Risk Assessment of Dynamic Positioning Operations: Modelling the Contribution of Human Factors

Abstract

Dynamic positioning (DP) systems are essential to maritime operations, as they ensure precise station keeping. Yet human error remains a major contributor to DP incidents, often interacting with technical failures and environmental conditions. This study proposes an adaptive probabilistic framework to characterise human-error contributions to DP risk and support targeted mitigation. We compare integrated Bayesian network (BN)/fuzzy analytic hierarchy process (AHP) and Bayesian network (BN)/Dempster–Shafer (D-S) theory to model causal relationships, aggregate uncertain expert judgements, and prioritise risk factors. Historical incident narratives, accident reports, and expert elicitation inform the model to analyse failure propagation and quantify factor contributions. In a representative DP case application, insufficient training, operator fatigue, and reduced situational awareness—together with software anomalies and adverse environmental loads—emerge as dominant contributors; BN backward analysis corroborates their diagnostic relevance. The approach yields actionable insights for risk reduction, including tailored training programmes, strengthened safety protocols, and integration of real-time monitoring. It provides an auditable, updateable basis for scenario-based training, software/maintenance assurance, and environment-aware operating envelopes, and is readily extendable as new evidence becomes available. Overall, the framework offers practical value for improving safety, operational continuity, and system resilience in DP operations.