Maritime Investigation Reports Involving Man-Over-Board (MOB) Casualties: A Methodology for Evaluation Process

Yıl 2019, Cilt: 5 Sayı: 2, 141 - 170, 17.12.2019

Orhan Gönel İsmail Çiçek

Öz

Flag states must issue their maritime investigation reports
in accordance with the International Maritime Organisation (IMO) circulars with
the inclusion of ‘lessons learned’ items from occurring accidents or incidents.
To identify the root cause of an event, therefore to include ‘lessons learned
information’ from reports, there must be enough detail of information about the
investigated event presented in reports. The information included in reports
may help identifying the procedural deficiencies or technical challenges. Considering
the Man-Over-Board (MOB) events as a sub group of maritime accident
investigations, authors systematically reviewed over 100 reports containing MOB
events in this study. Reviewed reports indicated major differences in formats
as well as level and type of information.

In this study, a systematic methodology for reviewing and
reporting the overall information retrieved from maritime accident reports is
presented. To cover all information from reviewed reports, 113 information
items are identified. An associated standard form is developed for use in
extracting information from all investigation reports. Enabling the data
collected systematically from reports, issued by the world maritime accident
reporting states and agencies, and successively populated into a database for
overall analysis, this form is called “Maritime MOB Events Investigation Form
(MEI Form)”. This paper presents the content of the MEI Form and demonstrates
the methodology of use for retrieving, formatting and analysing the information
from the MOB investigation reports using case examples. Benefits of collecting
this data in a structured execution methodology as part of the BIG DATA project
is shown.

Anahtar Kelimeler

Maritime Accident Investigation, Casualty Investigation Code, Man Over Board (MOB), Lessons Learned, Big Data, Database, Data Format, Report Forms

Denize Adam Düşme (DAD) Kazaları İçeren Deniz Kazası İnceleme Raporları: Bir Değerlendirme İşlem Yöntemi

Öz

Bayrak devletleri, deniz kazaları inceleme raporlarını Uluslararası
Denizcilik Örgütü (IMO) genelgelerine uygun olarak ve kaza veya olaylardan
öğrenilen dersleri içerecek şekilde yayınlamak zorundadırlar. Bir olayın kök
sebebinin tanımlamak için ve bu nedenle raporlardan "Çıkartılan Dersler"
dâhil edebilmesi için, sunulan raporlarda araştırılan olay ile ilgili yeterli
bilgi detayı olması gereklidir. Raporlarda yer alan bilgiler olay esnasında
yapılan işlemlerdeki eksikliklerin veya oluşan teknik zorlukların
belirlenmesine yardımcı olabilir. Bu çalışmada, Denize Adam Düşmesi (DAD)
olayları deniz kazaları araştırmasının bir alt grubu olarak değerlendirilmiş ve
DAD olaylarını içeren 100'den fazla rapor sistematik olarak gözden
geçirilmiştir. İncelenen raporlarda, format ve bilgilerin yanı sıra bilgi içeriklerinde
de önemli farklılıkların olduğunu tespit edilmiştir.

Bu çalışmada, deniz kazaları raporlarından elde edilen genel bilgilerin
gözden geçirilmesi ve raporlanması için sistematik bir yöntem sunulmuştur.
İncelenen raporlardaki tüm bilgileri kapsayacak şekilde 113 bilgi maddesi
tanımlanmıştır. Tüm araştırma raporlarından bilgi çıkarmada kullanmak amacıyla
bir standart form oluşturulmuştur. Dünyada deniz kazalarını rapor eden
devletler ve ajanslar tarafından yayınlanan ve genel analiz için bir veri tabanına
yerleştirilen raporlardan sistematik olarak toplanan verilerin sağlanması için
kullanılacak olan bu form “Denizcilik DAD Olayları İnceleme Formu (DAD Form
veya MEI Form)” olarak adlandırılmıştır. Bu çalışmada DAD Formunun içeriği
tanımlanmış, oluşturulan bu formlar kullanılarak araştırma raporlarından bilgi
derlenmesi, formatlanması ve analiz edilmesi amacıyla olay örnekleri ile
birlikte sistematik kaza inceleme yöntemi gösterilmiştir.

Anahtar Kelimeler

Deniz kazaları İnceleme, Kaza İnceleme Yönetmeliği, Denize Adam Düşmesi, Öğrenilen Dersler, Veri Formatlanması, Rapor Formatı

