Skip to main content
SpringerLink
Log in

Ripple Effect in the Supply Chain: Definitions, Frameworks and Future Research Perspectives

  • Chapter
  • First Online:
Handbook of Ripple Effects in the Supply Chain

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 276))

Abstract

This chapter aims at delineating major features of the ripple effect and methodologies to mitigate the supply chain disruptions and recover in case of severe disruptions. It observes the reasons and mitigation strategies for the ripple effect in the supply chain and presents the ripple effect control framework that is comprised of redundancy, flexibility and resilience. Even though a variety of valuable insights has been developed in the given area in recent years, new research avenues and ripple effect taxonomies are identified for the near future. Two special directions are highlighted. The first direction is the supply chain risk analytics for disruption risks and the data-driven ripple effect control in supply chains. The second direction is the concept of low-certainty-need (LCN) supply chains.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
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

  • Akkermans, H., & van Wassenhove, L. N. (2018). Supply chain tsunamis: Research on low probability high impact disruptions. Journal of Supply Chain Management, 54(1), 64–76.

    Article  Google Scholar 

  • Aven, T. (2017). How some types of risk assessments can support resilience analysis and management. Reliability Engineering and System Safety, 167, 536–543.

    Article  Google Scholar 

  • Camarinha-Matos, L. M., & Macedo, P. (2010). A conceptual model of value systems in collaborative networks. Journal of Intelligent Manufacturing, 21(3), 287–299.

    Article  Google Scholar 

  • Chopra, S., & Sodhi, M. S. (2004). Managing risk to avoid supply-chain breakdown. MIT Sloan Management Review, 46, 52–61.

    Google Scholar 

  • Chopra, S., & Sodhi, M. S. (2014). Reducing the risk of supply chain disruptions. MIT Sloan Management Review, 55(3), 73–80.

    Google Scholar 

  • DHL. (2018). Retrieved February 4, 2018 from https://resilience360.com/.

  • Dolgui, A., Ivanov, D., Rozhkov, M. (2019). Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain. International Journal of Production Research, in press.

    Google Scholar 

  • 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. Invited Special Issue 55th Volume Anniversary of IJPR.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gurnani, H., Mehrotra, A., & Ray, S. (2012). Supply chain disruptions: Theory and practice of managing risk. London: Springer.

    Book  Google Scholar 

  • Han, J., & Shin, K. S. (2016). Evaluation mechanism for structural robustness of supply chain considering disruption propagation. International Journal of Production Research, 54(1), 135–151.

    Article  Google Scholar 

  • Handfield, R. B., & McCormack, K. (2008). Supply chain risk management: Minimizing disruptions in global sourcing. Auerbach Publications.

    Google Scholar 

  • He, J., Alavifard, F., Ivanov, D., Jahani, H. (2018). A real-option approach to mitigate disruption risk in the supply chain. Omega. https://doi.org/10.1016/j.omega.2018.08.008.

  • Heckmann, I. (2016). Towards supply chain risk analytics. Wiesbaden: Springer-Gabler.

    Book  Google Scholar 

  • Hendricks, K. B., & Singhal, V. R. (2005). Association between supply chain glitches and operating performance. Management Science, 51(5), 695–711.

    Article  Google Scholar 

  • Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: A literature review. International Journal of Production Research, 53(16), 5031–5069.

    Article  Google Scholar 

  • Hosseini S., Ivanov D., Dolgui A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research: Part E, https://doi.org/10.1016/j.tre.2019.03.001.

    Article  Google Scholar 

  • IBM. (2018). Retrieved February 11, 2018 from https://www.youtube.com/watch?v=tdhpYQCWnCw&t=52s.

  • Ivanov, D. (2010). A framework for aligning (re)planning decisions on supply chains strategy, design, tactics, and operations. International Journal of Production Research, 48(13), 3999–4017.

    Article  Google Scholar 

  • Ivanov, D. (2017). Simulation-based ripple effect modelling in the supply chain. International Journal of Production Research, 55(7), 2083–2101.

