Journal of Korea Port Economic Association (한국항만경제학회지)

The Korea Port Economic Association (한국항만경제학회)
권호 표지

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

세계 주요선사의 경영성과에 대한 군집분석

  • 도티밍황 (목포해양대학교대학원 해상운송시스템학과) ;
  • 최경훈 (목포해양대학교) ;
  • 박계각 (목포해양대학교 국제해사수송시스템학부)
  • Received : 2017.11.14
  • Accepted : 2017.12.27
  • Published : 2017.12.29
Download PDF
⟨ Previous Next ⟩

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.

현재 경제 상황에서 세계 해운산업의 중요성은 매우 강조되고 있다. 최근 세계 경제 위기로 인해 전체 산업은 공급과 수요 측면에서 어려움에 직면에 하였으며 저효율 및 저수익 상황이라는 사실은 모든 이해 관계자들에게 불안감을 안겨주었다. 따라서 본 연구에서는 해운산업의 이해 관계자에게 세계 주요 해운회사의 재무성과를 자세히 살펴볼 수 있도록 주요 해운회사의 재무성과를 클러스터로 분류하였다. Fuzzy-C Means 기법을 활용하였으며 Levene 테스트와 ANOVA 테스트를 통하여 클러스터링 결과의 견고성을 평가하였다. 그 결과 유동성, 지급 여력 및 수익성이 분류 상 중요한 기준 되는 것으로 도출되었으며 이러한 결과는 선별 된 운송 회사의 경쟁력 수준을 제시하고 있으며 클러스터에 속한 기업은 동일한 특성을 갖고 있으므로 클러스터 내 한기업 특성을 파악하면 나머지 기업의 특성도 파악할 수 있어서 투자 결정함에 있어서 중요한 판단 기준으로 활용할 수 있다.

Keywords

References

  1. 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.
  2. Bezdek J.C. (1984), FCM: The Fuzzy C-means clustering algorithm, Computers and Geosciences, Vol 10, Issues 2-3, pp. 191-203. https://doi.org/10.1016/0098-3004(84)90020-7
  3. BHS1Global (2017), The hidden causes of the Hanjin bankruptcy crisis, Available from https://apmea.bhs1global.com/, last accessed in Dec 2017.
  4. Braden D. (2016), Hanjin Shipping bankruptcy timeline:How did we get here?, Available from https://www.joc.com/, last accessed in Dec 2017.
  5. 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. https://doi.org/10.1016/j.ins.2011.09.013
  6. 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.
  7. Ding Y. S. (2008), Forecasting financial condition of Chineselisted companies based on support vector machine, Expert System Application Journal, Vol 34, pp. 3081-3089. https://doi.org/10.1016/j.eswa.2007.06.037
  8. 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.
  9. 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. https://doi.org/10.1016/j.ajsl.2017.06.002
  10. 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.
  11. 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.
  12. Ko P. C. (2006), An evolution-based approach with modularized evaluationsto forecast financial distress, Knowledge Based System Journal, Vol 19, pp. 84-91. https://doi.org/10.1016/j.knosys.2005.11.006
  13. 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.
  14. 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. https://doi.org/10.5394/KINPR.2010.34.3.243
  15. 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. https://doi.org/10.1016/j.neucom.2009.02.018
  16. Maersk Group Annual Report 2015 (2016), Conference call 9.30am CET, Available from http://www.maersk.com, last accessed in July 2017.
  17. 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.
  18. 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. https://doi.org/10.5121/ijdkp.2014.4502
  19. 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.
  20. Szakonyi M. (2016), Shippers look deeper than carrier losses to avoid next Hanjin, Avaliable from https://www.joc.com/, last accessed in Dec 2017.
  21. 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.
  22. 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. https://doi.org/10.1080/00405000701489412
  23. Wan Hai Lines Ltd. Annual Report 2015 (2016), Available at http://www.wanhai.com, last accessed in Dec 2017.
  24. 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.
  25. 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. https://doi.org/10.1016/j.cie.2013.09.025
  26. Zhou Y. (2011), Research finance market based on Fuzzy C-means clustering, International Conference on Computer Science and Network Technology.