Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach

  • S.I.: Design and Management of Humanitarian Supply Chains
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

This is the first study that presents a supply chain (SC) resilience measure with the ripple effect considerations including both disruption and recovery stages. SCs have become more prone to disruptions due to their complexity and strategic outsourcing. While development of resilient SC designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. More adversely, resilience assessment in multi-stage SCs is particularly challenged by consideration of disruption propagation and its associated impact known as the ripple effect. We theorize a new measure to quantify the resilience of the original equipment manufacturer (OEM) with a multi-stage assessment of suppliers’ proneness to disruptions and the SC exposure to the ripple effect. We examine and test the developed notion of SC resilience as a function of supplier vulnerability and recoverability using a Bayesian network and considering disruption propagation using a real-life case-study in car manufacturing. The findings suggest that our model can be of value for OEMs to identify the resilience level of their most important suppliers based on forming a quadrant plot in terms of supplier importance and resilience. Our approach can be used by managers to identify the disruption profiles in the supply base and associated SC performance degradation due to the ripple effect. Our method explicitly allows to uncover latent, high-risk suppliers to develop recommendations to control the ripple effect. Utilizing the outcomes of this research can support the design of resilient supply networks with a large number of suppliers: critical suppliers with low resilience can be identified and developed.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

[Adapted from Henry and Ramirez-Marquez (2012)]

Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  • Altay, N., Gunasekaran, A., Dubey, R., & Childe, S. J. (2018). Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within humanitarian setting: a dynamic capability view. Production Planning and Control, 29(14), 1158–1174.

    Article  Google Scholar 

  • Arons, S. (2017). BMW to stop production in China, South Africa on Shortage. Bloomberg. URL https://www.bloomberg.com/europe. Accessed 18 Apr 2019.

  • Automotive News. (2018). http://edit.autonews.com/article/20180730/OEM10/180739995/&template=print&nocache=1. Accessed September 11, 2018.

  • Baharmand, H., Comes, T., & Lauras, M. (2017). Defining and measuring the network flexibility of humanitarian supply chains: Insights from the 2015 Nepal earthquake. Annals of Operations Research 1–40.

  • Bao, S., Zhang, C., Ouyang, M., & Miao, L. (2017). An integrated tri-level model for enhancing the resilience of facilities against intentional attacks. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2705-y.

    Article  Google Scholar 

  • BBC News. (2011). Japan disaster: Supply shortages in three months. http://www.bbc.com/news/business-12782566. Accessed 18 Apr 2019.

  • Behl, A., & Dutta, P. (2018). Humanitarian supply chain management: A thematic literature review and future directions of research. Annals of Operations Research 1–44.

  • Blackhurst, J., Rungtusanatham, M. J., Scheibe, K., & Ambulkar, S. (2018). Supply chain vulnerability assessment: A network based visualization and clustering analysis approach. Journal of Purchasing and Supply Management, 24(1), 21–30.

    Article  Google Scholar 

  • Bode, C., & Wagner, S. M. (2015). Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. Journal of Operations Management, 36, 215–228.

    Article  Google Scholar 

  • Boutselis, P., & McNaught, K. (2019). Using Bayesian networks to forecast spares demand from equipment failures in a changing service logistics context. International Journal of Production Economics, 209, 325–333.

    Article  Google Scholar 

  • Brandon-Jones, E., Squire, B., Autry, C., & Petersen, K. (2014). A contingent resource-based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55–73.

    Article  Google Scholar 

  • Brusset, X., & Teller, C. (2017). Supply chain capabilities, risk, and resilience. International Journal of Production Economics, 184, 59–68.

    Article  Google Scholar 

  • Carbonara, N., & Pellegrino, R. (2017). How do supply chain risk management flexibility-driven strategies perform in mitigating supply disruption risks? International Journal of Integrated Supply Management, 11(4), 354–379.

    Article  Google Scholar 

  • 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, 49, 86–97.

    Article  Google Scholar 

  • Chen, H. Y., Das, A., & Ivanov, D. (2019). Building resilience and managing post-disruption supply chain recovery: Lessons from the information and communication technology industry. International Journal of Information Management, 49, 330–342.

    Article  Google Scholar 

  • Chen, L., & Miller-Hooks, E. (2012). Resilience: An indicator of recovery capability in intermodal freight transport. Transportation Science, 46(1), 109–123.

    Article  Google Scholar 

  • Chen, X., Xi, Z., & Jing, P. (2017). A unified framework for evaluating supply chain reliability and resilience. IEEE Transactions on Reliability, 66(4), 1144–1156.

