In the present era, the spread of cyber-physical systems in the framework of the so-called Industry 4.0, is leading towards a complete automation of industrial processes, which are increasingly decentralized, smart, and require fewer and fewer frontline personnel. The risk assessment process is certainly not excluded from the revolution, and in perspective needs to be automatic, dynamic and linked with the conditions that emerge, moment by moment, in the life of a complex system. Analytical techniques can help in converting data in information and hence system knowledge to spot trends in operational performance, thus improving risk assessment quality. Even though the bow-tie approach is widely used within the context of complex systems, it still evidences several limitations, mainly connected to the actual assessment of likelihood and interdependencies in the fault and event trees. This paper shows how a bow tie analysis can be reframed as a Hierarchical Bayesian Network, where the probability distributions of the network nodes are updated with real time predictions during the operations. The proposed model was then applied to the risk assessment of a shore-to-ship LNG bunkering operation.