Novel Indexes to Measure Competitiveness of Container Shipping Companies

Year 2020, , 29 - 53, 27.05.2020

Ahmet Selçuk Başarıcı Tanzer Satır

https://doi.org/10.26650/JTL.2020.0001

Abstract

The purpose of this study is to propose novel and efficient competitiveness indexes to measure the level of competition among container shipping operators based on a specific region. These indexes should require only basic data, which is full container throughput on the basis of terminal/ hinterland and ship operator. This study takes advantages of two methods to propose novel indexes as alternatives to Herfindahl Hirschman Index (HHI), which is very popular to measure level of competition. Originally named Competition-based Overall Similarity Measurement Index (COSMI) and Entropy Competitiveness Index (ECI) utilize overall similarity measure from clustering analysis and entropy methodologies, respectively. Both indexes have been proposed with two variants for each. COSMI200+ ignores the throughput of each SO having an amount less than 200 Twentyfoot Equivalent Units (TEUs), but COSMITOP5 takes into account only the top 5 SOs in terms of local throughput in a hinterland. ECI-JOINT includes a joint entropy coefficient which is constant for each hinterland, but ECI-VAR takes into account a variable entropy coefficient defined by the number of ship operators in each hinterland. Analyzing a dataset for the terminals located in Turkey, the Entropy Competitiveness Index (by means of ECI-JOINT variant) has been exhibited as a good alternative to HHI with a great correlation coefficient with it: 0.97. Theoretically, Competitionbased Overall Similarity Measurement Index (by means of COSMITOP5 variant) seems a promising method, but it is highly affected by outliers and inconstant numbers of ship operators per route, indicating a moderate correlation coefficient with HHI: 0.45.

Keywords

Cluster analysis, Competitiveness index, Entropy, Maritime container transportation, Overall Similarity Measurement

Konteyner Taşımacılık Şirketlerinin Rekabet Düzeyini Ölçen Yeni İndeksler

Abstract

Bu çalışmanın amacı, belli bir bölge bazlı olarak konteyner gemi operatörleri arasındaki rekabet seviyesini ölçmeye yarayan yeni ve etkin rekabet indeksleri oluşturmaktır. Bu indeksler terminal/bölge ve gemi operatörü bazlı olarak, sadece başlıca verilerden olan dolu konteyner sevkiyat verilerine ihtiyaç duymalıdır. Bu çalışma, rekabet seviyesi ölçümünde popüler olan Herfindahl Hirschman İndeksine (HHI) alternatif yeni indeksler oluşturabilmek için iki farklı yöntemden yararlanmaktadır. Özgün olarak Rekabet Bazlı Toplam Benzerlik Ölçüsü İndeksi (COSMI) ve Entropi Rekabet İndeksi (ECI) olarak adlandırılan bu indeksler, sırasıyla kümeleme analizi toplam benzerlik ölçüsü ve entropi yöntemlerinden yararlanmaktadırlar. Çalışmada her iki indeksin ikişer uyarlaması incelenmiştir. COSMI200+, bir lokal bölgedeki 200 TEU’dan daha az yükleme-tahliye performansı gösteren gemi operatörlerini gözardı ederken, COSMITOP5 sadece en yüksek performansa sahip 5 gemi operatörünü dikkate almaktadır. ECI-JOINT uyarlamasında sabit olan ortak bir entropi katsayısı kullanılırken, ECI-VAR uyarlamasında her bir lokal bölgedeki gemi operatörü sayısına göre değişen entropi katsayısı kullanılmaktadır. Türkiye’de mukim terminallere ait verilerin analizi, Entropi Rekabet İndeksi’nin (ECI-JOINT uyarlaması ile) HHI ile 0,97 değerinde korelasyon katsayısına sahip olduğunu ve bu indekse iyi bir alternatif olduğunu göstermektedir. Teorik olarak Rekabet Bazlı Toplam Rekabet Ölçüsü İndeksi (COSMITOP5 uyarlaması ile) umut veren bir yöntemi barındırsa da, dışadüşenler ve rota bazlı değişken gemi operatörü sayısı nedeniyle HHI ile 0,45 değerinde orta dereceli bir korelasyon katsayısına sahiptir.

