A structured risk modelling framework for palm oil cargo spoilage in maritime transportation

Palm oil is highly sensitive to quality degradation during maritime transport due to thermal instability, ineffective cleaning, and operational errors. This study employs the Intuitionistic Fuzzy Fault Tree Analysis method to model the uncertainty and expert-based subjectivity associated with spoilage risks. A fault tree structure was created based on expert consultation, identifying 71 basic events leading to the top event, defined as cargo spoilage. Linguistic evaluations from ten experts were converted into intuitionistic fuzzy numbers and aggregated to estimate the failure probabilities. The top event probability was calculated as 0.233, suggesting a significant likelihood of spoilage throughout the voyage. Among all basic events, insufficient steam supply, excessive steam, inadequate training, poor placement of cleaning equipment, and sensor failures emerged as major contributors. These findings indicate that spoilage stems from a combination of technical, operational, and human-related shortcomings. Improving training strategies, procedural discipline, and equipment reliability is essential to mitigate these risks. The proposed framework provides a structured and flexible methodology for analysing spoilage risks and supports effective decision making for maritime operators. It offers a valuable contribution to the safe and sustainable transport of palm oil by identifying vulnerabilities and guiding improvements in both technical systems and human performance.