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Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility

In: Handbook of Ripple Effects in the Supply Chain

Author

Listed:
  • Dmitry Ivanov

    (Berlin School of Economics and Law)

  • Alexandre Dolgui

    (IMT Atlantique, LS2N, CNRS)

  • Ajay Das

    (Zicklin School of Business, CUNY-Baruch)

  • Boris Sokolov

    (Saint Petersburg Institute for Informatics and Automation of the RAS (SPIIRAS))

Abstract

The quality of model-based decision-making support strongly depends on the data, its completeness, fullness, validity, consistency, and timely availability. These requirements on data are of a special importance in supply chain (SC) risk management for predicting disruptions and reacting to them. Digital technology, Industry 4.0, Blockchain, and real-time data analytics have a potential to achieve a new quality in decision-making support when managing severe disruptions, resilience, and the Ripple effect. A combination of simulation, optimization, and data analytics constitutes a digital twin: a new data-driven vision of managing the disruption risks in SC. A digital SC twin is a model that can represent the network state for any given moment in time and allow for complete end-to-end SC visibility to improve resilience and test contingency plans. This chapter proposes an SC risk analytics framework and explains the concept of digital SC twins. It analyses perspectives and future transformations to be expected in transition toward cyber-physical SCs. It demonstrates a vision of how digital technologies and smart operations can help integrate resilience and lean thinking into a resileanness framework “Low-Certainty-Need” (LCN) SC.

Suggested Citation

  • Dmitry Ivanov & Alexandre Dolgui & Ajay Das & Boris Sokolov, 2019. "Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility," International Series in Operations Research & Management Science, in: Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov (ed.), Handbook of Ripple Effects in the Supply Chain, pages 309-332, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-14302-2_15
    DOI: 10.1007/978-3-030-14302-2_15
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    Citations

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    Cited by:

    1. Shraddha Mishra & Surya Prakash Singh, 2022. "A stochastic disaster-resilient and sustainable reverse logistics model in big data environment," Annals of Operations Research, Springer, vol. 319(1), pages 853-884, December.
    2. Büyüközkan, Gülçin & Uztürk, Deniz, 2022. "A Methodology to Investigate Challenges for Digital Twin Technology in Smart Agriculture," Land, Farm & Agribusiness Management Department 337119, Harper Adams University, Land, Farm & Agribusiness Management Department.
    3. Büyüközkan, Gülçin & Uztürk, Deniz, 2022. "A Methodology to Investigate Challenges for Digital Twin Technology in Smart Agriculture," Agri-Tech Economics Papers 337119, Harper Adams University, Land, Farm & Agribusiness Management Department.
    4. Iftikhar, Ilaria Giannoccaro & Anas, 2023. "Mitigating ripple effect in supply networks: the effect of trust and topology on resilience," OSF Preprints 2spt3, Center for Open Science.
    5. Shenle Pan & Ray Zhong & Ting Qu, 2019. "Smart product-service systems in interoperable logistics: Design and implementation prospects," Post-Print hal-02316272, HAL.
    6. Patrick Brandtner & Farzaneh Darbanian & Taha Falatouri & Chibuzor Udokwu, 2021. "Impact of COVID-19 on the Customer End of Retail Supply Chains: A Big Data Analysis of Consumer Satisfaction," Sustainability, MDPI, vol. 13(3), pages 1-18, January.
    7. Essam Kaoud & Mohammad A. M. Abdel-Aal & Tatsuhiko Sakaguchi & Naoki Uchiyama, 2020. "Design and Optimization of the Dual-Channel Closed Loop Supply Chain with E-Commerce," Sustainability, MDPI, vol. 12(23), pages 1-21, December.
    8. Yi Zheng & Li Liu & Victor Shi & Wenxing Huang & Jianxiu Liao, 2022. "A Resilience Analysis of a Medical Mask Supply Chain during the COVID-19 Pandemic: A Simulation Modeling Approach," IJERPH, MDPI, vol. 19(13), pages 1-21, June.
    9. Jessica Olivares-Aguila & Alejandro Vital-Soto, 2021. "Supply Chain Resilience Roadmaps for Major Disruptions," Logistics, MDPI, vol. 5(4), pages 1-18, November.
    10. Mukesh Kumar & Rakesh D. Raut & Mahak Sharma & Vikas Kumar Choubey & Sanjoy Kumar Paul, 2022. "Enablers for resilience and pandemic preparedness in food supply chain," Operations Management Research, Springer, vol. 15(3), pages 1198-1223, December.
    11. Alam, Md Fahim Bin & Tushar, Saifur Rahman & Ahmed, Tazim & Karmaker, Chitra Lekha & Bari, A.B.M. Mainul & de Jesus Pacheco, Diego Augusto & Nayyar, Anand & Islam, Abu Reza Md Towfiqul, 2024. "Analysis of the enablers to deal with the ripple effect in food grain supply chains under disruption: Implications for food security and sustainability," International Journal of Production Economics, Elsevier, vol. 270(C).
    12. Tom Binsfeld & Benno Gerlach, 2022. "Quantifying the Benefits of Digital Supply Chain Twins—A Simulation Study in Organic Food Supply Chains," Logistics, MDPI, vol. 6(3), pages 1-23, July.
    13. Niloofar Etemadi & Pieter Van Gelder & Fernanda Strozzi, 2021. "An ISM Modeling of Barriers for Blockchain/Distributed Ledger Technology Adoption in Supply Chains towards Cybersecurity," Sustainability, MDPI, vol. 13(9), pages 1-28, April.
    14. Ivanov, Dmitry, 2020. "Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    15. Benno Gerlach & Simon Zarnitz & Benjamin Nitsche & Frank Straube, 2021. "Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits," Logistics, MDPI, vol. 5(4), pages 1-24, December.
    16. Alexander Pavlov & Dmitry Ivanov & Frank Werner & Alexandre Dolgui & Boris Sokolov, 2022. "Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains," Annals of Operations Research, Springer, vol. 319(1), pages 609-631, December.
    17. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
    18. Chih-Hung Hsu & Xu He & Ting-Yi Zhang & An-Yuan Chang & Wan-Ling Liu & Zhi-Qiang Lin, 2022. "Enhancing Supply Chain Agility with Industry 4.0 Enablers to Mitigate Ripple Effects Based on Integrated QFD-MCDM: An Empirical Study of New Energy Materials Manufacturers," Mathematics, MDPI, vol. 10(10), pages 1-35, May.
    19. Duong, Quang Huy & Zhou, Li & Meng, Meng & Nguyen, Truong Van & Ieromonachou, Petros & Nguyen, Duy Tiep, 2022. "Understanding product returns: A systematic literature review using machine learning and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 243(C).
    20. Lee, Chien-Chiang & Hussain, Jafar, 2023. "Energy sustainability under the COVID-19 outbreak: Electricity break-off policy to minimize electricity market crises," Energy Economics, Elsevier, vol. 125(C).
    21. Nicoletti, Bernardo & Appolloni, Andrea, 2024. "A framework for digital twins solutions for 5 PL operators," Technology in Society, Elsevier, vol. 76(C).

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