Keywords

Deniz konteyner taşımacılığı, Entropi, Kümeleme analizi, Rekabet indeksi, Toplam Benzerlik Ölçüsü

References

• Almeida, R. & Fernandes, A.M. (2013). explaining local manufacturing growth in chile, the advantages of sectoral diversity, Applied Economics, 45 (16), pp. 2201-2213.
• BAE Systems (2007). Mediation, alignment, and information services for semantic interoperability (MAISSI): A Trade Study, (Report No. ADA470119), Burlington, Massachusetts: Final Technical Report.
• Baray, A. (2003). Entropi ve karar verme, Yönetim, Year: 14, Issue: 44, February 2003, pp.7-21. Bartholdi, J.J., Jarumaneeroj, P. & Ramudhin, A. (2016). A new connectivity index for container ports, Maritime Economics & Logistics, 18 (3), pp. 231–249.
• Cai, J. (2016). Computational Approaches for Estimating Life Cycle Inventory Data (Master’s thesis). Retrieved from https://deepblue.lib.umich.edu/ handle/ 2027.42/134693?show=full
• Clarke G. R. G. (2004), How Does the Investment Climate Affect Competition in Transition Economies? papers.ssrn.com. Retrieved March 18, 2017 from SSRN: https://ssrn.com/ abstract=790464 or http:// dx.doi.org/10.2139/ssrn.790464
• Competition Authority – Turkey (2018). Accessed: 09 January 2018, http://www.rekabet. gov.tr/tr/ Sayfa/ Yayinlar/rekabet-terimleri-sozlugu/terimler-listesi?icerik=8bfdbfb0-3cd1-4c6c-82f5-8ae801 72 67df
• Dillon, W. R. & Goldstein M. (1984). Multivariate Analysis Methods and Applications. New York: John Wiley and Sons.
• Diplaros, A. (2007), Exploiting spatial information for image segmentation and retrieval, (Doctoral dissertation). Retrieved from https://pure.uva.nl/ws/files/4416614/ 51736_ diplaros_ thesis.pdf
• Fauqueur, J. & Boujemaa, N. (2003). Region-based image retrieval: fast coarse segmentation and fine color description, Journal of Visual Languages and Computing 15 (1), pp.69–95.
• Galea, C., Farrugia, R., Muscat, A. & Zammit S. (2012). Objective quality of experience metrics for television services (Report No. MCA-2012-04). University of Malta: Department of Communications and Computer Engineering.
• Hair, J.F., William, C.B., Babin, B.J., Anderson, R.E., Tatham, R.L. (2009). Multivariate Data Analysis. Upper Saddle River, New Jersey: Pearson-Prentice Hall.
• Huggins, R. (2003). Creating a UK Competitiveness Index: Regional and Local Benchmarking, Regional Studies, 37:1, 89-96. Karam, A. & Eltawil, A.B. (2016). A Lagrangian relaxation approach for the integrated quay crane and internal truck assignment in container terminals, International Journal of Logistics Systems and Management, Vol. 24, Issue 1, pp. 113-136.
• Kanagala, A., Sahni, M., Sharma, S., Gou, B. & Yu, J. (2004). A probabilistic approach of HirschmanHerfindahl Index (HHI) to determine possibility of market power acquisition. Proceedings of Power Systems Conference and Exposition, 2004, (pp.1277-1282). The USA: New York, NY, October 10-13.
• Khazaeli, M.A. (2013). Automated Semantic Content Extraction from Images (Doctoral dissertation). Retrieved from https://digitalcommons.lsu.edu/gradschool_dissertations/2697/
• Lange, D. (2013), Effective and Effcient Similarity Search in Databases (Doctoral dissertation). Retrieved from https://s3.amazonaws.com/ academia.edu.documents/41866560
• Lee, P.T.W., Lin, C.W. & Chung, Y.S. (2014). Comparison analysis for subjective and objective weights of financial positions of container shipping companies, Maritime Policy & Management, 41, 3, pp.241–250.
• Lee, P.T.W., Lin, C.W. & Shin, S.H. (2012). A comparative study on financial positions of shipping companies in Taiwan and Korea using entropy and grey relation analysis, Expert systems with applications, Vol.39 (5), pp.5649-5657.
