IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v55y2017i7p2083-2101.html
   My bibliography  Save this article

Simulation-based ripple effect modelling in the supply chain

Author

Listed:
  • Dmitry Ivanov

Abstract

In light of low-frequency/high-impact disruptions, the ripple effect has recently been introduced into academic literature on supply chain management. The ripple effect in the supply chain results from disruption propagation from the initial disruption point to the supply, production and distribution networks. While optimisation modelling dominates this research field, the potential of simulation modelling still remains under-explored. The objective of this study is to reveal research gaps that can be closed with the help of simulation modelling. First, recent literature on both optimisation and simulation modelling is analysed. Second, a simulation model for multi-stage supply chain design with consideration of capacity disruptions and experimental results is presented in order to depict major areas of simulation application to the ripple effect modelling. Based on both literature analysis and the modelling example, managerial insights and future research areas are identified in regard to simulation modelling application to the ripple effect analysis in the supply chain. The paper concludes by summarising the most important insights and outlining a future research agenda.

Suggested Citation

  • Dmitry Ivanov, 2017. "Simulation-based ripple effect modelling in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 2083-2101, April.
  • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:7:p:2083-2101
    DOI: 10.1080/00207543.2016.1275873
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2016.1275873
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2016.1275873?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Brian M. Lewis & Alan L. Erera & Maciek A. Nowak & White Chelsea C., 2013. "Managing Inventory in Global Supply Chains Facing Port-of-Entry Disruption Risks," Transportation Science, INFORMS, vol. 47(2), pages 162-180, May.
    2. Tingting Cui & Yanfeng Ouyang & Zuo-Jun Max Shen, 2010. "Reliable Facility Location Design Under the Risk of Disruptions," Operations Research, INFORMS, vol. 58(4-part-1), pages 998-1011, August.
    3. Tang, Christopher S. & Davarzani, Hoda & Sarkis, Joseph, 2015. "Quantitative models for managing supply chain risks: A reviewAuthor-Name: Fahimnia, Behnam," European Journal of Operational Research, Elsevier, vol. 247(1), pages 1-15.
    4. W.C. Tsai, 2016. "A dynamic sourcing strategy considering supply disruption risks," International Journal of Production Research, Taylor & Francis Journals, vol. 54(7), pages 2170-2184, April.
    5. Sawik, Tadeusz, 2015. "On the fair optimization of cost and customer service level in a supply chain under disruption risks," Omega, Elsevier, vol. 53(C), pages 58-66.
    6. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
    7. Wilson, Martha C., 2007. "The impact of transportation disruptions on supply chain performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(4), pages 295-320, July.
    8. Benjamin R. Tukamuhabwa & Mark Stevenson & Jerry Busby & Marta Zorzini, 2015. "Supply chain resilience: definition, review and theoretical foundations for further study," International Journal of Production Research, Taylor & Francis Journals, vol. 53(18), pages 5592-5623, September.
    9. Ivanov, Dmitry & Pavlov, Alexander & Dolgui, Alexandre & Pavlov, Dmitry & Sokolov, Boris, 2016. "Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 90(C), pages 7-24.
    10. William Ho & Tian Zheng & Hakan Yildiz & Srinivas Talluri, 2015. "Supply chain risk management: a literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 53(16), pages 5031-5069, August.
    11. Faisal Aqlan & Sarah S. Lam, 2015. "Supply chain risk modelling and mitigation," International Journal of Production Research, Taylor & Francis Journals, vol. 53(18), pages 5640-5656, September.
    12. Liberatore, Federico & Scaparra, Maria P. & Daskin, Mark S., 2012. "Hedging against disruptions with ripple effects in location analysis," Omega, Elsevier, vol. 40(1), pages 21-30, January.
    13. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    14. Matsuo, Hirofumi, 2015. "Implications of the Tohoku earthquake for Toyota׳s coordination mechanism: Supply chain disruption of automotive semiconductors," International Journal of Production Economics, Elsevier, vol. 161(C), pages 217-227.
    15. Tang, Ou & Nurmaya Musa, S., 2011. "Identifying risk issues and research advancements in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 133(1), pages 25-34, September.
    16. Hasani, Aliakbar & Khosrojerdi, Amirhossein, 2016. "Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 20-52.
    17. Kevin B. Hendricks & Vinod R. Singhal, 2005. "Association Between Supply Chain Glitches and Operating Performance," Management Science, INFORMS, vol. 51(5), pages 695-711, May.
    18. Klibi, Walid & Martel, Alain & Guitouni, Adel, 2010. "The design of robust value-creating supply chain networks: A critical review," European Journal of Operational Research, Elsevier, vol. 203(2), pages 283-293, June.
    19. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Complex interdependent supply chain networks: Cascading failure and robustness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 58-69.
    20. Michael K. Lim & Achal Bassamboo & Sunil Chopra & Mark S. Daskin, 2013. "Facility Location Decisions with Random Disruptions and Imperfect Estimation," Manufacturing & Service Operations Management, INFORMS, vol. 15(2), pages 239-249, May.
    21. Schmitt, Amanda J. & Sun, Siyuan Anthony & Snyder, Lawrence V. & Shen, Zuo-Jun Max, 2015. "Centralization versus decentralization: Risk pooling, risk diversification, and supply chain disruptions," Omega, Elsevier, vol. 52(C), pages 201-212.
    22. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
