Skip to main content

Supply Chain Risk Management in the Transmission and Amplification of Disruptions

  • Chapter
  • First Online:
Supply Chain Risk Management

Abstract

The concept of risk management within the supply chain framework ought to involve indirect effects of disruptions. In other words, not only should it take into consideration the risk sources and their direct consequences, but also look into the indirect disruptions that may be transmitted and amplified in the supply chain structure. The transmission of disruptions means that the negative effects of risk are extended to a larger number of participants in a supply chain. If the negative risk effects are additionally magnified during the transmission, this suggests the occurrence of the amplification of disruptions. In other words, the subsequent links in a supply chain are exposed to a stronger impact of disruptions in the transmission. Thus, the supply chain management needs to apply a certain approach that enables to mitigate the negative consequences of the transmission and amplification of disruptions in supply chains. In this chapter, we review the extant literature on the essence, sources and factors of the transmission and amplification of disruptions in supply chains. In particular, we put emphasis on the issue of supply chain integration that may either drive or inhibit the transmission and amplification of disruptions. Having linked the obtained findings with the classical concepts of risk management, we develop and assess a framework of risk management that aims at mitigating the transmission and amplification of disruptions in supply chains.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Aczel, A. D. (1993). Complete business statistics (2nd ed.). Massachusetts: Boston.

    Google Scholar 

  • Ainuddin, R., Beamish, P., Hulland, J., & Rouse, M. (2007). Resource attributes and firm performance in international joint ventures. Journal of World Business, 42(1), 47–60.

    Article  Google Scholar 

  • Aven, T., & Kristensen, V. (2005). Perspectives on risk: Review and discussion of the basis for establishing a unified and holistic approach. Reliability Engineering and System Safety, 90(1), 1–14.

    Article  Google Scholar 

  • Berg, E., Knudsen, D., & Norrman, A. (2008). Assessing performance of supply chain risk management programs: A tentative approach. International Journal of Risk Assessment and Management, 9(3), 288–310.

    Article  Google Scholar 

  • Billington, C., Johnson, B., & Triantis, A. (2002). A Real options perspective on supply chain management in high technology. Journal of Applied Corporate Finance, 15(2), 32–43.

    Article  Google Scholar 

  • Blackhurst, J., Craighead, C., Elkins, D., & Handfield, R. (2005). An empirically derived agenda of critical research issues for managing supply-chain disruptions. International Journal of Production Research, 43(19), 4067–4081.

    Article  Google Scholar 

  • Cheng, S., & Kam, B. (2008). A conceptual framework for analyzing risk in supply networks. Journal of Enterprise Information Management, 22(4), 345–360.

    Article  Google Scholar 

  • Chin, W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). NJ: Lawrence-Erlbaum, Mahwah.

    Google Scholar 

  • Christopher, M. (2002). Supply chain vulnerability. Cranfield: Research Report.

    Google Scholar 

  • Christopher, M., & Peck, H. (2004). The five principles of supply chain resilience. Logistics Europe, February, 16–21.

    Google Scholar 

  • Christou, M., & Amendale, A. (1998). How lessons learned from exercises can improve the quality of risk studies. In A. Mosleh & R. A. Bari (Eds.), Proceedings of the 4th International Conference on Probabilistic Safety Assessment and Management, New York, NY.

    Google Scholar 

  • Cohen, J., & Cohen, P. (1975). Applied multiple regression/correlation analysis for the behavioural science. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Davison, A., & Hinkley, D. (2003). Bootstrap methods and their application. New York, NY: Cambridge University Press.

    Google Scholar 

  • Ellegaard, Ch. (2008). Supply risk management in a small company perspective. International Journal of Supply Chain Management, 13(6), 425–434.

    Article  Google Scholar 

  • Fornell, C., & Bookstein, F. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440–452.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Gilbert, G., & Gips, M. (2000). Supply-side contingency planning. Security Management, 44(3), 70–74.

