Advertisement

Assessing Supply Chain Resilience upon Critical Infrastructure Disruptions: A Multilevel Simulation Modelling Approach

  • Paolo Trucco
  • Boris Petrenj
  • Seyoum Eshetu BirkieEmail author
Chapter

Abstract

Supply chain risk management (SCRM) approaches suggest that actors in a supply chain network should consider different risk scenarios to address and mitigate supply chain risks in a better way. Overall performance of a supply chain could be severely affected by disruptions that are triggered by failures or service disruptions in the critical infrastructure (CI) systems that the supply chain relies on. Interdependencies among the CI systems and supply chains, particularly the so-called Key Resources Supply Chains (KRSC) such as food, worsen the effects as disruption and consequences propagate in the network. In order to understand such interdependencies and enhance SCRM approaches with a more holistic view, this chapter introduces a multilevel modelling approach. The economic loss impact of disruptions in CI systems and the potential effectiveness of different strategies to improve resilience in KRSC are modelled and assessed. A combination of Discrete Event Simulation and System Dynamics is used at the different levels of the simulation model. The proposed approach is demonstrated with an application case addressing the vulnerability and resilience analysis of a fast moving consumer goods supply chain against disruptions in underlying CI systems. Results of the multilevel simulation offered relevant insights toward a better understanding of the strength and dynamics of the interdependence between KRSC and CI, and consequently on resilience improvement efforts. Results help supply chain managers to prioritise resilience strategies, according to their expected benefits, when making decisions on the amount and location of resilience capabilities within a supply chain. The results strongly support the implementation of collaborative and coordinated resilience strategies among supply chain actors to direct efforts where they can be most effective.

