Skip to main content
SpringerLink
Log in

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

  • Chapter
  • First Online:
Book cover Handbook of Ripple Effects in the Supply Chain

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 276))

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Addo-Tenkorang, R., & Helo, P. T. (2016). Big data applications in operations/supply-chain management: A literature review. Computers & Industrial Engineering, 101, 528–543.

    Article  Google Scholar 

  • Andelfinger, V., & Hänisch, T. (2017). Industrie 4.0: Wie cyber-physische Systeme die Arbeitswelt verändern. Springer Gabler, Wiesbaden.

    Google Scholar 

  • Baryannis, G., Validi, S., Dani S., & Antoniou, G. (2018). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, https://doi.org/10.1080/00207543.2018.1530476.

    Article  Google Scholar 

  • Bearzotti, L. A., Salomone, E., & Chiotti, O. J. (2012). An autonomous multi-agent approach to supply chain event management. International Journal of Production Economics, 135(1), 468–478.

    Article  Google Scholar 

  • Ben-Daya, M., Hassini E., & Bahroun Z. (2018). Internet of things and supply chain management: A literature review. International Journal of Production Research, https://doi.org/10.1080/00207543.2017.1402140.

  • Bonfour, A. (2016). Digital future, digital transformation: From lean production to acceluction. Switzerland: Springer.

    Book  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, forthcoming.

    Google Scholar 

  • Choi, T.M., Wallace S.W., & Wang Y. (2018). Big Data Analytics in Operations Management. Production and Operations Management, https://doi.org/10.1111/poms.12838.

    Article  Google Scholar 

  • Crosby, M., Pattanayak, P., Verma, S., & Kalyanaraman, V. (2016). Blockchain technology: Beyond bitcoin. Applied Innovation, 2, 6–10.

    Article  Google Scholar 

  • Dolgui, A., & Proth, J. M. (2010). Supply chain engineering: Useful methods and techniques. London: Springer.

    Book  Google Scholar 

  • Dolgui, A., Ivanov, D., & Rozhkov, M. (2019a). 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, in press.

    Google Scholar 

  • Dolgui, A., Ivanov, D., Sethi, S., & Sokolov, B. (2019b). Scheduling in production, supply chain and Industry 4.0 systems by optimal control: Fundamentals, state-of-the-art, and applications. International Journal of Production Research, 57(2), 411–432.

    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), 414–430.

    Article  Google Scholar 

  • Dolgui, A., Ivanov, D., Potryasaev, S., Sokolov, B., Ivanova, M., & Werner, F. (2019c). Blockchain-oriented dynamic modelling of smart contract design and execution control in the supply chain. International Journal of Production Research, in press.

    Google Scholar 

  • Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., Roubaud, D., & Foropon, C. (2019). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1582820.

  • Dunke, F., Heckmann, I., Nickel, S., & Saldanha-da-Gama, F. (2018). Time traps in supply chains: I optimal still good enough? European Journal of Operational Research, 264, 813–829.

    Article  Google Scholar 

  • Elluru, S., Gupta, H., Kaur, H., & Singh, S.P. (2017). Proactive and reactive models for disaster resilient supply chain. Annals of Operations Research, published online.

    Google Scholar 

  • Fazili, M., Venkatadri, U., Cyrus, P., & Tajbakhsh, M. (2017). Physical internet, conventional and hybrid logistic systems: A routing optimisation-based comparison using the Eastern Canada road network case study. International Journal of Production Research, 55(9), 2703–2730.

    Article  Google Scholar 

  • Feldmann, K., & Pumpe, A. (2017). A holistic decision framework for 3D printing investments in global supply chains. Transportation Research Procedia, 25, 677–694.

    Article  Google Scholar 

  • Frazzon, E. M., Kück, M., & Freitag, M. (2018). Data-driven production control for complex and dynamic manufacturing systems. CIRP Annals–Manufacturing Technology, 67(1), 515–518.

    Article  Google Scholar 

  • Gunasekaran, A., Tiwari, M. K., Dubey, R., & Wamba, S. F. (2016). Big data and predictive analytics applications in supply chain management. Computers & Industrial Engineering, 101, 525–527.

    Article  Google Scholar 

  • Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., et al. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308–317.

    Article  Google Scholar 

  • Gunasekaran, A., Yusuf, Y. Y., Adeleye, E. O., & Papadopoulos, T. (2018). Agile manufacturing practices: The role of big data and business analytics with multiple case studies. International Journal of Production Research, 56(1–2), 382–397.

    Google Scholar 

  • Hagberg, J., Sundstrom, M., & Egels-Zandén, N. (2016). The digitalization of retailing: An exploratory framework. International Journal of Retail & Distribution Management, 44(7), 694–712.

