Procedia Computer Science
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.
© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the International Conference on Industry 4.0 and Smart Manufacturing
Original Publication Citation
Diaz, R., Smith, K., Acero, B., Longo, F., & Padovano, A. (2021). Developing an artificial intelligence framework to assess shipbuilding and repair sub-tier supply chains risk. Procedia Computer Science, 180, 996-1002.
Diaz, Rafael; Smith, Katherine; Acero, Beatriz; Longo, Francesco; and Padovano, Antonio, "Developing an Artificial Intelligence Framework to Assess Shipbuilding and Repair Sub-Tier Supply Chains Risk" (2021). VMASC Publications. 78.