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2023 | OriginalPaper | Buchkapitel

1. Risiken, Störungen und der Ripple-Effekt in Lieferketten

verfasst von : Dmitry Ivanov

Erschienen in: Einführung in die Widerstandsfähigkeit der Lieferkette

Verlag: Springer International Publishing

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Zusammenfassung

Dieses Kapitel ist dem Risikomanagement in Lieferketten gewidmet. Das Kapitel beginnt mit grundlegenden Definitionen von Unsicherheit und Risiken. Anschließend werden Klassifizierungen von Störungsrisiken vorgestellt. Entscheidungen im Rahmen des Managements von Störungsrisiken in der Lieferkette werden diskutiert. Der besondere Fokus richtet sich auf Störungsrisiken und den Ripple-Effekt in Lieferketten. Die Konzepte der Disruption Overlays und Disruption Tails werden erläutert. Schließlich werden Pandemien als eine besondere Art von Lieferkettenunterbrechungen (d. h. eine Super-Störung) am Beispiel des Ausbruchs des Coronavirus (COVID-19/SARS-CoV-2) betrachtet.

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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.CrossRef

Azadegan, A., Mellat Parast, M., Lucianetti, L., Nishant, R., & Blackhurst, J. (2020). Supply chain disruptions and business continuity: An empirical assessment. Decision Sciences, 51(1), 38–73.CrossRef

Basole, R. C., & Bellamy, M. A. (2014). Supply network structure, visibility, and risk diffusion: A computational approach. Decision Sciences, 45(4), 1–49.CrossRef

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

Bier, T., Lange, A., & Glock, C. H. (2020). Methods for mitigating disruptions in complex supply chain structures: A systematic literature review. International Journal of Production Research, 58(6), 1835–1856.CrossRef

Bode, C., & Macdonald, J. R. (2017). Stages of supply chain disruption response: Direct, constraining, and mediating factors for impact mitigation. Decision Sciences, 48(5), 836–874.CrossRef

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

Cao, S., Bryceson, K., & Hine, D. (2019). An Ontology-based Bayesian network modelling for supply chain risk propagation. Industrial Management and Data Systems, 119(8), 1691–1711.CrossRef

Chatfield, D. C., Hayya, J. C., & Cook, D. P. (2013). Stockout propagation and amplification in supply chain inventory systems. International Journal of Production Research, 51(5), 1491–1507.CrossRef

Chaudhuri, A., Srivastava, S. K., Srivastava, R. K., & Parveen, Z. (2016). Risk propagation and its impact on performance in food processing supply chain: A fuzzy interpretive structural modeling based approach. Journal of Modelling in Management, 11(2), 660–693.CrossRef

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.CrossRef

Choi, T.-M. (2020). Risk analysis in logistics systems: A research agenda during and after the COVID-19 pandemic. Transportation Research Part E: Logistics and Transportation, 140, 101961.CrossRef

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

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

Chopra, S., Reinhardt, G., & Mohan, U. (2007). The importance of decoupling recurrent and disruption risks in a supply chain. Naval Research Logistics, 54(5), 44–555.CrossRef

Christopher, M. (2011). Logistics & supply chain management (4. Aufl.). Financial Times Prentice Hall.

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

Demirel, G., MacCarthy, B. L., Ritterskamp, D., Champneys, A., & Gross, T. (2019). Identifying dynamical instabilities in supply networks using generalized modeling. Journal of Operations Management, 65(2), 133–159.CrossRef

Disney, S. M., & Towill, D. R. (2002). A discrete transfer function model to determine the dynamic stability of a vendor managed inventory supply chain. International Journal of Production Research, 40, 179–204.CrossRef

Dolgui, A., & Ivanov, D. (2020). Exploring supply chain structural dynamics: New disruptive technologies and disruption risks. International Journal of Production Economics, 229, 107886.CrossRef

Dolgui, A., & Ivanov, D. (2021). Ripple effect and supply chain disruption management: New trends and research directions. International Journal of Production Research, 59(1), 102–109.CrossRef

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.CrossRef

Dolgui, A., Ivanov, D., & Rozhkov, M. (2020). 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, 58(5), 1285–1301.CrossRef

Duong, L. N. K., & Chong, J. (2020). Supply chain collaboration in the presence of disruptions: A literature review. International Journal of Production Research, 58(11), 3488–3507.CrossRef

El Baz, J., & Ruel, S. (2021). Can supply chain risk management practices mitigate the disruption im-pacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2020.107972

Essuman, D., Boso, N., & Annan, J. (2020). Operational resilience, disruption, and efficiency: Conceptual and empirical analyses. International Journal of Production Economics. Available online, https://doi.org/10.1016/j.ijpe.2020.107762

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.CrossRef

Fiksel, J., Polyviou, M., Croxton, K. L., & Pettit, T. J. (2015). From risk to resilience: Learning to deal with disruption. MIT Sloan Management Review, 56(2), 79–86.