• Lloyd List Maritime Intelligence (2018). Informa, January 16, p.2.
• Miller, R.A. (1982, Fall). The Herfindahl-Hirschman Index As a Market Structure Variable: An Exposition for Antitrust Practitioners, The Antitrust bulletin, Retrieved from http://heinonline.org/HOL
• Niemann, M., Siebenhaar, M., Schulte, S. & Steinmetz, R. (2012). Comparison and retrieval of process models using related cluster pairs, Computers in Industry, 63 (2), p.168-180.
• Novak, D., Batko, M. &Zezula, P. (2012). Large-scale similarity data management with distributed Metric Index, Information Processing and Management 48 (5), pp.855-872.
• Ömürbek, N., Karaatlı, M. & Balcı, H.F. (2016). Analyzing the performances of automotive companies using entropy based MAUT and SAW methods, Dokuz Eylül Üniversitesi, İktisadi ve İdari Bilimler Fakültesi Dergisi, Vol.31, Issue No.1, pp. 227-255.
• Petit, L. (2012). The Competition Index, the Economic detection instrument of the Netherlands competition authority, papers.ssrn. Retrieved April 12, 2016 from http:\\ssrn.com/abstract=1992774
• Picard, N. & Franc, A. (2003). Are ecological groups of species optimal for forest dynamics modelling, Ecological Modelling, 163 (3), pp.175–186.
• Rathnayake, J. & Wijeratne, A.W. (2012). Second container port in Sri Lanka; Hambanthota or Trincomalee: an analysis using the game theory, International Journal of Logistics Systems and Management, Vol. 13, Issue 3, pp. 358-378.
• Robertson, H.I. (2013). Testing a new tool for alignment of musical recordings (Master’s thesis). Retrieved from http://digitool.library.mcgill.ca/webclient Rouhizadeh, M. (2015). Computational analysis of language use in autism (Doctoral dissertation). Retrieved from https://digitalcommons.ohsu.edu/etd/3732/
• Sicre, R. (2011). Analyse vidéo de comportements humains dans les points de ventes en temps-réel (Doctoral dissertation). Retreieved from http://www.theses.fr/en/2011BOR14261
• Skilling, D. & Zeckhauser, R.J. (2002). Political competition and debt trajectories in Japan and the OECD, Japan and the World Economy, 14 (2), pp.121-135.
• Su, D.T., Hsieh, C.H. & Tai, H.H. (2016). Container hub-port vulnerability: Hong Kong, Kaohsiung and Xiamen, Journal of Marine Engineering & Technology, 15:1, pp.19-30.
• Turney, P.D & Pantel, P. (2010). From Frequency to Meaning: Vector Space Models of Semantics, Journal of Artificial Intelligence Research 37, October 2010, pp.141-188.
• Van der Meer, C.A. (1997). A performance analysis of the faugeras color space as a component of color histogram-based image retrieval (Master’s thesis). Retrieved from http://www. dtic.mil/ docs/ citations/ ADA335595
• Vendrig, J. (2002). Interactive exploration of visual content. The Netherlands: Febodruk BV.
• Wei, C.P., Yang, C.S., Hsiao, H.W. & Cheng, T. H. (2006). Combining preference- and content-based approaches for improving document clustering effectiveness, Information Processing and Management, 42 (2), pp.350–372.
• Wilson, W.O. (2008). Immune Inspired Memory Algorithms Applied to Unknown Motif Detection (Doctoral dissertation). Retreived from https://pdfs.semanticscholar.org/ dd7b/ 7da0 b6ba54be 46a2 4a6ae8a2581984422ba1.pdf Yang,
• Y.C. & Shen, K.Y. (2013). Comparison of the operating performance of automated and traditional container terminals, International Journal of Logistics: Research and Applications, Vol. 16(2), pp.158– 173.
• Ye, X. (2016). Automated Software Defect Localization (Doctoral dissertation). Retreived from https://etd. ohiolink.edu/pg_10?0::NO:10:P10_ACCESSION_NUM:ohiou1462374079