    Article  Google Scholar 

  • Chowdhury, M. H., & Quaddus, M. (2017). Supply chain resilience: Conceptualization and scale development using dynamic capability theory. International Journal of Production Economics, 188, 185–204.

    Article  Google Scholar 

  • Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1–14.

    Google Scholar 

  • Constantinou, A. C., Fenton, N., Marsh, W., & Radlinski, L. (2016). From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. Artificial Intelligence in Medicine, 67, 75–93.

    Article  Google Scholar 

  • Craighead, C. W., Blackhurst, J., Rungtusanatham, M. J., & Handfield, R. B. (2007). The severity of supply chain disruptions: Design characteristics and mitigation capabilities. Decision Science, 38(1), 131–156.

    Article  Google Scholar 

  • 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. https://doi.org/10.1080/00207543.2019.1627438.

    Article  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), 413–430.

    Google Scholar 

  • Dubey, R., Altay, N., & Blome, C. (2017). Swift trust and commitment: The missing links for humanitarian supply chain coordination? Annals of Operations Research 1–19.

  • Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., & Childe, S. J. (2018). Supply chain agility, adaptability and alignment: Empirical evidence from the Indian auto components industry. International Journal of Operations & Production Management, 38(1), 129–148.

    Article  Google Scholar 

  • Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, A., Blome, C., & Luo, Z. (2019). Antecedents of resilient supply chains: An empirical study. IEEE Transactions on Engineering Management, 66(1), 8–19.

    Article  Google Scholar 

  • Ellis, S. C., Henry, R. M., & Shockley, J. (2010). Buyer perceptions of supply disruption risk: A behavioral view and empirical assessment. Journal of Operations Management, 28(1), 34–46.

    Article  Google Scholar 

  • Elluru, S., Gupta, H., Karu, H., & Prakash Singh, S. (2017). Proactive and reactive models for disaster resilient supply chain. Annals of Operations Research 1–26.

  • Fenton, N., & Neil, M. (2013). Risk assessment and decision analysis with Bayesian networks. Boca Raton, FL: CRC Press.

    Google Scholar 

  • Gao, S. Y., Simchi-Levi, D., Teo, C. P., & Yan, Z. (2019). Disruption risk mitigation in supply chains: The risk exposure index revisited. Operations Research, 67(3), 831–852.

    Article  Google Scholar 

  • Garvey, M. D., Carnovale, S., & Yeniyurt, S. (2015). An analytical framework for supply network risk propagation: A Bayesian network approach. European Journal of Operational Research, 243(2), 618–627.

    Article  Google Scholar 

  • Govindan, G., Jafarian, A., Azbari, M. E., & Choi, T. M. (2016). Optimal bi-objective redundancy allocation for systems reliability and risk management. IEEE Transactions on Cybernetics, 46, 1735–1748.

    Article  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 

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

    Article  Google Scholar 

  • Henry, D., & Ramirez-Marquez, E. (2012). Generic metric quantitative approaches for system resilience as a function of time. Reliability Engineering & System Safety, 99, 114–122.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hosseini, S., & Barker, K. (2016a). A Bayesian network model for resilience-based supplier selection. International Journal of Production Economics, 180, 68–87.

    Article  Google Scholar 

  • Hosseini, S., & Barker, K. (2016b). Modeling infrastructure resilience using Bayesian networks: A case study of inland waterway ports. Computers & Industrial Engineering, 93, 252–266.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hosseini, S., Ivanov, D., & Dolgui, A. (2019a). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E, 125, 285–307.

    Article  Google Scholar 

  • Hosseini, S., Morshedlou, N., Ivanov, D., Sarder, M. D., Barker, K., & Al Khaled, A. (2019b). Resilient supplier selection and optimal order allocation under disruption risks. International Journal of Production Economics, 213, 124–137.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ivanov, D. (2018). 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. (2019). “A blessing in disguise” or “as if it wasn’t hard enough already”: Reciprocal and aggravate vulnerabilities in the supply chain. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1634850.

    Article  Google Scholar 

  • Ivanov, D., & Arkhipov, A. (2011). Analysis of structure adaptation potential in designing supply chains in an agile supply chain environment. International Journal of Integrated Supply Management, 6(2), 165–180.

    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. https://doi.org/10.1080/00207543.2018.1521025.

    Article  Google Scholar 

  • Ivanov, D., Dolgui, A., & Sokolov, B. (Eds.). (2019). Handbook of ripple effects in the supply chain. New York: Springer. ISBN 978-3-030-14301-5.

    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., & Sokolov, B. (2019). Simultaneous structural-operational control of supply chain dynamics and resilience. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03231-0.