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.CrossRef

Ghadge, A., Dani, S., Chester, M., & Kalawsky, R. (2013). A systems thinking approach for modelling supply chain risk propagation. Supply Chain Management: An International Journal, 18(5), 523–538.CrossRef

Ghadge, A., Weiß, M., Caldwell, N. D., & Wilding, R. (2019). Managing cyber risk in supply chains: A review and research agenda. Supply Chain Management: An International Journal, 25(2), 223–240.CrossRef

Gholami-Zanjani, S. M., Klibi, W., Jabalameli, M. S., & Pishvaee, M. S. (2020). A robust location-inventory model for food supply chains operating under disruptions with ripple effects. International Journal of Production Research. in press.

Gianesello, P., Ivanov, D., & Battini, D. (2017). Closed-loop supply chain simulation with disruption considerations: A case-study on Tesla. International Journal of Inventory Research, 4(4), 257–280.CrossRef

Golan, M. S., Jernegan, L. H., & Linkov, I. (2020). Trends and applications of resilience analytics in supply chain modeling: Systematic literature review in the context of the COVID-19 pandemic. Environment Systems and Decisions. https://doi.org/10.1007/s10669-020-09777-w

Goldbeck, N., Angeloudis, P., & Ochieng, W. (2020). Optimal supply chain resilience with consideration of failure propagation and repair logistics. Transportation Research: Part E, 133, 101830.CrossRef

Gupta, S., Starr, M., Zanjirani Farahani, R., & Matinrad, N. (2016). Disaster management from a POM perspective: Mapping a new domain. Production and Operations Management, 25(10), 1611–1637.CrossRef

Gupta, V., & Ivanov, D. (2020). Dual sourcing under supply disruption with risk-averse suppliers in the sharing economy. International Journal of Production Research, 58(1), 291–307.CrossRef

Gupta, V., Ivanov, D., & Choi, T.-M. (2021). Competitive pricing of substitute products under supply disruption. Omega, 101, 102279.CrossRef

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

Haren, P., & Simchi-Levi, D. (2020). How coronavirus could impact the global supply chain by mid-March. Harward Business Review 2020. https://hbr.org/2020/02/how-coronavirus-could-impact-the-global-supply-chain-by-mid-march?ab=hero-subleft-1. Zugegriffen am 10.03.2020.

He, J., Alavifard, F., Ivanov, D., & Jahani, H. (2019). A real-option approach to mitigate disruption risk in the supply chain. Omega: The International Journal of Management Science, 88, 133–149.CrossRef

Heckmann, I. (2016). Towards supply chain risk analytics. Springer.CrossRef

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.CrossRef

Hosseini, S., Ivanov, D., & Dolgui, A. (2019a). Ripple effect modeling of supplier disruption: Integrated Markov Chain and Dynamic Bayesian Network Approach. International Journal of Production Research. in press.

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

Huffington Post. (2015). Toyota among Japanese automaker extending plant closures. http://www.huffingtonpost.com/2011/03/16/japans-auto-plant-closures_n_836653.html

Ivanov, D. (2018a). Structural dynamics and resilience in supply chain risk management. Springer.CrossRef

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

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.CrossRef

Ivanov, D. (2020a). “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, 58(11), 3252–3262.CrossRef

Ivanov, D. (2020b). Predicting the impact of epidemic outbreaks on the global supply chains: A simulation-based analysis on the example of coronavirus (COVID-19/SARS-CoV-2) case. Transportation Research – Part E, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922CrossRef

Ivanov, D. (2020c). Viable supply chain model: Integrating agility, resilience and sustainability perspectives. Lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03640-6

Ivanov, D. (2021a). Lean resilience: AURA (active usage of resilience assets) framework for post-COVID-19 supply chain management. International Journal of Logistics Management, forthcoming.

Ivanov, D. (2021b). Exiting the COVID-19 pandemic: After-shock risks and avoidance of disruption tails in supply chains. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04047-7

Ivanov, D., & Das, A. (2020). Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: A research note. International Journal of Integrated Supply Management, 13(1), 90–102.CrossRef

Ivanov, D., & Dolgui, A. (2019). Low-Certainty-Need (LCN) supply chains: A new perspective in managing disruption risks and resilience. International Journal of Production Research, 57(15–16), 5119–5136.CrossRef

Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915.CrossRef

Ivanov, D., & Dolgui, A. (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, 232, 107921.CrossRef

Ivanov, D., & Rozhkov, M. (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, 291(1–2), 387–407.CrossRef

Ivanov, D., & Sokolov, B. (2010). Adaptive supply chain management. Springer.CrossRef

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.CrossRef

Ivanov, D., & Sokolov, B. (2020). Simultaneous structural-operational control of supply chain dynamics and resilience. Annals of Operations Research, 283(1), 1191–1210.

