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Ripple Effect Analysis of Two-Stage Supply Chain Using Probabilistic Graphical Model

  • Seyedmohsen HosseiniEmail author
  • MD Sarder
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 276)

Abstract

Supply chain disruptions are increasingly caused by growing global supply sourcing, complexity, and interconnectedness of supply chains (SCs). A key challenge in the context of supply chain disruption management is to control and monitor the ripple effect of SCs. The ripple effect occurs when the impact of disruption cannot be localized and propagates throughout the SC. Like the bullwhip effect, the ripple effect can negatively impact performance both upstream and downstream of SC entities. This work proposes a new methodology, based on a probabilistic graphical model, to analyze the ripple effect in a two-stage SC. The probabilistic graphical model developed is capable of capturing disruption propagation that can transfer from upstream suppliers to downstream end customer in an SC.

Keywords

Ripple effect Supply chain Disruption Supply chain management 

References

  1. Behdani, B., & Srinivasan, R. (2017). Managing supply chain disruptions: an integrated agent-oriented approach. Computer Aided Chemical Engineering, 40, 595–600.CrossRefGoogle Scholar
  2. Bode, C., & Wagner, S. (2015). Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. Journal of Operations Management, 36, 215–228.CrossRefGoogle Scholar
  3. Cavalcantea, I.M., Frazzon E.M., Forcellinia, F.A., Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, forthcoming.Google Scholar
  4. Dolgui, A., Ivanov, D., & Sokolov, B. (2017). Ripple effect in the supply chain: an analysis and recent literature. International Journal of Production Research, 56(1–2), 414–430.Google Scholar
  5. Fenton, N., & Neil, M. (2013). Risk assessment and decision analysis with Bayesian networks. Boca Raton, FL: CRC Press, Taylor & Francis Group.Google Scholar
  6. Guo, J., & Gen, M. (2018). Optimal strategies for the closed-loop supply chain with the consideration of supply disruption and subsidy policy. Computers & Industrial Engineering.Google Scholar
  7. He, J., Alavifard, F., Ivanov, D., & Jahani, H. (2018). A real-option approach to mitigate disruption risk in the supply chain. Omega.Google Scholar
  8. Hosseini, S. (2016). Modeling and measuring resilience: Applications in supplier selection and critical infrastructure. https://hdl.handle.net/11244/44886.
  9. Hosseini, S., & Al Khaled, A. (2016). A hybrid ensemble and AHP approach for resilient supplier selection. Journal of Intelligent Manufacturing, 1–22.Google Scholar
  10. Hosseini, S., Al Khaled, A., & Sarder, M. D. (2016a). A general framework for assessing system resilience using Bayesian networks: A case study of sulfuric acid manufacturer. Journal of Manufacturing Systems, 41, 211–227.CrossRefGoogle Scholar
  11. Hosseini, S., Barker, K., & Ramirez-Marquez, J. E. (2016b). A review of definitions and measures of system resilience. Reliability Engineering & System Safety, 145, 47–61.CrossRefGoogle Scholar
  12. Hosseini, S., & Barker, K. (2016a). A Bayesian network model for resilience-based supplier selection. International Journal of Production Economics, 180, 68–87.CrossRefGoogle Scholar
  13. Hosseini, S., & Barker, K. (2016b). Modeling infrastructure resilience using Bayesian networks: A case study of inland waterway ports. Computers & Industrial Engineering, 93, 252–266.CrossRefGoogle Scholar
  14. Hosseini S., Ivanov D., Dolgui A. (2019a). Review of quantitative methods for supply chain resilience analysis. Transportation Research: Part E, https://doi.org/10.1016/j.tre.2019.03.001.CrossRefGoogle Scholar
  15. Hosseini, S., Morshedlou, N., Ivanov D., Sarder, MD., Barker, K., Al Khaled, A. (2019b). Resilient supplier selection and optimal order allocation under disruption risks. International Journal of Production Economics, https://doi.org/10.1016/j.ijpe.2019.03.018.CrossRefGoogle Scholar
  16. Hosseini, S., & Sarder, M. D. (2019). Development of a Bayesian network model for optimal site selection of electric vehicle charging station. International Journal of Electrical Power & Energy Systems, 105, 110–122.CrossRefGoogle Scholar