    Article  Google Scholar 

  • Ivanov, D., Sokolov, B., & Dolgui, A. (2014a). 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., & Pavlov, A. (2014b). 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., Solovyeva, I., Dolgui, A., & Jie, F. (2016). Discrete recovery policies for time-critical supply chains under conditions of ripple effect. International Journal of Production Research, 54(23), 7245–7258.

    Article  Google Scholar 

  • Jensen, F. V., & Nielsen, T. D. (2007). Bayesian networks and decision graphs. Berlin: Springer.

    Book  Google Scholar 

  • Käki, A., Salo, A., & Talluri, S. (2015). Disruptions in supply networks: A probabilistic risk assessment approach. Journal of Business Logistics, 36(3), 273–287.

    Article  Google Scholar 

  • Kamalahmadi, M., & Parast, M. (2017). As assessment of supply chain disruption mitigation strategies. International Journal of Production Economics, 184, 210–230.

    Article  Google Scholar 

  • Kim, Y., Chen, Y.-S., & Linderman, K. (2015). Supply network disruptions resilience: A network structural perspective. Journal of Operations Management, 33–34, 43–59.

    Article  Google Scholar 

  • Kovacs, G., & Tatham, P. (2009). Responding to disruptions in the supply network-from dormant to action. International Journal of Business Logistics, 30(2), 215–229.

    Article  Google Scholar 

  • Kull, T. J., & Talluri, S. (2008). A supply risk reduction model using integrated multicriteria decision making. IEEE Transactions on Engineering Management, 55(3), 409–419.

    Article  Google Scholar 

  • Langseth, H., & Portinale, L. (2007). Bayesian networks in reliability. Reliability Engineering & System Safety, 92(1), 92–108.

    Article  Google Scholar 

  • Levner, E., & Ptuskin, A. (2017). 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 

  • Liu, Z., Liu, Y., & Baoping, C. (2018). Risk analysis of blowout preventer by mapping GO models into Bayesian networks. Journal of Loss Prevention in the Process Industries, 52, 54–65.

    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, 56(12), 4337–4355.

    Article  Google Scholar 

  • Marquez, D., Neil, M., & Fenton, N. (2010). Improved reliability modeling using Bayesian networks and dynamic discretization. Reliability Engineering & System Safety, 95(4), 412–425.

    Article  Google Scholar 

  • Massey, R. (2011). Tsunami force Sunderland Nissan to shut down for three days because of shortage of parts from Japan. Daily Mail. http://www.dailymail.co.uk/news/article-1374358/Tsunami-forces-Sunderland-Nissan-plant-shut-shortage-parts.html. Accessed 18 Apr 2019.

  • Nair, A., & Vidal, J. M. (2011). Supply network topology and robustness against disruptions-an investigation using multi-agent model. International Journal of Production Research, 49(5), 1391–1404.

    Article  Google Scholar 

  • Narasimhan, R., & Talluri, S. (2009). Perspectives on risk management in supply chains. Journal of Operations Management, 27(2), 114–118.

    Article  Google Scholar 

  • Ojha, R., Ghadge, A., Tiwari, M. K., & Bititci, U. S. (2018). Bayesian network modelling for supply chain risk propagation. International Journal of Production Research, 56(17), 5795–5819.

    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.

    Article  Google Scholar 

  • Pele, D. T., Lazar, E., & Dufour, A. (2017). Information entropy and measures of market risk. Entropy, 19(226), 1–19.

    Google Scholar 

  • Petousis, P., Han, S. X., Aberle, D., & Bui, A. A. (2016). Prediction of lung cancer incidence on the low-dose computed tomography arm of the national lung screening: A dynamic Bayesian network. Artificial Intelligence in Medicine, 72, 42–55.

    Article  Google Scholar 

  • Prasad, S., Woldt, J., Tata, J., & Altay, N. (2017). Application of project management to disaster resilience. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2679-9.

    Article  Google Scholar 

  • Qazi, A., Dickson, A., Quigley, J., & Gaudenzi, B. (2018). Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks. International Journal of Production Economics, 196, 24–42.

    Article  Google Scholar 

  • Qazi, A., Quigley, J., Dickson, A., & Ekici, O. (2017). Exploring dependency based probabilistic supply risk measures for prioritizing interdependent risks and strategies. European Journal of Operational Research, 259(1), 189–204.

    Article  Google Scholar 

  • Rubbernews.com. (2018). Michigan supplier fire idles 4,000 at Ford truck plant in Dearborn. http://www.rubbernews.com/article/20180510/NEWS/180519997?template=printart. Accessed September 11, 2018.