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

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.CrossRef

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.CrossRef

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.CrossRef

Ivanov, D., Dolgui, A., & Sokolov, B. (2019a). 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.CrossRef

Ivanov, D., Dolgui, A., & Sokolov, B. (Hrsg.). (2019b). Handbook of ripple effects in the supply chain. Springer.

Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2021). Global supply chain and operations management: A decision-oriented introduction into the creation of value (3. Aufl.). Springer Nature.CrossRef

Kinra, A., Ivanov, D., Das, A., & Dolgui, A. (2020). Ripple effect quantification by supply risk exposure assessment. International Journal of Production Research, 58(19), 5559–5578.CrossRef

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.CrossRef

Knight, F. (1921). Risk, uncertainty and profit: Hart, Schaffner and Marx Prize Essays 31. Houghton Mifflin.

Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558.CrossRef

Li, Y., & Zobel, C. W. (2020). Exploring supply chain network resilience in the presence of the ripple effect. International Journal of Production Economics, 228, 107693.CrossRef

Li, Y., Zobel, C. W., Seref, O., & Chatfield, D. C. (2020). Network characteristics and supply chain resilience under conditions of risk propagation. International Journal of Production Economics, 223, 107529.CrossRef

Li, Y., Chen, K., Collignon, S., & Ivanov, D. (2021). Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability. European Journal of Operational Research, 291(3), 1117–1131.CrossRef

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

Linkov, I., & Kott, A. (2019). Fundamental concepts of cyber resilience: Introduction and overview. In A. Kott & I. Linkov (Hrsg.), Cyber resilience of systems and networks (S. 1–25). Springer.

Llaguno, A., Mula, J., & Campuzano-Bolarin, F. (2021). State of the art, conceptual framework and simulation analysis of the ripple efect on supply chains. International Journal of Production Research. https://doi.org/10.1080/00207543.2021.1877842

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.CrossRef

Manager Magazine. (2021). Warum die Chiphersteller die Autoindustrie schmoren lassen. (in German). https://www.manager-magazin.de/unternehmen/autoindustrie/autoindustrie-warum-die-versorgung-mit-mikrochips-stockt-und-die-produktion-laehmt-a-970562ab-c6cf-4b23-9097-f089594eb2d5?utm_source=pocket-newtab-global-de-DE. Zugegriffen am 21.01.2021.

March, J. G., & Shapira, Z. (1987). Managerial perspectives on risk and risk taking. Management Science, 33, 1404–1418.CrossRef

Markowitz, H. (1952). Portfolio selection. The Journal of Finance, VII(1), 77–91.

Marsh, P., Brown, K., Anderlini, J., Johnston, T., Waldmeir, P., Jung, S., Rickards, J., Weitzman, H., & Ward, A. (2011) Japan crisis impact on the supply chain: Global industries consider their options. Financial Times Europe, No. 37568 17 March, 16.

Moetz, A., Stylos-Duesmann, P., & Otto, B. (2019). Schedule instability in automotive production networks: The development of a network-oriented resequencing method. Proceedings of the 9th IFAC conference on manufacturing modelling, management and control (MIM 2019), edited by Dmitry Ivanov, Alexandre Dolgui, Farouk Yalaoui. IFAC-PapersOnLine, 52(13), 2810–2815.CrossRef

Nair, A., & Vidal, J. M. (2011). Supply network topology and robustness against disruptions: An investigation using multiagent model. International Journal of Production Research, 49(5), 1391–1404.CrossRef

Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Wamba, S. F. (2017). The role of big data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142(2), 1108–1118.CrossRef

Paul, S., Sarker, R., Essam, D., & Lee, P. T.-W. (2019). Managing sudden disturbances in a three-tier manufacturing supply chain: A mathematical modelling approach. Annals of Operations Research, 280, 299–335.CrossRef

Paul, S. K., & Chowdhury, P. (2021). A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19. International Journal of Physical Distribution & Logistics Management, 51(2), 104–125.CrossRef

Paul, S. K., Sarker, R., & Essam, D. (2017). A quantitative model for disruption mitigation in a supply chain. European Journal of Operational Research, 257(3), 881–895.CrossRef

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

Peck, H. (2005). Drivers of supply chain vulnerability: An integrated framework. International Journal of Physical Distribution & Logistics Management, 35(4), 210–232.CrossRef

Pettit, J. T., Fiksel, J., & Croxton, L. K. (2010). Ensuring supply chain resilience: Development of a conceptual framework. Journal of Business Logistics, 31(1), 1–21.CrossRef

Ponomarov, S., & Holcomb, M. (2009). Understanding the concept of supply chain resilience. International Journal of Logistics Management, 20(1), 124–143.

