Developing an Artificial Intelligence Framework to Assess Shipbuilding and Repair Sub-Tier Supply Chains Risk

Abstract The defense shipbuilding and repair industry is a labor-intensive sector that can be characterized by low-product volumes and high investments in which a large number of shared resources, technology, suppliers, and processes asynchronously converge into large construction projects. It is mainly organized by the execution of a complex combination of sequential and overlapping stages. While entities engaged in this large-scale endeavor are often knowledgeable about their first-tier suppliers, they usually do not have insight into the lower tiers suppliers. A sizable part of any supply chain disruption is attributable to instabilities in sub-tier suppliers. This research note conceptually delineates a framework that considers the elicitation of the existing associations between suppliers and sub-tier suppliers. This framework, Shipbuilding Risk Supply Chain (Ship-RISC), offers a simulation framework to leverage real-time and data using an Industry 4.0 approach to generate descriptive and prescriptive analytics based on the execution of simulation models that support risk management assessment and decision-making.