  • Sahebjamnia, N., Torabi, A., & Mansouri, A. (2018). Building organizational resilience in the face of multiple disruptions. International Journal of Production Economics, 197, 63–83.

    Article  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 

  • Shao, B. B. M., Shi, Z. M., Choi, T. Y., & Chae, S. (2018). A data-analytics approach to identifying hidden critical suppliers in supply networks: Development of nexus supplier index. Decision Support Systems, 114, 37–48.

    Article  Google Scholar 

  • Sheffi, Y. (2007). The resilient enterprise: Overcoming vulnerability for competitive advantage. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Sheffi, Y., & Rice, J. (2005). A supply chain view of the resilient enterprise. MIT Sloan Management Review, 47(1), 41–48.

    Google Scholar 

  • Sierra, L. A., Yepes, V., Garcia-Segura, T., & Pellicer, E. (2018). Bayesian network method for decision-making about social sustainability of infrastructure projects. Journal of Cleaner Production, 176, 521–534.

    Article  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 

  • Smith, D., Veitch, B., Khan, F., & Taylor, R. (2017). Understanding industrial safety: Comparing fault tree, Bayesian network, and FRAM approaches. Journal of Loss Prevention in Process Industries, 45, 88–101.

    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 

  • Song, B., Lee, C., & Park, Y. (2013). Assessing the risks of service failures based on ripple effects: A Bayesian network approach. International Journal of Production Economics, 141, 493–504.

    Article  Google Scholar 

  • Sturlaugson, L., Perreault, L., & Sheppard, J. W. (2017). Factored performance functions and decision making in continuous time Bayesian networks. Journal of Applied Logic, 22, 28–45.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Talluri, S., Kull, T. J., Yildiz, H., & Yoon, J. (2013). Assessing the efficiency of risk mitigation strategies in supply chains. Journal of Business Logistics, 34(4), 253–269.

    Article  Google Scholar 

  • Tang, C., Yi, Y., Yang, Z., & Sun, J. (2016). Risk analysis of emergent water pollution accidents based on a Bayesian network. Journal of Environmental Management, 165(1), 199–205.

    Article  Google Scholar 

  • Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics: Research and Applications, 9(1), 33–45.

    Article  Google Scholar 

  • Torabi, S. A., Baghersad, M., & Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E, 79, 22–48.

    Article  Google Scholar 

  • Tukamuhabwa, B. R., Stevenson, M., Bubsy, J., & Zorzini, M. (2015). Supply chain resilience: Definition, review and theoretical foundations for future study. International Journal of Production Research, 35(18), 5592–5623.

    Article  Google Scholar 

  • Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modeling. Ecological Modeling, 203(3–4), 312–318.

    Article  Google Scholar 

  • Wagner, S. M., & Neshat, N. (2010). Assessing the vulnerability of supply chains using graph theory. International Journal of Productions Economics, 126(1), 121–129.

    Article  Google Scholar 

  • Wamba, S. F., Gunasekaran, A., Dubey, R., & Ngai, E. W. (2018). Big data analytics in operations and supply chain management. Annals of Operations Research, 270(1–2), 1–4.

    Article  Google Scholar 

  • Wang, Q. (2008). Probability distribution and entropy as a measure of uncertainty. Journal of Physics A: Mathematical and Theoretical, 41(6), 065004.

    Article  Google Scholar 

  • Yoon, J., Talluri, S., Yildiz, H., & Ho, W. (2018). Models for supplier selection and risk mitigation: A holistic approach. International Journal of Production Research, 56(10), 3636–3661.

    Article  Google Scholar 

  • ZeroHedge. (2011). Latest Japanese supply chain disruptions summary. https://www.zerohedge.com/article/latest-japanese-supply-chain-disruption-summary. Accessed 18 Apr 2019.

  • Zhao, K., Kumar, A., Harrison, T. P., & Yen, J. (2011). Analyzing the resilience of complex supply network topologies against random targeted disruptions. IEEE Systems Journal, 5(1), 28–39.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Industrial Engineering Technology, University of Southern Mississippi, Long Beach, MS, USA

    Seyedmohsen Hosseini

  2. Supply Chain and Operations Management, Berlin School of Economics and Law, Berlin, Germany

    Dmitry Ivanov

Authors
  1. Seyedmohsen Hosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dmitry Ivanov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dmitry Ivanov.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hosseini, S., Ivanov, D. A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach. Ann Oper Res 319, 581–607 (2022). https://doi.org/10.1007/s10479-019-03350-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-019-03350-8

Keywords

Search

Navigation