    Article  Google Scholar 

  • Ivanov, D. (2018a). Revealing interfaces of supply chain resilience and sustainability: A simulation study. International Journal of Production Research, 56(10), 3507–3523.

    Article  Google Scholar 

  • Ivanov, D. (2018b). Structural dynamics and resilience in supply chain risk management. New York: Springer.

    Book  Google Scholar 

  • Ivanov, D. (2019) Disruption tails and revival policies: A simulation analysis of supply chain design and production-ordering systems in the recovery and post-disruption periods. Computers and Industrial Engineering, 127, 558–570.

    Article  Google Scholar 

  • Ivanov, D., & Dolgui, A. (2018). Low-Certainty-Need (LCN) Supply chains: A new perspective in managing disruption risks and resilience. International Journal of Production Research, in press.

    Google Scholar 

  • Ivanov, D., & Rozhkov, M. (2017). Coordination of production and ordering policies under capacity disruption and product write-off risk: An analytical study with real-data based simulations of a fast moving consumer goods company. Annals of Operations Research, published online.

    Google Scholar 

  • Ivanov, D., & Sokolov, B. (2010). Adaptive supply chain management. London: Springer.

    Book  Google Scholar 

  • Ivanov, D., & Sokolov, B. (2012a). The inter-disciplinary modelling of supply chains in the context of collaborative multi-structural cyber-physical networks. Journal of Manufacturing Technology Management, 23(8), 976–997.

    Article  Google Scholar 

  • Ivanov, D., & Sokolov, B. (2012b). ‘Structure dynamics control-based service scheduling in collaborative cyber-physical supply networks. In L. Camarinha-Matos, L. Xu, & H. Afsarmanesh (Eds.), Proceedings of the IFIP Conference on Virtual Enterprises PRO-VE 2012 IFIP AICT (Vol. 380, pp. 280–288).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ivanov, D., Arkhipov, A., & Sokolov, B. (2004). Intelligent supply chain planning in virtual enterprises. In: L. Camarihna-Matos (Ed.), Virtual Enterprises and Collaborative Networks, Proceedings of the IFIP Conference on Virtual Enterprises PRO-VE 2004 (pp. 215–223). Kluwer Academic Publishers.

    Google Scholar 

  • Ivanov, D., Sokolov, B., & Kaeschel, J. (2010). A multi-structural framework for adaptive supply chain planning and operations with structure dynamics considerations. European Journal of Operational Research, 200, 409–420.

    Article  Google Scholar 

  • Ivanov, D., Sokolov, B., & Pavlov, A. (2014a). Optimal distribution (re)planning in a centralized multi-stage network under conditions of ripple effect and structure dynamics. European Journal of Operational Research, 237(2), 758–770.

    Article  Google Scholar 

  • Ivanov, D., Sokolov, B., & Dolgui, A. (2014b). The ripple effect in supply chains: Trade-off ‘efficiency-flexibility-resilience’ in disruption management. International Journal of Production Research, 52(7), 2154–2172.

    Article  Google Scholar 

  • Ivanov, D., Sokolov, B., & Dilou Raguinia, E. A. (2014c). Integrated dynamic scheduling of material flows and distributed information services in collaborative cyber-physical supply networks. International Journal of Systems Science: Operations & Logistics, 1(1), 18–26.

    Google Scholar 

  • Ivanov, D., Hartl, R., Dolgui, A., Pavlov, A., & Sokolov, B. (2015). Integration of aggregate distribution and dynamic transportation planning in a supply chain with capacity disruption and the ripple effect consideration. International Journal of Production Research, 53(23), 6963–6979.

    Article  Google Scholar 

  • Ivanov, D., Sokolov, B., Pavlov, A., Dolgui, A., & Pavlov, D. (2016). Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies. Transportation Research Part E, 90, 7–24.

    Article  Google Scholar 

  • Ivanov, D., Pavlov, A., Pavlov, D., & Sokolov, B. (2017a). Minimization of disruption-related return flows in the supply chain. International Journal of Production Economics, 183, 503–513.

