Cargo handling operations on board tankers pose a significant threat to the cleanliness and health of the ocean ecosystem. Incidents originating from these operations are often attributed to human error, as widely acknowledged. Therefore, it is crucial to control the human factor involved in these operations to enhance ship safety and foster a sustainable, clean marine environment. To tackle this problem, this paper presents a novel model that identifies the causal factors behind oil spills resulting from crew failure in these operations. To attain this, fuzzy Bayesian network (FBN) approach is used in this study to analyse the probabilistic correlations among the causal elements that are disclosed qualitatively and quantitatively. Sensitivity analyses and validation procedures are carried out to enhance the accuracy of results. Eliminating errors in cargo calculation is of paramount importance as research has shown that such errors lead to the largest impact on spill during loading and discharging (L&D) operations. The study's findings offer valuable insights into the causes of L&D operation-related spills. Ship management companies, the loss-prevention division of Protection and Indemnity Clubs (P&I), and regulatory bodies may employ the research results to prevent spill repetitions and protect the marine environment.
Keywords: Fuzzy Bayesian network; Human error; Oil spill; Tanker cargo handling.
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.