    23. Bueno-Solano, Alfredo & Cedillo-Campos, Miguel Gastón, 2014. "Dynamic impact on global supply chains performance of disruptions propagation produced by terrorist acts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 1-12.
    24. Yu, Haisheng & Zeng, Amy Z. & Zhao, Lindu, 2009. "Single or dual sourcing: decision-making in the presence of supply chain disruption risks," Omega, Elsevier, vol. 37(4), pages 788-800, August.
    25. Cui, Tingting & Ouyang, Yanfeng & Shen, Zuo-Jun Max J, 2010. "Reliable Facility Location Design under the Risk of Disruptions," University of California Transportation Center, Working Papers qt5sh2c7pw, University of California Transportation Center.
    26. Ivanov, Dmitry & Pavlov, Alexander & Sokolov, Boris, 2014. "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, Elsevier, vol. 237(2), pages 758-770.
    27. Frank Chen & Zvi Drezner & Jennifer K. Ryan & David Simchi-Levi, 2000. "Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information," Management Science, INFORMS, vol. 46(3), pages 436-443, March.
    28. Khakzad, Nima, 2015. "Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 263-272.
    29. Losada, Chaya & Scaparra, M. Paola & O’Hanley, Jesse R., 2012. "Optimizing system resilience: A facility protection model with recovery time," European Journal of Operational Research, Elsevier, vol. 217(3), pages 519-530.
    30. Dmitry Ivanov & Boris Sokolov & Inna Solovyeva & Alexandre Dolgui & Ferry Jie, 2016. "Dynamic recovery policies for time-critical supply chains under conditions of ripple effect," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7245-7258, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov & Marina Ivanova, 2017. "Literature review on disruption recovery in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6158-6174, October.
    2. Ivanov, Dmitry & Pavlov, Alexander & Dolgui, Alexandre & Pavlov, Dmitry & Sokolov, Boris, 2016. "Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 90(C), pages 7-24.
    3. Ivanov, Dmitry & Pavlov, Alexander & Pavlov, Dmitry & Sokolov, Boris, 2017. "Minimization of disruption-related return flows in the supply chain," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 503-513.
    4. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry & Zennaro, Ilenia, 2021. "Costs of resilience and disruptions in supply chain network design models: A review and future research directions," International Journal of Production Economics, Elsevier, vol. 235(C).
    5. Ivanov, Dmitry & Dolgui, Alexandre, 2021. "OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications," International Journal of Production Economics, Elsevier, vol. 232(C).
    6. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
    7. Jabbarzadeh, Armin & Fahimnia, Behnam & Sheu, Jiuh-Biing & Moghadam, Hani Shahmoradi, 2016. "Designing a supply chain resilient to major disruptions and supply/demand interruptions," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 121-149.
    8. Dmitry Ivanov & Maxim Rozhkov, 2020. "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, Springer, vol. 291(1), pages 387-407, August.
    9. Pythagoras N. Petratos & Alessio Faccia, 2023. "Fake news, misinformation, disinformation and supply chain risks and disruptions: risk management and resilience using blockchain," Annals of Operations Research, Springer, vol. 327(2), pages 735-762, August.
    10. Sanjoy Kumar Paul & Ruhul Sarker & Daryl Essam & Paul Tae-Woo Lee, 2019. "A mathematical modelling approach for managing sudden disturbances in a three-tier manufacturing supply chain," Annals of Operations Research, Springer, vol. 280(1), pages 299-335, September.
    11. Azad, Nader & Hassini, Elkafi, 2019. "Recovery strategies from major supply disruptions in single and multiple sourcing networks," European Journal of Operational Research, Elsevier, vol. 275(2), pages 481-501.
    12. Dmitry Ivanov & Boris Sokolov & Inna Solovyeva & Alexandre Dolgui & Ferry Jie, 2016. "Dynamic recovery policies for time-critical supply chains under conditions of ripple effect," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7245-7258, December.
    13. Margolis, Joshua T. & Sullivan, Kelly M. & Mason, Scott J. & Magagnotti, Mariah, 2018. "A multi-objective optimization model for designing resilient supply chain networks," International Journal of Production Economics, Elsevier, vol. 204(C), pages 174-185.
    14. Fahimnia, Behnam & Jabbarzadeh, Armin, 2016. "Marrying supply chain sustainability and resilience: A match made in heaven," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 306-324.
    15. Nader Azad & Elkafi Hassini, 2019. "A Benders Decomposition Method for Designing Reliable Supply Chain Networks Accounting for Multimitigation Strategies and Demand Losses," Transportation Science, INFORMS, vol. 53(5), pages 1287-1312, September.
    16. Alikhani, Reza & Torabi, S.Ali & Altay, Nezih, 2021. "Retail supply chain network design with concurrent resilience capabilities," International Journal of Production Economics, Elsevier, vol. 234(C).
    17. Cui, Jianxun & Zhao, Meng & Li, Xiaopeng & Parsafard, Mohsen & An, Shi, 2016. "Reliable design of an integrated supply chain with expedited shipments under disruption risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 95(C), pages 143-163.
    18. Rezapour, Shabnam & Farahani, Reza Zanjirani & Pourakbar, Morteza, 2017. "Resilient supply chain network design under competition: A case study," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1017-1035.
    19. Goldbeck, Nils & Angeloudis, Panagiotis & Ochieng, Washington, 2020. "Optimal supply chain resilience with consideration of failure propagation and repair logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    20. Zhixue Liu & Shukun Wang & Yanfeng Ouyang, 2017. "Reliable Biomass Supply Chain Design under Feedstock Seasonality and Probabilistic Facility Disruptions," Energies, MDPI, vol. 10(11), pages 1-18, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tprsxx:v:55:y:2017:i:7:p:2083-2101. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.