    Article  Google Scholar 

  • Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2017b). Literature review on disruption recovery in the supply chain. International Journal of Production Research, 55(20), 6158–6174.

    Article  Google Scholar 

  • Ivanov, D., Dolgui, A., Ivanova, M., & Sokolov, B. (2018) Simulation vs. optimization approaches to ripple effect modelling in the supply chain. In M. Freitag, H. Kotzab, & J. Pannek (Eds.), Dynamics in Logistics. LDIC 2018, Bremen 20–22, 2018. Lecture Notes in Logistics (pp. 34–39). Springer, Cham.

    Google Scholar 

  • Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846.

    Google Scholar 

  • Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2019). Global supply chain and operations management (2nd ed.). Cham: Springer.

    Google Scholar 

  • Khojasteh, Y. (Ed.). (2017). Supply chain risk management. Springer Singapore.

    Google Scholar 

  • Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating supply chain networks: A critical review. European Journal of Operational Research, 203(2), 283–293.

    Article  Google Scholar 

  • Kouvelis, P., Dong, L., Boyabatli, O., & Li, R. (2012). Handbook of integrated risk management in global supply chains. Hoboken, NJ: Wiley.

    Google Scholar 

  • KPMG. (2017). Supply chain big data series. Retrieved February 17, 2018 from https://assets.kpmg.com/content/dam/kpmg/au/pdf/2017/big-data-shaping-supply-chains-of-tomorrow.pdf.

  • Levner, E., & Ptuskin, A. (2018). Entropy-based model for the ripple effect: Managing environmental risks in supply chains. International Journal of Production Research, 56(7), 2539–2551.

    Article  Google Scholar 

  • Liberatore, F., Scaparra, M. P., & Daskin, M. S. (2012). Hedging against disruptions with ripple effects in location analysis. Omega, 40, 21–30.

    Article  Google Scholar 

  • Macdonald, J. R., Zobel, C. W., Melnyk, S. A., & Griffis, S. E. (2018). Supply chain risk and resilience: Theory building through structured experiments and simulation. International Journal of Production Research. https://doi.org/10.1080/00207543.2017.1421787.

    Article  Google Scholar 

  • Mizgier, K. J. (2017). Global sensitivity analysis and aggregation of risk in multi-product supply chain networks. International Journal of Production Research, 55(1), 130–144.

    Article  Google Scholar 

  • Pavlov, A., Ivanov, D., Dolgui, A., & Sokolov, B. (2018). Hybrid fuzzy-probabilistic approach to supply chain resilience assessment. IEEE Transactions on Engineering Management, 65(2), 303–315.

    Article  Google Scholar 

  • Pavlov, A., Ivanov, D., Pavlov, D., Slinko, A. (2019). Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03182-6.

  • Quang, H. T., & Hara, Y. (2018). Risks and performance in supply chain: The push effect. International Journal of Production Research, 56(4), 1369–1388.

    Google Scholar 

  • RM. (2018). Retrieved February 10, 2018 from https://www.riskmethods.net/en/software/overview.

  • Sawik, T. (2016). On the risk-averse optimization of service level in a supply chain under disruption risks. International Journal of Production Research, 54(1), 98–113.

    Article  Google Scholar 

  • Sawik, T. (2018). Supply chain disruption management using stochastic mixed integer programming. Cham: Springer.

    Book  Google Scholar 

  • Scheibe, K. P., & Blackhurst, J. (2018). Supply chain disruption propagation: A systemic risk and normal accident theory perspective. International Journal of Production Research, 56(1–2), 43–59.

    Article  Google Scholar 

  • Schmitt, T. G., Kumar, S., Stecke, K. E., Glover, F. W., & Ehlen, M. A. (2017). Mitigating disruptions in a multi-echelon supply chain using adaptive ordering. Omega, 68, 185–198.

    Article  Google Scholar 

  • Simangunsong, E., Hendry, L. C., & Stevenson, M. (2012). Supply-chain uncertainty: A review and theoretical foundation for future research. International Journal of Production Research, 50(16), 4493–4523.

