Lois, Robert
(2022)
Supply Chain Risk Assessment through Data-Driven Bayesian Networks.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
The supply chain is an integrated process of suppliers, plants, warehouses, and manufacturers all working together in an effort to procure raw materials, process the raw materials into final products, and deliver the final products to customers. However, the supply chain today has grown into a complex network, leading to vulnerabilities and an increase of uncertainty for decision makers. These vulnerabilities are defined as events with an associated likelihood to cause disruptions. With a limited amount of information on events occurring, the uncertainty decision makers encounter ultimately impedes the goals of the supply chain. These consequences are prevalent in low-volume, high-value supply chains such as the nuclear power generating industry.
The goal of this research is to reduce the uncertainty decision makers face in the nuclear power generating supply chain by developing a Bayesian network to monitor, plan, and control supply chain disruptions. The aim is to integrate models of event disruptions, resource availability, and mitigation options. Events that disrupt the flow of goods and information are identified through an ontological approach and are quantified with a likelihood of occurring through a general elicitation method. Resource availability of the nuclear power generating supply chain is modeled using control theory to simulate inventory data. The inventory data of upstream suppliers is estimated using Kalman filters and particle filters. The likelihood of events and the resource availability data are - integrated into a Bayesian network depicting the nuclear power plant supply network. Mitigation options are added to the Bayesian network to reduce the likelihood of events at a financial cost to deploy the option. Several scenarios are used to illustrate the application of the Bayesian network in terms of the supplier selection problem to demonstrate how uncertainty in decision making is
reduced.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
10 June 2022 |
| Date Type: |
Publication |
| Defense Date: |
1 April 2022 |
| Approval Date: |
10 June 2022 |
| Submission Date: |
12 April 2022 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
173 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Mechanical Engineering |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
machine learning, data-driven modeling, supply chain, decision making, probabilistic reasoning, risk assessment |
| Date Deposited: |
10 Jun 2022 19:43 |
| Last Modified: |
10 Jun 2022 19:43 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/42595 |
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