Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters

In harsh environments, offshore oil and gas support operations are subjected to frequent logistics supply chain operational disruption, due environmental factors with their associated risks. To capture these stochastic influential related decision making, it is helpful develop a robust dynamic probabilistic model. The current study presents proactive methodology that integrates the Pure-Birth Markovian process (PBMP) Bayesian network (BN) for effective analysis of disruption risk. PBMP captures stochasticity in failure characteristics engineering systems estimating time-evolution degradation probability. BN explores interactions among most important analyze risk environment. effects factors’ non-linear dependencies propagated updated, given evidence on degree disruption. level further assessed using cost aggregation-based expectation theory. theory incurred cost/economic under different scenarios. proposed tested an vessel operation estimate likely terms financial loss operating critical functions establish impact At upper bound probability occurrence, economic risk/additional US$2.38E+05 variance (𝜎²) 3.05 × 10? was predicted. result obtained suggests adaptive environments.