Advertisement

New Measures of Vulnerability Within Supply Networks: A Comparison of Industries

  • James P. MinasEmail author
  • N. C. Simpson
  • Ta-Wei (Daniel) Kao
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 276)

Abstract

Modern supply chains have become increasingly complex and interconnected, raising concerns as to the potential loss of system-wide resilience. One distinct element of supply chain risk is the potential for detrimental material to propagate through the supply chain undetected, eventually exposing unsuspecting consumers to defective products. In this chapter, based on methods inspired by epidemiology, we propose new measures for quantifying this risk. We then apply these measures to real-life supply networks from eight industries to compare their relative levels of risk across a 17-year time horizon. Our results indicate that while in aggregate supply chain risk has increased overtime, both the level and sources of risk differ markedly by industry.

References

  1. Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 425–455.CrossRefGoogle Scholar
  2. Basole, R. C., & Bellamy, M. A. (2014a). Visual analysis of supply network risks: Insights from the electronics industry. Decision Support Systems, 67, 109–120.CrossRefGoogle Scholar
  3. Basole, R. C., & Bellamy, M. A. (2014b). Supply network structure, visibility, and risk diffusion: A computational approach. Decision Sciences, 45(4), 753–789.CrossRefGoogle Scholar
  4. Basole, R. C., Bellamy, M. A., Park, H., & Putrevu, J. (2016). Computational analysis and visualization of global supply network risks. IEEE Transactions on Industrial Informatics, 12(3), 1206–1213.CrossRefGoogle Scholar
  5. Battiston, S., Gatti, D. D., Gallegati, M., Greenwald, B., & Stiglitz, J. E. (2007). Credit chains and bankruptcy propagation in production networks. Journal of Economic Dynamics and Control, 31(6), 2061–2084.CrossRefGoogle Scholar
  6. Bellamy, M. A., Ghosh, S., & Hora, M. (2014). The influence of supply network structure on firm innovation. Journal of Operations Management, 32(6), 357–373.CrossRefGoogle Scholar
  7. Carnovale, S., & Yeniyurt, S. (2014). The role of ego networks in manufacturing joint venture formations. Journal of Supply Chain Management, 50(2), 1–17.CrossRefGoogle Scholar
  8. Carnovale, S., & Yeniyurt, S. (2015). The role of ego network structure in facilitating ego network innovations. Journal of Supply Chain Management, 51(2), 22–46.CrossRefGoogle Scholar
  9. Choi, T. Y., Dooley, K. J., & Rungtusanatham, M. (2001). Supply networks and complex adaptive systems: Control versus emergence. Journal of Operations Management, 19(3), 351–366.CrossRefGoogle Scholar
  10. Choi, T., & Hong, Y. (2002). Unveiling the structure of supply networks: Case studies in Honda, Acura, and DaimlerChrysler. Journal of Operations Management, 20, 469–493.CrossRefGoogle Scholar
  11. De Stefano, M. C., & Montes-Sancho, M. J. (2018). Supply chain environmental R&D cooperation and product performance: Exploring the network dynamics of positional embeddedness. Journal of Purchasing and Supply Management.  https://doi.org/10.1016/j.pursup.2018.10.003.CrossRefGoogle Scholar
  12. Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: An analysis and recent literature. International Journal of Production Research, 56(1-2), 414–430.CrossRefGoogle Scholar
  13. Dong, M., Liu, Z., Yu, Y., & Zheng, J. (2015). Opportunism in distribution networks: The role of network embeddedness and dependence. Production and Operations Management, 24(10), 1657–1670.CrossRefGoogle Scholar
  14. Gai, P., & Kapadia, S. (2010). Contagion in financial networks. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, p. rspa20090410.Google Scholar
  15. Grewal, R., Lilien, G. L., & Mallapragada, G. (2006). Location, location, location: how network embeddedness affects project success in open source systems. Management Science, 52(7), 1043–1056.CrossRefGoogle Scholar
  16. Hethcote, H. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), 599–653.CrossRefGoogle Scholar
  17. Kermack, W., & McKendrick, A. (1927). Contributions to the mathematical theory of epidemics. In Proceedings of the Royal Society London Ser. A, Vol. 115(772), pp. 700–721.Google Scholar
  18. Kim, Y., Chen, Y.-S., & Linderman, K. (2015). Supply network disruption and resilience: A network structural perspective. Journal of Operations Management, 33–34, 43–59.CrossRefGoogle Scholar
  19. Kim, Y. H. (2017). The effects of major customer networks on supplier profitability. Journal of Supply Chain Management, 53(1), 26–40.CrossRefGoogle Scholar
  20. Mazzola, E., Perrone, G., & Kamuriwo, D. S. (2015). Network embeddedness and new product development in the biopharmaceutical industry: The moderating role of open innovation flow. International Journal of Production Economics, 160, 106–119.CrossRefGoogle Scholar
  21. Miller, R. (1962). How to plan and control with PERT. Harvard Business Review, 40(2), 93–104.Google Scholar
  22. Pathak, S. D., Day, J. M., Nair, A., Sawaya, W. J., & Kristal, M. M. (2007). Complexity and adaptivity in supply networks: Building supply network theory using a complex adaptive systems perspective*. Decision Sciences, 38(4), 547–580.CrossRefGoogle Scholar
  23. Phelps, C. C. (2010). A longitudinal study of the influence of alliance network structure and composition on firm exploratory innovation. Academy of Management Journal, 53(4), 890–913.CrossRefGoogle Scholar
  24. Sokolov, B., Ivanov, D., Dolgui, A., & Pavlov, A. (2016). Structural quantification of the ripple effect in the supply chain. International Journal of Production Research, 54(1), 152–169.CrossRefGoogle Scholar
  25. Yoo, E., Rand, W., Eftekhar, M., & Rabinovich, E. (2016). Evaluating information diffusion speed and its determinants in social media networks during humanitarian crises. Journal of Operations Management, 45, 123–133.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • James P. Minas
    • 1
    Email author
  • N. C. Simpson
    • 2
  • Ta-Wei (Daniel) Kao
    • 3
  1. 1.Ithaca CollegeIthacaUSA
  2. 2.University at Buffalo (SUNY)BuffaloUSA
  3. 3.University of Michigan-DearbornDearbornUSA

Personalised recommendations