    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. https://doi.org/10.1016/j.omega.2018.08.008.

  • Hofmann, E. Strewe, U.M, & Bosia, N. (2018). Supply chain finance and blockchain technology. Springer International.

    Google Scholar 

  • Holmström, J., & Gutowski, T. (2017). Additive manufacturing in operations and supply chain management: No sustainability benefit or virtuous knock-on opportunities? Journal of Industrial Ecology, 21(1), 21–24.

    Article  Google Scholar 

  • IBM. (2017). Retrieved November 20, 2017, from https://www-03.ibm.com/press/us/en/pressrelease/50816.wss.

  • Ivanov, D. (2018a). 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. (2018b). Structural dynamics and resilience in supply chain risk management. New York: Springer.

    Book  Google Scholar 

  • Ivanov, D. (2018c). Managing risks in supply chains with digital twins and simulation. Retrieved from https://www.anylogistix.com/resources/white-papers/managing-risks-in-supply-chains-with-digital-twins/.

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

  • Ivanov, D., Dolgui, A., & Sokolov, B. (2013). Multi-disciplinary analysis of interfaces “Supply Chain Event Management – RFID – Control Theory”. International Journal of Integrated Supply Management, 8, 52–66.

    Google Scholar 

  • Ivanov D., & Rozhkov M. (2017). 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, published online.

    Google Scholar 

  • Ivanov, D., Sokolov B., & Kaeschel J. (2010) A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations. European Journal of Operational Research, 200(2), 409–420.

    Google Scholar 

  • Ivanov, D., Sokolov, B., & Dilou Raguinia, E. A. (2014a). Integrated dynamic scheduling of material flows and distributed information services in collaborative cyber-physical supply networks. International Journal of Systems Science: Operations & Logistics, 1(1), 18–26.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ivanov, D., Sokolov, B., & Pavlov, A. (2014c). 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., Dolgui, A., Werner, F., & Ivanova, M. (2016). A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. International Journal of Production Research, 54(2), 386–402.

    Google Scholar 

  • Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2017). Literature review on disruption recovery in the supply chain. International Journal of Production Research, 55(20), 6158–6174.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Ivanov D., Tsipoulanidis, A., & Schönberger, J. (2019b). Global supply chain and operations management: A decision-oriented introduction into the creation of value (2nd ed.). Cham: Springer Nature.

    Google Scholar 

  • Johnson, K., Lee, A. B. H., & Simchi-Levi, D. (2016). Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing and Service Operations Management., 18(1), 69–85.

    Article  Google Scholar 

  • Khajavi, S. H., Partanen, J., & Holmström, J. (2014). Additive manufacturing in the spare parts supply chain. Computers in Industry, 65(1), 50–63.

    Article  Google Scholar 

  • Kshetri, N. (2018). Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80–89.

    Article  Google Scholar 

  • Levalle, R. R., & Nof, S. Y. (2017). Resilience in supply networks: Definition, dimensions, and levels. Annual Reviews in Control, 43, 224–236.

    Article  Google Scholar 

  • Li, J., Jia, G., Cheng, Y., & Hu, Y. (2017). Additive manufacturing technology in spare parts supply chain: A comparative study. International Journal of Production Research, 55(5), 1498–1515.

    Article  Google Scholar 

  • Liao, Y., Deschamps, Y., de Freitas, E., Loures R., & Ramos, L.F.P. (2017). Past, present and future of industry 4.0–a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609–3629.

    Google Scholar 

  • Minner S., Battini D., & Çelebi D. (2018). Innovations in production economics. International Journal of Production Economics, https://doi.org/10.1016/j.ijpe.2017.10.017.

    Article  Google Scholar 

  • Moghaddam, M., & Nof, S. Y. (2018). Collaborative service-component integration in cloud manufacturing. International Journal of Production Research, 56(1–2), 676–691.

    Google Scholar 

  • Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254–264.

    Article  Google Scholar 

  • Oesterreich, T. D., & Teuteberg, F. (2016). Understanding the implications of digitisation and automation in the context of industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 83, 121–139.

    Article  Google Scholar 

  • Panetto, H., Iung, B., Ivanov, D., Weichhart, G., & Wang, X. (2019). Challenges for the cyber-physical manufacturing enterprises of the future. Annual Reviews in Control. https://doi.org/10.1016/j.arcontrol.2019.02.002.

  • 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.

    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.

    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.

  • Porter M.E., & Heppelmann, J.E. (2015). How smart, connected products are transforming companies. Harvard Business Review.

    Google Scholar 

  • Priore, P., Ponte, B., Rosillo R. & de la Fuente, D. (2018). Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments. International Journal of Production Research, https://doi.org/10.1080/00207543.2018.1552369.

