Browse > Article

Cluster Analysis on the Management Performance of Major Shipping Companies in the World  

Do, Thi Minh Hoang (목포해양대학교대학원 해상운송시스템학과)
Choi, Kyoung Hoon (목포해양대학교)
Park, Gyei Kark (목포해양대학교 국제해사수송시스템학부)
Publication Information
Journal of Korea Port Economic Association / v.33, no.4, 2017 , pp. 17-36 More about this Journal
Abstract
In the modern economic context, it is apparent that there is a strong focus on the importance of global shipping industry. Recently, the world economic crisis has negatively influenced the industry with regard to both supply and demand, which has seen almost no sign of recovery. The fact that the entire industry is operating with low efficiency and at a low profit state has made all stakeholders anxious. This research examines the financial performance of the world's major shipping lines in order to give maritime stakeholders a closer look into the industry behind the ranking. Besides, the research evaluates the competitiveness of shipping companies in terms of financial ability and suggestions for strategic actions to stakeholders are provided. For these purposes, Fuzzy-C Means is used to cluster the selected lines into different groups based on their financial indices, namely liquidity, asset management, debt management and profitability. Levene's tests which are then followed by ANOVA tests are also utilized to assess the robustness of the clustering outcomes. The results indicate that liquidity, solvency and profitability act as the main criteria in the classification problem.
Keywords
Major Shipping Lines; Financial Performance; Clustering; Fuzzy-C Means;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Konsta K. (2013), Key performance indicators (KPIs), Shipping Marketing and Safety Orientation: The Case of Greek tanker shipping companies, International Journal of Business and Management, Vol. 63, No. 3-4, pp. 83-101.
2 Lee C. H., Ryoo D. K., Sohn B. R., Seo Y. J. (2010), A study on drawing priority of competitiveness factors of ship management, Journal of Navigation and Port Research, Vol. 34, No. 3, pp. 243-249.   DOI
3 Lin T. H. (2009), A cross model study of corporate financial distress prediction inTaiwan: multiple discriminant analysis, logit, probit and neural networks models, Neurocomputing Journal, Vol 72, pp. 3507-3516.   DOI
4 Maersk Group Annual Report 2015 (2016), Conference call 9.30am CET, Available from http://www.maersk.com, last accessed in July 2017.
5 Maro V. (2010), Shipping Companies' Financial Performance Measurement using Industry Key Performance Indicators. Case Study: The highly volatile period 2007-2010, SNAME's 3rd International Symposium on Ship Operations, Management and Economics.
6 Sivarathri S., Govardhan A. (2014), Experiments on hypothesis "Fuzzy k-means is better than k-means for clustering", International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol. 4, No. 5, pp. 21-34.   DOI
7 Szakonyi M. (2016), Shippers look deeper than carrier losses to avoid next Hanjin, Avaliable from https://www.joc.com/, last accessed in Dec 2017.
8 Wackett M. (2016), Trouble HMM wants cheaper charter hire, but its containerships face arrest if payments are withheld, Available from https://theloadstar.co.uk/, last accessed in Dec 2017.
9 Wang Y.J. (2010), Evaluating financial performance of Taiwan container shipping companies by strength and weakness indices, International Journal of Computer Mathematics, Vol. 87, No. 1, pp. 38-52.   DOI
10 Wan Hai Lines Ltd. Annual Report 2015 (2016), Available at http://www.wanhai.com, last accessed in Dec 2017.
11 Winkler R., Klawonn F., Kruse R. (2012), Problems of Fuzzy C-means clustering and similar algorithms with high dimensional data set, Challenges at the interface of data analysis, Computer Science and Optimization, pp. 79-87.
12 Yin X.F. (2013), A fuzzy C-means based hybrid evolutionary approach to the clustering of supply chain, Journal of Computers and Industrial Engineering, Vol. 66, pp. 768-780.   DOI
13 Zhou Y. (2011), Research finance market based on Fuzzy C-means clustering, International Conference on Computer Science and Network Technology.
14 Sys C., (2010), Inside the box: Assessing the competitive conditions, the concentration and the market structure of the container liner shipping industry, Doctoral Dissertation, Ghent University.
15 Chiang C.H. (2007), Performance evaluation of shipping companies with finance ratio and intellectual capital, Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, pp. 3089-3102.
16 Ansari A., Riasi A. (2016), Customer clustering using a combination of Fuzzy C-means and genetic algorithm, International Journal of Business and Management, Vol. 11, No. 7, pp 59-66.
17 Bezdek J.C. (1984), FCM: The Fuzzy C-means clustering algorithm, Computers and Geosciences, Vol 10, Issues 2-3, pp. 191-203.   DOI
18 BHS1Global (2017), The hidden causes of the Hanjin bankruptcy crisis, Available from https://apmea.bhs1global.com/, last accessed in Dec 2017.
19 Braden D. (2016), Hanjin Shipping bankruptcy timeline:How did we get here?, Available from https://www.joc.com/, last accessed in Dec 2017.
20 Chen M. Y. (2013), A hybrid ANFIS model for business failure prediction utilizingparticle swarm optimization and subtractive clustering, Information Science Journal, Vol 220, pp. 180-195.   DOI
21 Ding Y. S. (2008), Forecasting financial condition of Chineselisted companies based on support vector machine, Expert System Application Journal, Vol 34, pp. 3081-3089.   DOI
22 Dustin B. (2016), Hanjin Shipping bankruptcy timeline:How did we get here?, Available from http://www.joc.com/maritime-news/container-lines/hanjin-shipping/hanjin-shipping-bankruptcy-timeline-how-did-we-get-here_20160915.html, last accessed in. July 2017.
23 Ha Y. S., Seo J. S. (2017), An analysis of the competitiveness of major liner shipping companies, The Asian Journal of Shipping and Logistics, Vol. 33, No. 2, pp. 53-60.   DOI
24 Hugh R. M. (2016), Container shipping overcapacity forecast to worsen, Available from http://www.joc.com/maritime-news/container-lines/container-shipping-overcapacity-forecast-worsen_20161102.html, last accessed in July 2017.
25 Infographic, 2016, Top 20 shipping lines in the world, Available from http://blog.octopi.co/2016/08/17/top-20-shippinglines-in-the-world-infographic/, last accessed in. July 2017.
26 Ko P. C. (2006), An evolution-based approach with modularized evaluationsto forecast financial distress, Knowledge Based System Journal, Vol 19, pp. 84-91.   DOI