Monte Carlo-Based Risk Probability Modeling for Ship Incident

Abstract

The shipping industry, a critical component of global logistics, faces persistent operational risks that threaten safety, environmental integrity, and economic stability. Traditional risk assessments, often reliant on descriptive statistics, fail to capture the probabilistic and multifaceted nature of maritime accidents. This study bridges this gap by developing a robust Monte Carlo simulation framework to quantify incident probabilities for a tanker fleet. Utilizing a comprehensive dataset from a shipping company, including incident reports, tanker characteristics, and root causes, the model iteratively samples operational and technical variables up to 50,000 iterations to project risk distributions and identify critical failure pathways. The results demonstrate that risk is highly contextual and not an intrinsic tanker property. The analysis reveals that mid-sized tankers (20,000–35,000 GT) are most susceptible to technical failures like propulsion and auxiliary machinery breakdowns, aligning with their high risk for asset loss and security breaches. Conversely, larger tankers (> 60,000 GT) exhibit systematically lower risk across most categories, which is attributed to advanced safety systems and stricter protocols. A notable exception is environmental risk, where smaller tankers (≤ 5000 GT) pose the lowest threat due to their limited spillage potential. The simulation achieved convergence at 10,000 iterations for personnel injury and security breach incidents, and 5000 for asset loss and environmental impacts, providing a validated threshold for reliable prediction. This study concludes that the Monte Carlo method effectively translates historical data into actionable insights, enabling proactive, precisely timed mitigations tailored to specific tanker profiles and incident types. The findings offer a paradigm shift from reactive to predictive risk management in maritime operations.