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 and supply chain operational disruption, due to harsh environmental factors with their associated risks. To capture these stochastic influential factors and support related decision making, it is helpful to develop a robust and dynamic probabilistic model.

The current study presents a proactive methodology that integrates the Pure-Birth Markovian process (PBMP) with the Bayesian network (BN) for the effective analysis of offshore logistics disruption risk. The PBMP captures the stochasticity in the failure characteristics of the engineering systems for estimating the time-evolution degradation probability. The BN explores the dynamic interactions among the most important offshore logistics influential factors to analyze the disruption risk in a harsh environment. The effects of influential factors’ non-linear dependencies are propagated and updated, given evidence on the degree of disruption. The level of logistics disruption is further assessed using cost aggregation-based expectation theory. The theory explores the incurred cost/economic risk under different operational scenarios. The proposed methodology is tested on an offshore supply vessel operation to estimate the likely operational disruption risk in terms of financial loss in a harsh operating environment. The most critical influential functions are assessed to establish their degree of impact on the logistics disruption. At the upper bound probability of disruption occurrence, an economic risk/additional incurred cost of US$2.38E+05 with a variance (𝜎²) of 3.05 × 10⁹ was predicted. The result obtained suggests that the proposed methodology is adaptive and effective for dynamic logistics disruption risk analysis in harsh offshore environments.