References

  1. Birkie, S. E., Trucco, P., & Kaulio, M. (2014). Disentangling core functions of operational resilience: A critical review of extant literature. International Journal of Supply Chain and Operations Resilience, 1(1), 76–103.CrossRefGoogle Scholar
  2. Brailsford, S., Churilov, L., & Dangerfield, B. (Eds.). (2014). Discrete-event simulation and system dynamics for management decision making. New York: Wiley.Google Scholar
  3. BSI (British Standards Institute). (2015). Security risk index. London: BSI Supply Chain Solutions. 2015.Google Scholar
  4. Clarke, L., & MacDonald, A. (2014). Copper ends slightly higher after jolt from Chile earthquake, Wall Street Journal, 2 April. Available at: http://www.wsj.com/articles/SB10001424052702304157204579476440226661758
  5. Conrad, S. H., LeClaire, R. J., O’Reilly, G. P., & Uzunalioglu, H. (2006). Critical national infrastructure reliability modeling and analysis. Bell Labs Technical Journal, 11(3), 57–71.CrossRefGoogle Scholar
  6. Craighead, C. W., Blackhurst, J., Rungtusanatham, M. J., & Handfield, R. B. (2007). The severity of supply chain disruptions: Design characteristics and mitigation capabilities. Decision Sciences, 38(1), 131–156.CrossRefGoogle Scholar
  7. Fahimnia, B., Tang, C. S., Davarzani, H., & Sarkis, J. (2015). Quantitative models for managing supply chain risks: A review. European Journal of Operational Research, 247(1), 1–15.CrossRefGoogle Scholar
  8. Hall, P. V. (2004). ‘We’d have to sink the ships’: Impact studies and the 2002 West Coast port lockout. Economic Development Quarterly, 18(4), 354–367.CrossRefGoogle Scholar
  9. INIS (Italian National Institute of Statistics). (2015). “I.Stat”, National Accounts Data. Available at: www.dati.istat.it
  10. Ivanov, D., & Sokolov, B. (2013). Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty. European Journal of Operational Research, 224(2), 313–323.Google Scholar
  11. Ivanov, D., Sokolov, B., & Dolgui, A. (2014). The Ripple effect in supply chains: Trade-off ‘efficiency-flexibility-resilience’ in disruption management. International Journal of Production Research, 52(7), 2154–2172.CrossRefGoogle Scholar
  12. Kamalahmadi, M., & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics, 171(1), 116–133.CrossRefGoogle Scholar
  13. Kim, Y., Chen, Y., & Linderman, K. (2015). Supply network disruption and resilience: A network structural perspective. Journal of Operations Management, 33–34, 43–59.CrossRefGoogle Scholar
  14. Kumar, S., & Nigmatullin, A. (2011). A system dynamics analysis of food supply chains—case study with non-perishable products. Simulation Modelling Practice and Theory, 19(10), 2151–2168.CrossRefGoogle Scholar
  15. MacKenzie, C. A., Santos, J. R., & Barker, K. (2012). Measuring changes in international production from a disruption: Case study of the Japanese earthquake and tsunami. International Journal of Production Economics, 138(2), 293–302.CrossRefGoogle Scholar
  16. McNally, R. K., Lee, S.-W., Yavagal, D., & Xiang, W.-N. (2007). Learning the critical infrastructure interdependencies through an ontology-based information system. Environment and Planning B: Planning and Design, 34(6), 1103–1124.CrossRefGoogle Scholar
  17. Ouyang, M. (2014). Review on modeling and simulation of interdependent critical infrastructure systems. Reliability Engineering and System Safety, 121, 43–60.CrossRefGoogle Scholar
  18. Owen, C., Albores, P., Greasley, A., & Love, D. (2010). Simulation in the supply chain context: Matching the simulation tool to the problem. In Proceedings of the 2010 Operational Research Society Simulation Workshop, pp. 229–242.Google Scholar
  19. Patterson, S. (2015). Water troubles in tiny Chilean town threaten global copper supply. Wall Street Journal, 26 November. Available at: http://www.wsj.com/articles/scarcity-of-water-poses-challenge-for-copper-miners-1448549196
  20. Pidd, M. (2003). Tools for thinking: Modelling in management science (2nd ed.). New York: Wiley.Google Scholar
  21. Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20(1), 124–143.CrossRefGoogle Scholar
  22. Rice, J. B., & Caniato, F. (2003). Building a secure and resilient supply network. Supply Chain Management Review, 7(5), 22–30.Google Scholar
  23. Rinaldi, S. M., Peerenboom, J. P., & Kelly, T. K. (2001). Identifying, understanding, and analyzing critical infrastructure interdependencies. IEEE Control Systems Magazine, 21(6), 11–25.CrossRefGoogle Scholar
  24. Santella, N., Steinberg, L. J., & Parks, K. (2009). Decision making for extreme events: Modeling critical infrastructure interdependencies to aid mitigation and response planning. Review of Policy Research, 26(4), 409–422.CrossRefGoogle Scholar
  25. Sheffi, Y. (2007). The resilient enterprise: Overcoming vulnerability for competitive advantage. Cambridge, MA: MIT Press.Google Scholar
  26. Sterman, J. (2000), Business dynamics: Systems thinking and modeling for a complex world. New York: McGraw-Hill.Google Scholar
  27. Tako, A. A., & Robinson, S. (2012). The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems, 52(4), 802–815.CrossRefGoogle Scholar
  28. Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics Research and Applications, 9(1), 33–45.CrossRefGoogle Scholar
  29. The Council of the European Union. (2008). Council Directive 2008/114/EC of 8 December 2008 on the indentification and designation of European critical infrastructures and the assessment of the need to improve their protection. Official Journal of the European Union, pp. 75–82.Google Scholar
  30. World Economic Forum. (2012). New models for addressing supply chain and transport risk. World Economic Forum: Industry Agenda.Google Scholar
  31. Wu, D., & Olson, D. L. (2008). Supply chain risk, simulation, and vendor selection. International Journal of Production Economics, 114(2), 646–655.CrossRefGoogle Scholar
  32. Wu, T., Huang, S., Blackhurst, J., Zhang, X., & Wang, S. (2013). Supply chain risk management: An agent-based simulation to study the impact of retail stockouts. IEEE Transactions on Engineering Management, 60(4), 676–686.CrossRefGoogle Scholar
  33. Yang, T., & Wu, J. (2007). The impact of transportation disruptions on performance of e-Collaboration supply chain. In L. Xu, A. Tjoa & S. Chaudhry (Eds.), IFIP International Federation for Information Processing, Research and Practical Issues of Enterprise Information Systems II Vol. I (Vol. 256, pp. 663–667). Boston: Springer.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Paolo Trucco
    • 1
  • Boris Petrenj
    • 1
  • Seyoum Eshetu Birkie
    • 1
    • 2
    Email author
  1. 1.School of ManagementPolitecnico di MilanoItaly
  2. 2.EiT-M, Mekelle UniversityMekelleEthiopia

Personalised recommendations