Open Access
Yang, Tao
Graduate Program:
Industrial Engineering
Doctor of Philosophy
Document Type:
Date of Defense:
December 07, 2006
Committee Members:
  • Arunachalam Ravindran, Committee Chair
  • M Jeya Chandra, Committee Member
  • Vittaldas V Prabhu, Committee Member
  • Susan Xu, Committee Member
  • Supply Chain Management
  • Risk Management
  • Supply Risk
  • Quantification
  • Optimization
  • distribution
In today’s highly competitive market, firms have to focus on their core competencies to achieve success. While enjoying the benefits of building a highly integrated supply chain, firms also expose themselves to uncertain events, which are generally called as “risks.” Considering the importance of suppliers in a supply chain, supply risk has to be managed. This thesis first reviews supplier selection criteria and methods. Then, risks in a supply chain are discussed in detail with a summary of current research and the application of risk management in a supply chain. After defining the research questions, this thesis proposes a quantification method for supply risks. A five-step strategic supplier selection model considering risks and an optimization model for inventory, production, and transportation considering risks are developed thereafter. The major contributions of this thesis are: * Developed a supply risk quantification method. In order to quantify supply risks, the two components of risk, hazard and exposure, are first quantified separately and then combined either analytically or by scenario analysis. Firms can use their own historical data to build the hazard functions and the exposure functions or use the format and distributions recommended in this thesis. * Developed a five-step multi-criteria strategic supplier selection method. Weight range with three parameters is used to determine the best possible ranking for each candidate supplier as opposed to the single weight used in the traditional AHP method. Then, different types of multi-criteria optimization models are used to present decision makers with alternatives from which to choose. * Expanded the MTOM model from the author’s Master’s thesis to a multi-criteria optimization model considering risks. The model can handle multiple suppliers, manufacturers, retailers, components, products, transportation options, and production lines and can optimize inventory, transportation, and production simultaneously considering risks and total cost.