Pournader, M., Kach, A., & Talluri, S. (2020). A review of the existing and emerging topics in supply chain risk management literature. Decision Sciences, 51(4), 867–919.CrossRef

Quang, H. T., & Hara, Y. (2017). Risks and performance in supply chain: The push effect. International Journal of Production Research, 56, 1369.CrossRef

Queiroz, M. M., Ivanov, D., Dolgui, A., & Fosso, W. S. (2020). Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03685-7

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

Rosenhead, J., Elton, M., & Gupta, S. K. (1972). Robustness and optimality as criteria for strategic decisions. Operational Research Quarterly, 23(4), 413–431.CrossRef

Saghafian, S., & Van Oyen, M. P. (2016). Compensating for dynamic supply disruptions: Backup flexibility design. Operations Research, 64(2), 390–405.CrossRef

Sahebjamnia, N., Torabi, S. A., & Mansouri, S. A. (2015). Integrated business continuity and disaster recovery planning: Toward organizational resilience. European Journal of Operational Research, 242(1), 261–273.CrossRef

Sawik, T. (2020). Supply chain disruption management (2. Aufl.). Springer.CrossRef

Sawik, T. (2021). A linear model for optimal cybersecurity investment in industry 4.0 supply chains. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1856442

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.CrossRef

Shen, B., & Li, Q. (2017). Market disruptions in supply chains: A review of operational models. International Transactions Inoperational Research, 24, 697–711.CrossRef

Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P. Y., Combs, K., Ge, Y., Gusikhin, O., Sander, M., & Zhang, D. (2015). Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces, 45(5), 375–390.CrossRef

Simchi-Levi, D., Wang, H., & Wei, Y. (2018). Increasing supply chain robustness through process flexibility and inventory. Production and Operations Management, 27(8), 1491–1496.CrossRef

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.CrossRef

Sodhi, M., & Tang, C. S. (2012). Managing supply chain risk. Springer.CrossRef

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.CrossRef

Tan, W. J., Zhang, A. N., & Cai, W. (2019). A graph-based model to measure structural redundancy for supply chain resilience. International Journal of Production Research, 57(20), 6385–6404.CrossRef

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

Tang, L., Jing, K., He, J., & Stanley, H. E. (2016). Complex interdependent supply chain networks: Cascading failure and robustness. Physica A, 443, 58–69.CrossRef

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.CrossRef

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

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.CrossRef

Van der Vegt, G., Essens, P., Wahlstrom, M., & George, G. (2015). Managing risk and resilience. Academy of Management Journal, 58(4), 971–980.CrossRef

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

Wu, D., & Olson, D. L. (2008). Supply chain risk, simulation, and vendor selection. International Journal of Production Economics, 114(2), 646–655.CrossRef

Xu, S., Zhang, X., Feng, L., & Yang, W. (2020). Disruption risks in supply chain management: A literature review based on bibliometric analysis. International Journal of Production Research, 58(11), 3508–3526.CrossRef

Yan, T., Choi, T. Y., Kim, Y., & Yang, Y. (2015). A theory of the nexus supplier: A critical supplier from a network perspective. Journal of Supply Chain Management, 51(1), 52–66.CrossRef

Yang, Z., Aydin, G., Babich, V., & Beil, D. R. (2009). Supply disruptions, asymmetric information, and a backup production option. Management Science, 55(2), 192–120.CrossRef

Yao, Y., & Fabbe-Costas, N. (2018). Can you measure resilience if you are unable to define it? The analysis of Supply Network Resilience (SNRES). Supply Chain Forum: An International Journal, 19(4), 255–265.CrossRef

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.CrossRef

Zhao, K., Zuo, Z., & Blackhurst, J. V. (2019). Modelling supply chain adaptation for disruptions: An empirically grounded complex adaptive systems approach. Journal of Operations Management, 65(2), 190–212.CrossRef

Titel: Risiken, Störungen und der Ripple-Effekt in Lieferketten
verfasst von: Dmitry Ivanov
Verlag: Springer International Publishing
Buch: Einführung in die Widerstandsfähigkeit der Lieferkette
Print ISBN: 978-3-031-25185-6

Electronic ISBN: 978-3-031-25186-3

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-25186-3_1