    Article  Google Scholar 

  • Simchi-Levi, D., Schmidt, W., & Wei, Y. (2014 February). From superstorms to factory fires: Managing unpredictable supply chain disruptions. Harvard Business Review.

    Google Scholar 

  • Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P. Y., Combs, K., Ge, Y., et al. (2015). Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces, 45(5), 375–390.

    Article  Google Scholar 

  • Snyder, L. V., Atan, Z., Peng, P., Rong, Y., Schmitt, A. J., & Sinsoysal, B. (2016). OR/MS models for supply chain disruptions: A review. IIE Transactions, 48(2), 89–109.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Tang, O., & Musa, S. N. (2011). Identifying risk issues and research advancements in supply chain risk management. International Journal of Production Economics, 133, 25–34.

    Article  Google Scholar 

  • Teich, T. (2003). Extended value chain management (EVCM). Chemnitz: GUC-Verlag.

    Google Scholar 

  • Teich, T., & Ivanov, D. (2012). Integrated customer-oriented product design and process networking of supply chains in virtual environments. International Journal of Networking and Virtual Organizations, 11(1), 48–61.

    Article  Google Scholar 

  • Tomlin, B. (2006). On the value of mitigation and contingency strategies for managing supply chain disruption risks. Management Science, 52, 639–657.

    Article  Google Scholar 

  • UPS. (2018). Retrieved February 11, 2018 from https://www.youtube.com/watch?v=aYoNd2nQqLg.

  • Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34, 77–84.

    Article  Google Scholar 

  • Wang, Y., & Zhang, F. (2018). Modeling and analysis of under-load-based cascading failures in supply chain networks. Nonlinear Dynamics. https://doi.org/10.1007/s11071-018-4135-z.

    Article  Google Scholar 

  • Waters, D. (2011). Supply chain risk management: Vulnerability and resilience in logistics (2nd ed.). Kohan Page.

    Google Scholar 

  • Wilkinson, G. (2018). Integrating artificial intelligence with simulation modeling. Retrieved February 11, 2018 from https://www.anylogic.com/blog/.

  • Yang, Y., et al. (2017). Mitigating supply chain disruptions through interconnected logistics services in the physical internet. International Journal of Production Research, 55(14), 3970–3983.

    Article  Google Scholar 

  • Zhuge, H. (2011). Semantic linking through spaces for cyber-physical-socio intelligence: A methodology. Artificial Intelligence, 175(5–6), 988–1019.

    Article  Google Scholar 

Download references

Acknowledgements

This research was partially supported by the grant of the Russian Science Foundation project No. 17-11-01254

Author information

Authors and Affiliations

  1. Berlin School of Economics and Law, Department of Business and Economics, 10825, Berlin, Germany

    Dmitry Ivanov

  2. IMT Atlantique, LS2N, CNRS, La Chantrerie, 4, rue Alfred Kastler, 44300, Nantes, France

    Alexandre Dolgui

  3. Saint Petersburg Institute for Informatics and Automation of the RAS (SPIIRAS), V.O. 14 line 39, 199178, St. Petersburg, Russia

    Boris Sokolov

Authors
  1. Dmitry Ivanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexandre Dolgui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Boris Sokolov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dmitry Ivanov .

Editor information

Editors and Affiliations

  1. Department of Business and Economics, Berlin School of Economics and Law, Berlin, Germany

    Dmitry Ivanov

  2. Department of Automation, Production and Computer Sciences, IMT Atlantique, LS2N - CNRS UMR, Nantes, France

    Alexandre Dolgui

  3. SPIIRAS, St. Petersburg, Russia

    Boris Sokolov

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ivanov, D., Dolgui, A., Sokolov, B. (2019). Ripple Effect in the Supply Chain: Definitions, Frameworks and Future Research Perspectives. In: Ivanov, D., Dolgui, A., Sokolov, B. (eds) Handbook of Ripple Effects in the Supply Chain. International Series in Operations Research & Management Science, vol 276. Springer, Cham. https://doi.org/10.1007/978-3-030-14302-2_1

Download citation

Publish with us

Policies and ethics

Search

Navigation