Kaynakça

• Edmonston, C., (2012). Sobering MOB Facts. Boat Owners Association of the United States. BoatUS Magazine. No: October-November 2012, pp. 62, Alexandria, USA.
• URL-1, IMO. SOLAS regulation I/21. IMO Casualties Page. (2019). 17 03 2019, http://www.imo.org/en/OurWork/MSAS/Casualties/Pages/Default.aspx.
• URL-2, Maritime Pollution Act (MARPOL). IMO Casualties Page. (2019). 17 03 2019, http://www.imo.org/en/OurWork/MSAS/Casualties/Pages/Default.aspx.
• Contracting Governments. International Convention on Load Lines (LL Convention). Article 23, 1966. London.
• United Nations. Convention on the Law of the Sea (UNCLOS). Article 94, 1982. Jamaica
• MSC. Code of International Standards and Recommended Practices for a Safety Investigation into a Marine Casualty or Marine Incident (Casualty Investigation Code). RESOLUTION MSC.255(84). International Maritime Organisation. IMO Maritime Safety Committee, 84th session, 2008. pp. resolution MSC.255(84)), revoking resolutions A.849(20) and A.884(21).
• ILO. INTERNATIONAL LABOUR CONFERENCE MARITIME LABOUR CONVENTION. Regulation 5.1.6 – Marine casualties. International Labor Organisation, 2006.
• URL-3, IMO. Casualties. International Maritime Organisation. (2019). 12 03 2019, http://www.imo.org/en/OurWork/MSAS/Casualties/Pages/Default.aspx.
• URL-4, IMO FSI. Casualty Analysis Procedure (CAP). (2019). 15 03 2019. 15 03 2019, http://www.imo.org/en/OurWork/MSAS/Casualties/Documents/CASUALTY%20ANALYSIS%20PROCEDURE.pdf.
• Kim, G. H., Trimi, S., & Chung, J. H. 3, (2014). Big-data applications in the government sector. Communications of the ACM, Vol. 57, pp. 78-85
• Gandomi, A., Haider, M., (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, Vol. 35, pp. 137-144.
• Koga, S. (2015). Major challenges and solutions for utilizing big data in the maritime industry. World Maritime University, Ph.D. Thesis.
• Bertot, J. C., Gorham, U., Jaeger, P. T., Sarin, L. C., & Choi, H., (2014). Big data, open government and e-government: Issues, policies and recommendations. Information Polity, pp. 5-16.
• McKinsey, G., I., (2011). Big data: The next frontier for innovation, competition, and productivity. Global Institute.
• Lambrou, M., (2016). Innovation capability, knowledge management and big data technology: a Maritime business case., International Journal of Advanced Corporate Learning (iJAC), Vol. 9, pp. 40-44.
• Gano, D., L., (2007). Comparison of common root cause analysis tools and methods. Apollo Root Cause Analysis-A new way of thinking. Apollonian Publ.
• Arslan, O., & Guler, N., (2011). Kimyasal tanker işletmeciliği için stratejik yönetim modellemesi., ITU Dergisi, Vol. 10.
• Kececi, T., Bayraktar, D., & Arslan, O., (2015). A ship officer performance evaluation model using fuzzy-ahp. Istanbul Technical University, 2015, Journal of Shipping and Ocean Engineering, Istanbul. Vol. 5, pp. 26-43.
• Akyuz, E., & Celik, M., (2014). Utilisation of cognitive map in modelling human error in marine accident analysis and prevention. Safety science, Vol. 70, pp. 19-28.
• Akyuz, E., & Celik, M., (II) (2014). A hybrid decision-making approach to measure effectiveness of safety management system implementations on-board ships., Safety Science, Vol. 68, pp. 169-179.
• Lim, C., H., (2010). A Study on the Introduction of IMO Casualty Investigation Code and Marine Safety Investigation System in Korea. Journal of the Korean Society of Marine Environment & Safety, Vol. 16, pp. 57-63.
• Schröder-Hinrichs, J. U., Baldauf, M., & Ghirxi, K., T., (2011). Accident investigation reporting deficiencies related to organizational factors in machinery space fires and explosions. Accident Analysis & Prevention, Vol. 43, pp. 1187-1196.
• Moradi, A., Etebarian, A., Shirvani, A., & Soltani, I., (2014). Development of a fuzzy model for Iranian marine casualties management. Journal of Fuzzy Set Valued Analysis, Vol. 1.
• Fukuoka, K., & Furusho, M., (2016). Relationship between latent conditions and the characteristics of holes in marine accidents based on the Swiss cheese model. World Maritime University Journal of Maritime Affairs, Vol. 15, pp. 267-292.
• Weber, R., Aha, D. W., Muñoz-Ávila, H., & Breslow, L. A. Berlin, (2000). An intelligent lessons learned process. International Symposium on Methodologies for Intelligent Systems. Vol. October, pp. 358-367.
• IMO MSC.255(84). Resolution MSC.255(84). Paragraph 2.11. 2008. p. 8.
• URL-5. IMO. Global Integrated Shipping Information System. International Maritime Organisation. (2019). 12 03 2019, https://gisis.imo.org/Public/MCI/Search.aspx.
• URL-6. Our Work: Lessons learned English. International Maritime Organisation. (2019) 12 03 2019, http://www.imo.org/en/OurWork/MSAS/Casualties/Pages/Lessons-learned.aspx.
• URL-7. Our Work: Lessons Learned French. International Maritime Organisation. (2019). 12 03 2019, http://www.imo.org/en/OurWork/MSAS/Casualties/Pages/Lessons-Learned-French.aspx.
• URL-8. Our Work: Lessons Learned Spanish. International Maritime Organisation. (2019). 12 03 2019, http://www.imo.org/en/OurWork/MSAS/Casualties/Pages/Lessons-Learned-Spanish.aspx.
• UK Inv. Rpt No 8/2011. FATAL MAN OVERBOARD FROM JOANNA Alongside in Glasgow, Scotland, 13 December 2010. UK Marine Accident Investigation Branch, June 2011.