.10.2020
https://doi.org/10.18613/deudfd.803354

Öz

Liman performansının küresel tedarik zincirlerinin genel performansı üzerindeki önemli etkisi nedeniyle, terminal operasyonlarının verimliliğini artırmak, limanların rekabet avantajı elde etmeleri için önemli bir gerekliliktir. Verimliliğin liman rekabetinde oynadığı önemli role dayanarak, literatürde limanların göreceli verimliliklerini araştıran ve nispeten verimsiz olanlar için yönetimsel çıkarımlar ortaya koyan birçok çalışma bulunmaktadır. Ancak çoğu durumda, limanların boyutları, yük akış potansiyelleri veya içinde bulundukları ortam farklı niteliklere sahip olabileceğinden verimlilik analizinden elde edilen sonuçlar kendi başına yanıltıcı olabilirler. Bu sebeple çalışmamız Türk konteyner limanlarına odaklanarak, limanları hem fiziksel özelliklerini hem de etkinlik değerlerini dikkate alarak sınıflandırmayı amaçlamaktadır. Çalışmamızda limanların etkinlik değerlerini belirlemek için veri zarflama analizi, terminallerin sınıflandırılması için ise kümeleme analizi uygulanmaktadır. Kümeleme analizinin sonuçları, limanlar için kıyaslama seçeneklerinin daha iyi değerlendirilmesine imkan tanımakta ve Türk konteyner liman endüstrisinin özelliklerine genel bir bakış sağlamaktadır.

Kaynakça

  • Afifi, A., May, S. and Clark, V. A. (2011). Practical Multivariate Analysis. US: CRC Press.
  • 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.
  • Bacher, J. (1994). Clusteranalyse. Anwendungsorientierte Einführung. Germany: Oldenbourg Verlag.
  • Banker, R. D., Charnes, A. & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30 (9), 1078-1092.
  • Barros, C. P. (2003). The measurement of efficiency of Portuguese sea port authorities with DEA. International Journal of Transport Economics, 30 (3), 335-354.
  • Barros, C. P. and Athanassiou, M. (2015). Efficiency in European seaports with DEA: evidence from Greece and Portugal, in H.E. Haralambides (Ed.), Port Management, pp. 293-313. London: Palgrave Macmillan.
  • Charnes, A., Cooper, W.W. and Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2 (6), 429–444.
  • Cook, W. D., Tone, K. and Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1-4.
  • Cullinane, K. and Song, D. W. (2006). Estimating the relative efficiency of European container ports: a stochastic frontier analysis. Research in Transportation Economics, 16, 85-115.
  • De Koster, M. B. M., Balk, B. M. and Van Nus, W. T. I. (2009). On using DEA for benchmarking container terminals. International Journal of Operations & Production Management, 29 (11), 1140-1155.
  • Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., and Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of operational research, 132 (2), 245-259.
  • Grebitus, C. (2008). Food Quality from The Consumer's Perspective: An Empirical Analysis of Perceived Pork Quality. Germany: Cuvillier Verlag.
  • Karagöz, Y. (2016). SPSS ve AMOS 23 Uygulamalı Istatiksel Analizler. Ankara: Nobel Akademik.
  • López-Espín, J. J., Aparicio, J., Giménez, D. and Pastor, J. T. (2014). Benchmarking and data envelopment analysis. An approach based on metaheuristics. Procedia Computer Science, 29, 390-399.
  • Panayides, P. M., Maxoulis, C. N., Wang, T. F. and Ng, K. Y. A. (2009). A critical analysis of DEA applications to seaport economic efficiency measurement. Transport Reviews, 29 (2), 183-206.
  • Park, Y. S., Mohamed Abdul Ghani, N. M. A., Gebremikael, F. and Egilmez, G. (2019). Benchmarking environmental efficiency of ports using data mining and RDEA: the case of a US container ports. International Journal of Logistics Research and Applications, 22 (2), 172-187.
  • Robinson, R. (2002). Ports as elements in value-driven chain systems: the new paradigm. Maritime Policy & Management, 29 (3), 241-255.
  • Roll, Y. and Hayuth, Y. (1993). Port performance comparison applying data envelopment analysis (DEA). Maritime Policy & Management, 20 (2), 153-161.
  • Schøyen, H. and Odeck, J. (2013). The technical efficiency of Norwegian container ports: A comparison to some Nordic and UK container ports using Data Envelopment Analysis (DEA). Maritime Economics & Logistics, 15 (2), 197-221.
  • Shi, W. and Li, K. X. (2017). Themes and tools of maritime transport research during 2000-2014. Maritime Policy & Management, 44 (2), 151-169.
  • Tovar, B. and Rodríguez-Déniz, H. (2015). Classifying ports for efficiency benchmarking: A review and a frontier-based clustering approach. Transport Reviews, 35 (3), 378-400.
  • TURKLIM (2017). Port sector report. Türkiye Liman İşletmecileri Derneği,.İstanbul.
  • Wiegmans, B. and Dekker, S. (2016). Benchmarking deep-sea port performance in the Hamburg-Le Havre range. Benchmarking: An International Journal, 23 (1), 96-112.
  • Woo, S. H., Pettit, S., Beresford, A. and Kwak, D. W. (2012). Seaport research: A decadal analysis of trends and themes since the 1980s. Transport Reviews, 32 (3), 351-377.

