Dissertation/Thesis Abstract

Examining Critical Material Supply Chains Through a Bayesian Network Model
by Kling, Joseph A., D.Engr., The George Washington University, 2018, 110; 10928265
Abstract (Summary)

The United States economic and national security sectors remain vulnerable to shortages of critical materials due to the risks posed by disruptions in globally-dispersed supply networks. Numerous methods over the past 10 years have been proposed to identify, assess, and evaluate risks in critical material supply chains. This praxis provides a method to quantify the impact of supply disruptions and inform the application of risk mitigation measures for a critical material supply chain from mineral deposits to final platform. It proposes a Bayesian network modeling method not yet applied to the problem in publicly available studies and fits with an assessment methodology proposed by the National Science and Technology Center (NSTC). Results from this study provide indicative answers to how supply disruptions propagate through a selected critical material supply network, which nodes are vulnerable to supply disruptions, and whether mitigating actions can reduce the impact of supply disruptions. The approach here demonstrates that a Bayesian network model can be one of the tools in a criticality assessment methodology.

Indexing (document details)
Advisor: Tozer, Bentz, Jarvandi, Ali
Commitee: Carroll, Adam, Etemadi, Amirhossein, Malalla, Ebrahim
School: The George Washington University
Department: Engineering Management
School Location: United States -- District of Columbia
Source: DAI-B 79/12(E), Dissertation Abstracts International
Subjects: Management, Engineering, Systems science
Keywords: Applied sciences, Bayesian networks, Critical materials, Engineering management, Supply chain risk management, Supply risk
Publication Number: 10928265
ISBN: 978-0-438-27118-0
Copyright © 2021 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy