Document Type

Article

Publication Date

2021

DOI

10.1016/j.procs.2021.01.363

Publication Title

Procedia Computer Science

Volume

180

Pages

996–1002

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.

Rights

© 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.

ORCID

0000-0002-8637-5967 (Diaz)

Download

Share

COinS