CLUSTERING TURKISH CONTAINER PORTS BASED ON PHYSICAL ATTRIBUTES AND EFFICIENCY SCORES

Yıl 2020, Cilt 12, 1 - 14, 05.10.2020
https://doi.org/10.18613/deudfd.803354

Öz

Due to the significant impact of port performance on overall performance of global supply chains, enhancing the efficiency of terminal operations is an important task for ports to achieve a competitive edge. By acknowledging the role that efficiency plays in port competition, there are many research in the literature revealing the relative efficiencies of ports that are under investigation and providing managerial implications for the ones that are relatively inefficient. However, in many cases, the results obtained from the efficiency analysis can be misleading on its own as the ports may have different natures in terms of their size, cargo flow potentials or the environment that they are embedded in. Therefore, focusing on Turkish container ports, our study aims to classify the ports by taking both their physical attributes and efficiency scores into consideration. In order to determine the efficiency scores our study applies data envelopment analysis and the classification of the terminals is carried out by the application of cluster analysis. Results of the clustering lead to better assessment of benchmarking options for the ports and provide a general overview of the characteristics of Turkish container port industry.

Kaynakça

  • Afifi, A., May, S. and Clark, V. A. (2011). Practical Multivariate Analysis. US: CRC Press.
  • 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.
  • Bacher, J. (1994). Clusteranalyse. Anwendungsorientierte Einführung. Germany: Oldenbourg Verlag.
  • Banker, R. D., Charnes, A. & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30 (9), 1078-1092.
  • Barros, C. P. (2003). The measurement of efficiency of Portuguese sea port authorities with DEA. International Journal of Transport Economics, 30 (3), 335-354.
  • Barros, C. P. and Athanassiou, M. (2015). Efficiency in European seaports with DEA: evidence from Greece and Portugal, in H.E. Haralambides (Ed.), Port Management, pp. 293-313. London: Palgrave Macmillan.
  • Charnes, A., Cooper, W.W. and Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2 (6), 429–444.
  • Cook, W. D., Tone, K. and Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1-4.
  • Cullinane, K. and Song, D. W. (2006). Estimating the relative efficiency of European container ports: a stochastic frontier analysis. Research in Transportation Economics, 16, 85-115.
  • De Koster, M. B. M., Balk, B. M. and Van Nus, W. T. I. (2009). On using DEA for benchmarking container terminals. International Journal of Operations & Production Management, 29 (11), 1140-1155.
  • Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., and Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of operational research, 132 (2), 245-259.
  • Grebitus, C. (2008). Food Quality from The Consumer's Perspective: An Empirical Analysis of Perceived Pork Quality. Germany: Cuvillier Verlag.
  • Karagöz, Y. (2016). SPSS ve AMOS 23 Uygulamalı Istatiksel Analizler. Ankara: Nobel Akademik.
  • López-Espín, J. J., Aparicio, J., Giménez, D. and Pastor, J. T. (2014). Benchmarking and data envelopment analysis. An approach based on metaheuristics. Procedia Computer Science, 29, 390-399.
  • Panayides, P. M., Maxoulis, C. N., Wang, T. F. and Ng, K. Y. A. (2009). A critical analysis of DEA applications to seaport economic efficiency measurement. Transport Reviews, 29 (2), 183-206.
  • Park, Y. S., Mohamed Abdul Ghani, N. M. A., Gebremikael, F. and Egilmez, G. (2019). Benchmarking environmental efficiency of ports using data mining and RDEA: the case of a US container ports. International Journal of Logistics Research and Applications, 22 (2), 172-187.
  • Robinson, R. (2002). Ports as elements in value-driven chain systems: the new paradigm. Maritime Policy & Management, 29 (3), 241-255.
  • Roll, Y. and Hayuth, Y. (1993). Port performance comparison applying data envelopment analysis (DEA). Maritime Policy & Management, 20 (2), 153-161.
  • Schøyen, H. and Odeck, J. (2013). The technical efficiency of Norwegian container ports: A comparison to some Nordic and UK container ports using Data Envelopment Analysis (DEA). Maritime Economics & Logistics, 15 (2), 197-221.
  • Shi, W. and Li, K. X. (2017). Themes and tools of maritime transport research during 2000-2014. Maritime Policy & Management, 44 (2), 151-169.
  • Tovar, B. and Rodríguez-Déniz, H. (2015). Classifying ports for efficiency benchmarking: A review and a frontier-based clustering approach. Transport Reviews, 35 (3), 378-400.
  • TURKLIM (2017). Port sector report. Türkiye Liman İşletmecileri Derneği,.İstanbul.
  • Wiegmans, B. and Dekker, S. (2016). Benchmarking deep-sea port performance in the Hamburg-Le Havre range. Benchmarking: An International Journal, 23 (1), 96-112.
  • Woo, S. H., Pettit, S., Beresford, A. and Kwak, D. W. (2012). Seaport research: A decadal analysis of trends and themes since the 1980s. Transport Reviews, 32 (3), 351-377.

Ayrıntılar

Birincil Dil İngilizce
Konular Denizcilik
Bölüm Makaleler
Yazarlar

Bayram Bilge SAĞLAM Bu kişi benim
DOKUZ EYLÜL ÜNİVERSİTESİ, DENİZCİLİK FAKÜLTESİ
0000-0003-4977-1634
Türkiye


Abdullah AÇIK> (Sorumlu Yazar)
DOKUZ EYLÜL ÜNİVERSİTESİ, DENİZCİLİK FAKÜLTESİ
0000-0003-4542-9831
Türkiye

