Modelling Ship Officer Performance Variability Using Functional Resonance Analysis Method and Dynamic Bayesian Network

Ship maneuvering is a complex operation with inherent uncertainties. To express this complexity in system performance during the navigation process, an analysis model has been developed using Functional Resonance Analysis Method (FRAM) and Dynamic Bayesian Network (DBN). The functional level of dynamic work onboard is assessed and modeled using FRAM qualitatively, in which a key function and the function’s potential coupling for specific instantiation are identified. Further analysis is done by integrating the FRAM analysis with DBN for quantification. The evolution of system performance over time is determined through changes in the probability of function’s mode, namely strategic, tactical opportunistic, and scrambled. The model presented in this study concerns the fluctuation of ship officer performance to overcome the obstacles during the encounter event. As a result, the integration of FRAM-DBN shows promising usability to evaluate human performance. The essence of human adaptive capacity is also highlighted through system resilience potency, that is, the potency to learn, respond, monitor, and anticipate. We also discuss how this finding contributes to enhance safety analysis, in specific, to provide explicit representation of the dynamic in human performance in ship navigation based on Safety-II idea.