  • Qu, T., Thürer, M., Wang, J., Wang, Z., Fu, H., Li, C., et al. (2017). System dynamics analysis for an internet-of-things-enabled production logistics system. International Journal of Production Research, 55(9), 2622–2649.

    Article  Google Scholar 

  • Reddy, G.R., Singh, H., & Hariharan, S. (2016): Supply chain wide transformation of traditional industry to industry 4.0. Journal of Engineering and Applied Sciences, 11(18), 11089–11097.

    Google Scholar 

  • Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2018). Blockchain technology and supply chain management. International Journal of Production Research, https://doi.org/10.1080/00207543.2018.1533261.

    Article  Google Scholar 

  • Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26–48.

    Article  Google Scholar 

  • Schlüter, F., Hetterscheid, E., & Henke, M. (2017). A simulation-based evaluation approach for digitalization scenarios in smart supply chain risk management. Journal of Industrial Engineering and Management Science, 1, 179–206.

    Article  Google Scholar 

  • Sheffi, Y. (2015). Preparing for disruptions through early detection. MIT Sloan Management Review, 57, 31.

    Google Scholar 

  • Simchi-Levi, D., & Wu, M. X. (2018). Powering retailers digitization through analytics and automation. International Journal of Production Research, 56(1–2), 809–816.

    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 

  • Strozzi, F., Colicchia, C., Creazza, A., & Noè, C. (2017). Literature review on the ‘smart factory’ concept using bibliometric tools. International Journal of Production Research, 55(22), 6572–6591.

    Article  Google Scholar 

  • Tran-Dang, H., Krommenacker, N., & Charpentier, P. (2017). Containers monitoring through the physical internet: A spatial 3D model based on wireless sensor networks. International Journal of Production Research, 55(9), 2650–2663.

    Article  Google Scholar 

  • Tupa, J., Simota, J., & Steiner, F. (2017). Aspects of risk management implementation for Industry 4.0. Procedia Manufacturing, 11, 1223–1230.

    Google Scholar 

  • Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, Elsevier, 165, 234–246.

    Article  Google Scholar 

  • Wamba, S. F., Ngai, E. W. T., Riggins, F., & Akter, S. (2017). Transforming operations and production management using big data and business analytics: Future research directions. International Journal of Operations & Production Management, 37(1), 2–9.

    Article  Google Scholar 

  • Yang, Y., Pan, S., & Ballot, E. (2017). Innovative vendor-managed inventory strategy exploiting interconnected logistics services in the physical internet. International Journal of Production Research, 55(9), 2685–2702.

    Article  Google Scholar 

  • Zelbst, P. J., Green, K. W., Sower, V. E., & Reyes, P. M. (2012). Impact of RFID on manufacturing effectiveness and efficiency. International Journal of Operations & Production Management, 32(3), 329–350.

    Article  Google Scholar 

  • Zhang, J., & Jung, Y. (2018). Additive manufacturing. Oxford: Elsevier Science & Technology.

    Google Scholar 

  • Zhong, R. Y., Xu, C., Chen, C., & Huang, G. Q. (2017). Big data analytics for physical internet-based intelligent manufacturing shop floors. International Journal of Production Research, 55(9), 2610–2621.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Business and Economics, Berlin School of Economics and Law, 10825, Berlin, Germany

    Dmitry Ivanov

  2. IMT Atlantique, LS2N, CNRS, La Chantrerie, 4, rue Alfred Kastler, 44300, Nantes, France

    Alexandre Dolgui

  3. Narendra Paul Loomba Department of Management, Zicklin School of Business, CUNY-Baruch, One Bernard Baruch Way, New York, NY, 10010, USA

    Ajay Das

  4. Saint Petersburg Institute for Informatics and Automation of the RAS (SPIIRAS), V.O. 14 Line, 39 199178, St. Petersburg, Russia

    Boris Sokolov

Authors
  1. Dmitry Ivanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexandre Dolgui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ajay Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Boris Sokolov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dmitry Ivanov .

Editor information

Editors and Affiliations

  1. Department of Business and Economics, Berlin School of Economics and Law, Berlin, Germany

    Dmitry Ivanov

  2. Department of Automation, Production and Computer Sciences, IMT Atlantique, LS2N - CNRS UMR, Nantes, France

    Alexandre Dolgui

  3. SPIIRAS, St. Petersburg, Russia

    Boris Sokolov

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ivanov, D., Dolgui, A., Das, A., Sokolov, B. (2019). Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility. In: Ivanov, D., Dolgui, A., Sokolov, B. (eds) Handbook of Ripple Effects in the Supply Chain. International Series in Operations Research & Management Science, vol 276. Springer, Cham. https://doi.org/10.1007/978-3-030-14302-2_15

Download citation

Publish with us

Policies and ethics

Search

Navigation