    Google Scholar 

  • Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis. Upper Saddle River, NJ: Pearson Prentice Hall.

    Google Scholar 

  • Hillson, D. (2002). The risk breakdown structure (RBS) as an aid to effective risk management. In: 5th European Project Management Conference. Cannes, France, 1–11.

    Google Scholar 

  • Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(4), 195–204.

    Article  Google Scholar 

  • Hult, G., Hurley, R., & Knight, G. (2004). Innovativeness: Its antecedents and impact on business performance. Industrial Marketing Management, 33(5), 429–438.

    Article  Google Scholar 

  • Jarvis, C., Mackenzie, S., Podsakoff, P., Mick, D., & Bearden, W. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199–218.

    Article  Google Scholar 

  • Juttner, U. (2005). Supply chain risk management. Understanding the business requirements from a practitioner perspective. International Journal of Logistics Management, 16(1), 120–141.

    Article  Google Scholar 

  • Juttner, U., Peck, H., & Christopher, M. (2003). Supply chain risk management: Outlining an agenda for future research. International Journal of Logistics: Research and Applications, 6(4), 197–210.

    Article  Google Scholar 

  • Kern, D., Moser, R., Hartmann, E., & Moder, M. (2012). Supply risk management: Model development and empirical analysis. International Journal of Physical Distribution & Logistics Management, 42(1), 60–82.

    Article  Google Scholar 

  • Kersten, W., Hohrath, P. H., & Böger, M. (2007). An empirical approach to supply chain risk management: Development of a strategic framework. In 18th Annual POMs Conference, Dallas, TX.

    Google Scholar 

  • Kersten, W., Schroeder, M., Skirde, H., & Feser, M. (2012). The development of supply chain risk management (SCRM) implementation model. In 23rd Annual POMs Conference, Chicago, IL.

    Google Scholar 

  • Khan, O., & Burns, B. (2007). Risk and supply chain management: Creating a research agenda. International Journal of Logistics Management, 18(2), 197–216.

    Article  Google Scholar 

  • Kleindorfer, P., & Saad, G. (2005). Disruption risk management in supply chains. Production and Operations Management, 14(1), 53–68.

    Article  Google Scholar 

  • McDonald, R. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31(2), 239–270.

    Article  Google Scholar 

  • Mentzer, J. (2004). Global supply chain risk management. White Paper: University of Tennesee. September 30.

    Google Scholar 

  • Nishiguchi, T., & Baaudet, A. (1998). The Toyota Group and the Aisin fire. Sloan Management Review, 49–59.

    Google Scholar 

  • Norrman, A., & Jansson, U. (2004). Ericsson’s proactive supply chain risk management approach after a serious sub-supplier accident. International Journal of Physical Distribution and Logistics Management, 34(5), 434–456.

    Article  Google Scholar 

  • Nunnally, J., & Bernstein, I. (1994). Psychometric theory. New York, NY: McGraw-Hill.

    Google Scholar 

  • O’Leary-Kelly, S., & Vokurka, R. (1998). The empirical assessment of construct validity. Journal of Operations Management, 16(4), 387–405.

    Article  Google Scholar 

  • Peck, H. (2004). Resilience—surviving the unthinkable. Logistics Manager, March, 16–18.

    Google Scholar 

  • Radjou, N., Orlov, L., & Nakashima, T. (2002). Adapting to supply network change. The TechStrategy TM Report, March.

    Google Scholar 

  • Rao, S., & Goldsby, T. J. (2009). Supply chain risk: A review and typology. Journal of Logistics Management, 20(1), 97–123.

    Article  Google Scholar 

  • Rice, J., & Caniato, F. (2003). Building a secure and resilient supply chain. Supply Chain Management Review, 7(5), 22–30.

    Google Scholar 

  • Sheehan, N. T. (2009). Making risk pay: The boards’ role. Journal of Business Strategy, 30(1), 33–39.

