Quantitative Robustness Index Design for Supply Chain Networks

  • Ming Dong
  • F. Frank Chen
Part of the Springer Series in Advanced Manufacturing book series (SSAM)


As an event-driven system, a supply chain network will face uncertainties inside the supply chain and also unexpected events outside the supply chain network such as contingency, disruption, and disaster. These uncertainties and unexpected events have negative impacts on the survival and performance of supply chain networks. As a dynamic system, a supply chain network will evolve over time. Nodes and links may be added and deleted, or part of networks can be disconnected. The functional performance of nodes and links of supply chain networks may deteriorate quickly under unexpected events. Providing resilient capability against failures is a critical issue for supply chain networks since a single failure may propagate along the chain and cause a series of severe losses in network performance and structure. Robustness of a supply chain network is an important research issue, a vulnerable supply chain network may not be able to operate at all. The goal of this chapter is to investigate the relationship between robustness metrics and basic network parameters. A systemwide approach is presented to quantifying the robustness of supply chain networks. This approach considers both network structural and network functional parameters. Metagraphs are employed to calculate the structural robustness of nodes, and a topological index is used to capture the robustness of the overall network structure. Finally, the integrated systemwide network robustness index can be obtained by the proposed algorithm. Several hypothetical supply chain networks are employed to demonstrate the proposed approach.


Supply Chain Adjacency Matrix Unexpected Event Topological Index Supply Chain Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

16.11 References

  1. Agrawal N, Nahmias S, (1997) Rationalization of the supplier base in the presence of yield uncertainty. Production and Operations Management 6:291–308CrossRefGoogle Scholar
  2. Anupindi R, Akella R, (1993) Diversification under supply uncertainty. Management Science 39:944–963zbMATHGoogle Scholar
  3. Basu A, Blanning RW, (1994) Metagraphs: A tool for modeling decision support systems. Management Science 40:1579–1600zbMATHGoogle Scholar
  4. Basu A, Blanning RW, (2000) A formal approach to workflow analysis. Information Systems Research 11:17–36CrossRefGoogle Scholar
  5. Basu A, Blanning RW, Avraham S, (1997) Metagraphs in hierarchical modeling. Management Science 43:623–639zbMATHCrossRefGoogle Scholar
  6. Bollapragada S, Morton TE, (1999) Myopic heuristics for the random yield problem. Operations Research 47:713–722zbMATHGoogle Scholar
  7. Burke GJ, Carrillo JE, Vakharia AJ, (2004) Sourcing decisions with stochastic supplier reliability and stochastic demand. Working Paper, Warrington College of Business Administration, University of FloridaGoogle Scholar
  8. Dong M, Chen FF, (2005) Performance modeling and analysis of integrated logistic chains: An analytic framework. European Journal of Operational Research 162:83–98zbMATHCrossRefMathSciNetGoogle Scholar
  9. Gaonkar R, Viswanadham N, (2003) Robust supply chain design: A strategic approach for exception-handling. Proceedings of the IEEE Robotics and Automation Conference 2:1762–1767Google Scholar
  10. Gaonkar R, Viswanadham N, (2004) A conceptual and analytical framework for the management of risk in supply chains. Proceedings of the IEEE Robotics and Automation Conference 3:2699–2704Google Scholar
  11. Hopp W, Spearman M, (2000) Factory Physics, McGraw-Hill/Irwin, New York, Second EditionGoogle Scholar
  12. Hosoya H, (1971) A newly proposed quantity characterizing the topological nature of structural isomers of saturated hydrocarbons. Bulletin of the Chemical Society of Japan 44:2332–2339CrossRefGoogle Scholar
  13. Pai R, Kallepalli V, Caudill R, Zhou M, (2003) Methods toward supply chain risk analysis. Proceedings of the IEEE Systems, Man and Cybernetics Conference 5:4560–4565Google Scholar
  14. Rodrigue JP, (2003) Graph theory: measures and indices. i, Date accessed 16/4/06Google Scholar
  15. Sakakibara H, Kajitani Y, Okada N, (2004) Road network robustness for avoiding functional isolation in disasters. Journal of Transportation Engineering 130:560–567CrossRefGoogle Scholar
  16. Sheffi Y, (2001) Supply chain management under the threat of international terrorism. The International Journal of Logistics Management 12:1–11CrossRefGoogle Scholar
  17. Souter G, (2000) Risks from supply chain also demand attention. Business Insurance 34:28–28Google Scholar
  18. Stauffer D, (2003) Supply-chain risk: Deal with it. Harvard Business SchoolGoogle Scholar
  19. Yu D, Luh PB, (2004) Achieving reliable delivery in supply chains: the control of uncertainties. Proceedings of the IEEE Robotics and Automation Conference 3:2693–2698Google Scholar

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • Ming Dong
    • 1
  • F. Frank Chen
    • 2
  1. 1.Department of Industrial Engineering and Management, School of Mechanical EngineeringShanghai Jiao Tong UniversityP.R. China
  2. 2.Grado Department of Industrial and Systems EngineeringVirginia Polytechnic Institute and State UniversityUSA

Personalised recommendations