Yayımlanma Tarihi 5 Ekim 2020
Yayınlandığı Sayı Yıl 2020, Cilt 12, Sayı

Kaynak Göster

Bibtex @araştırma makalesi { deudfd803354, journal = {Dokuz Eylül Üniversitesi Denizcilik Fakültesi Dergisi}, issn = {1309-4246}, eissn = {2458-9942}, address = {}, publisher = {Dokuz Eylül Üniversitesi}, year = {2020}, volume = {12}, pages = {1 - 14}, doi = {10.18613/deudfd.803354}, title = {CLUSTERING TURKISH CONTAINER PORTS BASED ON PHYSICAL ATTRIBUTES AND EFFICIENCY SCORES}, key = {cite}, author = {Sağlam, Bayram Bilge and Açık, Abdullah} }
APA Sağlam, B. B. & Açık, A. (2020). CLUSTERING TURKISH CONTAINER PORTS BASED ON PHYSICAL ATTRIBUTES AND EFFICIENCY SCORES . Dokuz Eylül Üniversitesi Denizcilik Fakültesi Dergisi , IV. Ulusal Liman Kongresi ve V. Ulusal Deniz Turizmi Sempozyumu , 1-14 . DOI: 10.18613/deudfd.803354
MLA Sağlam, B. B. , Açık, A. "CLUSTERING TURKISH CONTAINER PORTS BASED ON PHYSICAL ATTRIBUTES AND EFFICIENCY SCORES" . Dokuz Eylül Üniversitesi Denizcilik Fakültesi Dergisi 12 (2020 ): 1-14 <https://dergipark.org.tr/tr/pub/deudfd/issue/57109/803354>
Chicago Sağlam, B. B. , Açık, A. "CLUSTERING TURKISH CONTAINER PORTS BASED ON PHYSICAL ATTRIBUTES AND EFFICIENCY SCORES". Dokuz Eylül Üniversitesi Denizcilik Fakültesi Dergisi 12 (2020 ): 1-14
RIS TY - JOUR T1 - TÜRK KONTEYNER LİMANLARININ FİZİKSEL ÖZELLİKLERİ VE ETKİNLİK SKORLARINA GÖRE KÜMELENMESİ AU - Bayram BilgeSağlam, AbdullahAçık Y1 - 2020 PY - 2020 N1 - doi: 10.18613/deudfd.803354 DO - 10.18613/deudfd.803354 T2 - Dokuz Eylül Üniversitesi Denizcilik Fakültesi Dergisi JF - Journal JO - JOR SP - 1 EP - 14 VL - 12 IS - SN - 1309-4246-2458-9942 M3 - doi: 10.18613/deudfd.803354 UR - https://doi.org/10.18613/deudfd.803354 Y2 - 2020 ER -
EndNote %0 Dokuz Eylül Üniversitesi Denizcilik Fakültesi Dergisi CLUSTERING TURKISH CONTAINER PORTS BASED ON PHYSICAL ATTRIBUTES AND EFFICIENCY SCORES %A Bayram Bilge Sağlam , Abdullah Açık %T CLUSTERING TURKISH CONTAINER PORTS BASED ON PHYSICAL ATTRIBUTES AND EFFICIENCY SCORES %D 2020 %J Dokuz Eylül Üniversitesi Denizcilik Fakültesi Dergisi %P 1309-4246-2458-9942 %V 12 %N %R doi: 10.18613/deudfd.803354 %U 10.18613/deudfd.803354
ISNAD Sağlam, Bayram Bilge , Açık, Abdullah . "CLUSTERING TURKISH CONTAINER PORTS BASED ON PHYSICAL ATTRIBUTES AND EFFICIENCY SCORES". Dokuz Eylül Üniversitesi Denizcilik Fakültesi Dergisi 12 / (Ekim 2020): 1-14 . https://doi.org/10.18613/deudfd.803354
AMA Sağlam B. B. , Açık A. CLUSTERING TURKISH CONTAINER PORTS BASED ON PHYSICAL ATTRIBUTES AND EFFICIENCY SCORES. Dokuz Eylül Üniversitesi Denizcilik Fakültesi Dergisi. 2020; 12: 1-14.
Vancouver Sağlam B. B. , Açık A. CLUSTERING TURKISH CONTAINER PORTS BASED ON PHYSICAL ATTRIBUTES AND EFFICIENCY SCORES. Dokuz Eylül Üniversitesi Denizcilik Fakültesi Dergisi. 2020; 12: 1-14.
IEEE B. B. Sağlam ve A. Açık , "CLUSTERING TURKISH CONTAINER PORTS BASED ON PHYSICAL ATTRIBUTES AND EFFICIENCY SCORES", Dokuz Eylül Üniversitesi Denizcilik Fakültesi Dergisi, c. 12, ss. 1-14, Eki. 2020, doi:10.18613/deudfd.803354

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