  17. Ivanov, D. (2009). An adaptive framework for aligning (re)planning decisions on supply chain strategy, design, tactics, and operations. International Journal of Production Research, 48(13), 3999–4017.CrossRefGoogle Scholar
  18. Ivanov, D. (2016). Simulation-based ripple effect modeling in the supply chain. International Journal of Production Research, 55(7), 2083–2101.CrossRefGoogle Scholar
  19. Ivanov, D. (2018a). Disruption trails and revival policies: A simulation analysis of supply chain design and production-ordering systems in the recovery and post-disruption periods. Computers & Industrial Engineering.Google Scholar
  20. Ivanov, D. (2018b). Revealing interfaces of supply chain resilience and sustainability: A simulation study. International Journal of Production Research, 56(10), 3507–3523.CrossRefGoogle Scholar
  21. Ivanov, D., Das, A., Choi, T.-M. (2018). New flexibility drivers for manufacturing, supply chain and service operations. International Journal of Production Research, 56(10), 3359–3368.CrossRefGoogle Scholar
  22. Ivanov, D., & Dolgui, A. (2018). Low-certainty-need (LCN) supply chains: A new perspective in managing disruptions risks and resilience. International Journal of Production Research.  https://doi.org/10.1080/00207543.2018.1521025.
  23. Ivanov, D., Dolgui, A., & Sokolov, B. (2015). Supply chain design with disruption considerations: Review of research streams on the ripple effect in the supply chain. IFAC-PapersOnline, 48(3), 1700–1707.Google Scholar
  24. Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2017a). Literature review on disruption recovery in the supply chain. International Journal of Production Research, 55(20), 6158–6174.CrossRefGoogle Scholar
  25. Ivanov, D., Pavlov, A., & Sokolov, B. (2017b). Minimization of disruption-related return flows in the supply chain. International Journal of Production Economics, 183(Part B), 503–513.Google Scholar
  26. Ivanov, D., Dolgui, A., Sokolov, B., Werner, F. (2016a). Schedule robustness analysis with help of attainable sets in continuous flow problem under capacity disruptions. International Journal of Production Research, 54(11), 3397–3413.CrossRefGoogle Scholar
  27. Ivanov, D., Mason, S.J., Hartl, R. (2016b). Supply chain dynamics, control, and disruption management. International Journal of Production Research, 54(1), 1–7.CrossRefGoogle Scholar
  28. Ivanov, D., Pavlov, A., Dolgui, A., Sokolov, B. (2016c). 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.CrossRefGoogle Scholar
  29. Ivanov, D., Sokolov, B., Solovyeva, I., Dolgui, A., Jie, F. (2016d). Dynamic recovery policies for time-critical supply chains under conditions of ripple effect. International Journal of Production Research, 54(23), 7245–7258.CrossRefGoogle Scholar
  30. Ivanov, D., Pavlov, A., & Sokolov, B. (2014a). Optimal distribution (re)-planning in a centralized multi-stage supply network under conditions of the ripple effect and structure dynamics. European Journal of Operational Research, 237(2), 758–770.CrossRefGoogle Scholar
  31. 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.CrossRefGoogle Scholar
  32. Ivanov, D., & Sokolov, B. (2012). Structure dynamics control approach to supply chain planning and adaption. International Journal of Production Research, 50(21), 6133–6149.CrossRefGoogle Scholar
  33. Ivanov, D., Sokolov, B., & Pavlov, A. (2013). Dual problem formulation and its application to optimal redesign of an integrated production-distribution network with structure dynamics and ripple effect considerations. International Journal of Production Research, 51(18), 5386–5403.CrossRefGoogle Scholar
  34. Kondo, A. (2018). The effect of supply chain disruptions caused by the great east Japan earthquake on workers. Japan and the World Economy, 47, 40–50.CrossRefGoogle Scholar
  35. 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.CrossRefGoogle Scholar
  36. Luangkesorn, K. L., Klein, G., & Bidanda, B. (2016). Analysis of production systems with potential for severe disruptions. International Journal of Production Economics, 171(4), 478–486.CrossRefGoogle Scholar
  37. 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

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Industrial Engineering TechnologyUniversity of Southern MississippiLong BeachUSA
  2. 2.Department of Engineering TechnologiesBowling Green State UniversityBowling GreenUSA

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