Modeling and assessing interdependencies between critical infrastructures using Bayesian network: A case study of inland waterway port and surrounding supply chain network

Abstract

The evolution of international supply chains have created a constant need for the transportation of goods through maritime transport. This mode of transport has been beneficial through its efficiency in the mobility of a large quantities of merchandise. In this context, inland waterway ports are one of the mainstays of the maritime transportation network. The aim of this paper is (1) to model and assess the interdependency between inland port infrastructure and its surrounding supply chain and (2) to show how the disturbance in one will have a ripple effect and trigger cascading failures in the entire network. To comprehend this interconnectedness, we outline three thorough interdependency types: geographic, service provision, and access for repair. After these interdependency types are validated, the factors related to port disruption and its supply chain performance are identified, and the Bayesian network is applied to visualize the interdependency among these factors. The quantification of interdependency is examined, and the results are profoundly analyzed through different advanced techniques such as belief propagation and sensitivity analysis. The general interpretation of these analyses entails that environmental factors and supplier responsiveness are imperative to port disruption and supply chain performance, respectively.