Document Type


Date of Degree

Spring 2010

Degree Name

PhD (Doctor of Philosophy)

Degree In

Industrial Engineering

First Advisor

Peter J. O'Grady

First Committee Member

Yong Chen

Second Committee Member

Andrew Kusiak

Third Committee Member

Pavlo Krokhmal

Fourth Committee Member

Gerald Schnoor


Supply chain management (SCM) is the oversight of materials, information, and finances as they move in a process from supplier to manufacturer to wholesaler to retailer to consumer. Supply chain management involves coordinating and integrating these flows both within and among companies as efficiently as possible. The supply chain consists of interconnected components that can be complex and dynamic in nature.

Therefore, an interruption in one subnetwork of the system may have an adverse effect on another subnetworks, which will result in a supply chain disruption. Disruptions from an event or series of events can have costly and widespread ramifications. When a disruption occurs, the speed at which the problem is discovered becomes critical. There is an urgent need for efficient monitoring of the supply chain. By examining the vulnerability of the supply chain network, supply chain managers will be able to mitigate risk and develop quick response strategies in order to reduce supply chain disruption. However, modeling these complex supply chain systems is a challenging research task.

This research is concerned with developing an extended Bayesian Network approach to analyze supply chain disruptions. The aim is to develop strategies that can reduce the adverse effects of the disruptions and hence improve overall system reliability.

The supply chain disruptions is modeled using Bayesian Networks-a method of modeling the cause of current and future events, which has the ability to model the large number of variables in a supply chain and has proven to be a powerful tool under conditions of uncertainty. Two impact factors are defined. These are the Bayesian Impact Factor (BIF) and the Node Failure Impact Factor (NFIF). An industrial example is used to illustrate the application proposed to make the supply chain more reliable.


Bayesian Network, Bayesian Network Learning, Neural Network, Supply Chain Disruption


xii, 122 pages


Includes bibliographical references (pages 110-122).


Copyright 2010 Ivy Elizabeth Donaldson Soberanis