    Article  Google Scholar 

  • Smeltzer, L., & Siferd, S. (1998). Proactive supply management: The management of risk. International Journal of Purchasing and Material Management, 34(1), 38–45.

    Google Scholar 

  • Soler, M., & Bassetto, S. (2008). Analyse des risques de la chaine d’approvisionnements. Janvier.

    Google Scholar 

  • Spekman, R., & Davis, E. (2004). Risky business: Expanding the discussion on risk and the extended enterprise. International Journal of Physical Distribution and Logistics Management, 34(5), 414–433.

    Article  Google Scholar 

  • Stecke, K., & Kumar, S. (2009). Sources of supply chain disruptions, factors that breed vulnerability, and mitigating strategies. Journal of Marketing Channels, 16(3), 193–226.

    Article  Google Scholar 

  • Straub, D., Boudreau, M., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information Systems, 13(1), 380–427.

    Google Scholar 

  • Svensson, G. (2000). A conceptual framework for the analysis of vulnerability in supply chains. International Journal of Physical Distribution and Logistics Management, 30(9), 731–749.

    Article  Google Scholar 

  • Swierczek, A. (2012). Propagation of amplified disruptions in supply chains. Conceptual perspective and practical implications. In 23rd Annual POMS Conference, Chicago, IL.

    Google Scholar 

  • Swierczek, A. (2013). An identification of the ‘rippling effect’ in the transmission of disruptions. The dilemmas of theoretical study and empirical research. Journal of Economics & Management 12, 83–96.

    Google Scholar 

  • Swierczek, A. (2014). The impact of supply chain integration on the ‘snowball effect’ in the transmission of disruptions: An empirical evaluation of the model. International Journal of Production Economics, 157, 89–104.

    Article  Google Scholar 

  • Swierczek, A. (2016). The ‘snowball effect’ in the transmission of disruptions in supply chains: The role of intensity and span of integration. International Journal of Logistics Management, 27(3), 1002–1038.

    Google Scholar 

  • Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451–488.

    Article  Google Scholar 

  • Tenenhaus, M., Esposito, V., Chatelin, Y., & Lauro, C. (2005). PLS path modelling. Computational Statistics & Data Analysis, 48(1), 159–205.

    Article  Google Scholar 

  • Tsai, J., Bowring, E., Marsella, S., & Tambe, M. (2013). Empirical evaluation of computational fear contagion models in crowd dispersions. Autonomous Agents and Multi-Agent Systems, 27(2), 200–217.

    Article  Google Scholar 

  • Tsang, E. (2002). Acquiring knowledge by foreign partners from international joint ventures in a transition economy: Learning-by-doing and learning myopia. Strategic Management Journal, 23(9), 835–854.

    Article  Google Scholar 

  • van Dorp, J., & Duffey, M. (1999). Statistical dependence in risk analysis for project networks using Monte Carlo methods. International Journal of Production Economics, 58(1), 17–29.

    Article  Google Scholar 

  • van Dorp, J. (2004). Statistical dependence through common risk factors: With applications in uncertainty analysis. European Journal of Operations Research, 16(1), 240–255.

    Google Scholar 

  • Werts, C., Linn, R., & Joreskog, K. (1974). Intraclass reliability estimates: Testing structural assumptions. Educational and Psychological Measurement, 34(1), 25–33.

    Article  Google Scholar 

  • Zsidisin, G., & Ritchie, B. (Eds.). (2009). Supply chain risk. A handbook of assessment, management, and performance. New York: Springer Verlag.

    Google Scholar 

Download references

Acknowledgements

The study was financed by the National Science Centre as a research project no. DEC-2012/05/E/HS4/01598.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Artur Swierczek .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter

Swierczek, A. (2018). Supply Chain Risk Management in the Transmission and Amplification of Disruptions. In: Khojasteh, Y. (eds) Supply Chain Risk Management. Springer, Singapore. https://doi.org/10.1007/978-981-10-4106-8_10

Download citation

Publish with us

Policies and ethics