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

Author

Listed:
  • Dmitry Ivanov

    (Berlin School of Economics and Law)

  • Alexandre Dolgui

    (IMT Atlantique, LS2N-CNRS UMR 6004)

  • Ajay Das

    (CUNY-Baruch)

  • Boris Sokolov

    (St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS))

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 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 the 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 the 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, 2025. "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,, Springer.
    • Handle: RePEc:spr:isochp:978-3-031-85508-5_16
    DOI: 10.1007/978-3-031-85508-5_16
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