Statistical Approach of Canonical Correlation Analysis, Risk Estimate Analysis and Response Surface Methodology towards Factors Affecting the Efficiency of the Management of Vessels

Gobi Krishnan Veluplay ., Wan Muhamad Amir W Ahmad ., Kasypi Mokhtar ., Halim ., Nor Azlida Aleng .


Initially, when there were no standard regulations or guidelines on safety, many accident cases were recorded with high fatality rates, loss of properties and environment pollution. Finally, International Safety Management Code (ISM Code) was introduced to enhance the maritime safety, however it is only applied to vessels above 500 Gross Register Tonnage (GRT) and hence the ships below 500 GRT are exempted from this regulation. By reason of lack of proper management system on board particularly on smaller ships, many other factors affecting the safety of vessels have arisen. In line with this, the accident rate does not fall over as it keeps on increasing. Therefore, this research was conducted to find out the factors contributing towards ineffective management as a result of lack in proper management system. The findings of the research were based on the analysis Canonical Correlation analysis, Risk Estimate Analysis and Response Surface Methodology. In short, human error factor is the most contributing factor towards an ineffective management system followed by external factor, stability factor and inefficient management. Hence, a proper model and valid safety management should be implemented for the sake of future maritime industry.


safety management, risk estimate, canonical correlation

Full Text:



Akten, N. (2006). Shipping accidents: a serious threat for marine environment. J. Black Sea/Mediterranean Environment, 12, 269-304.

Amir, W.M., Gobi, K.V., Kasypi, M., NurFadhlina, H., & Azlida, A. (2014). Comprehensive Analysis of the Factors That Affecting Inefficient Management of Vessels Using LRM, RSM and SEM. International Journal of Engineering and Applied Sciences, 5,2.

Bhattacharya, S. (2012). The effectiveness of the ISM Code: A qualitative enquiry. Marine Policy, 36, 528-535.

Dupont, W.D & Plummer, W.D. (1990). Power and sample size calculation: A review and computer program. Controlled Clinical Trails, 116-128.

Einarsson, S. & Brynjarsson, B. (2008). Improving human factors, incident and accident reporting and safety management systems in the Seveso industry. Journal of Loss Prevention in the Process Industry, 21, 550-554.

Fatigue: IMO guidance, mitigation and management. (2006), March. Seaways, 7-9. Fishing vessel accident probability. Journal of Safety Research, 33, 497-510.

Gobi, K.V., Amir, W.M., NurFadhlina, H., & Kasypi, M. (2014). Modeling and Analysis of Factors Affecting the Inefficient Management of Vessels: A Malaysian Case Study. International Journal of Innovation and Research in Educational Sciences, 1, 25-31.

Gordon, R., Kirwan, B. & Perrin, E. (2007). Measuring safety culture in a research and development centre: a comparison of two methods in the Air Traffic Management domain. Safety Science, 45(6), 669–95.

Havold, J.I. (2010).Safety culture aboard fishing vessels. Safety Science, 48, 1054-1061.

International Maritime Organization. (2001). Guidelines on fatigue. London.

International Safety Management Code (ISM Code). 1998). (IMO-117E).

Kobylinski, L. (2007). System and risk approach to ship safety, with special emphasis of stability. Archives of Civil and Mechanical Engineering, VII, 97-106.

Kobylinski, L. (2008). Stability of ships: risk assessment due hazards created by force of sea. Archives of Civil and Mechanical Engineering, VIII, 38-45.

Le Blanc, L.A. & Rucks, C.T. (1996). A multiple discriminant analysis of vessel accidents. Pergamon, 28, 501-510.

Mark, J.K. & Piet, R. (2009). The impact of climate change and weather on transport: An overview of empirical findings. Transportation Research Part D, 14, 205-221.

Mugusi, F.M., Mehta, S., Villamor, E., Urassa, W., Saathhoff, E., Bosch, R.J. & Fawzi, W.W. (2009). Factors Associated with mortality in HIV-infected and uninfected patients with pulmonary Tuberculosis. BMC Public Health.

Naing, N.N. (2003). Determination of sample size. Malaysian Journal of Medical Science, 84-86.

National Transportation Safety Board [NTSB]. (1981). Major Marine Collisions and Effects of Preventive Recommendations. Report No. NTSB-MSS-81-1.

Tarelko, W. (2012). Origins of ship safety requirement formulated by International Maritime Organization. Procedia Engineering, 45, 847-856.

Thematic Network for Safety Assessment of Waterborne Transport (THEMES). (2003). Report on suggestion for the integration of human factors in safety and environmental analysis. Retrieve from:

Watcher, J.K. & Yorio. P.L. (2013). A system of safety management practices and workerengagement for reducing and preventing accidents: An empirical and theoretical investigation. Accident Analysis and Prevention.

Wu, W. J. & Jeng, D.J.P. (2012). Safety Management Documentation Models for the Maritime Labour Convention, 2006. The Asian Journal of Shipping and Logistics, 28, 41-66.

Xhelilaj, E. & Lapa, K., (2010). The role of human fatigue factor towards maritime casualties. Maritime Transport & Navigational Journal, 2, 24-32.


  • There are currently no refbacks.

Editorial Office:

Educational Research Multimedia & Publications,
S.N. 21, Plot No 24, Mirza Ghalib Road Malegaon Nasik,
Maharashtra India - 423203.
+919764558895 (whatsapp),,

Copyrights © 2010-2020 - ERM Publications, India     

This work is licensed under