Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Advancements and challenges of digital twins in industry

Nature Computational Science volume 4, pages 169–177 (2024)Cite this article

Subjects

Abstract

Digital twins, which are considered an effective approach to realize the fusion between virtual and physical spaces, have attracted a substantial amount of attention in the past decade. With their rapid development in recent years, digital twins have been applied in various fields, particularly in industry. However, there are still some gaps to be filled and some limitations to be addressed. Here we provide a brief overview of digital twin advancements in industry and highlight the main pitfalls to avoid and challenges to overcome, to improve the maturity of digital twins and facilitate large-scale industrial applications in the future.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Digital twin conceptual models.
Fig. 2: Digital twin applications in industry from the perspective of product life cycle and shop floor life cycle.

Similar content being viewed by others

References

  1. Tao, F. & Qi, Q. Make more digital twins. Nature 573, 490–491 (2019).

    Article  Google Scholar 

  2. Lei, Z. et al. Digital twin based monitoring and control for DC–DC converters. Nat. Commun. 14, 5604 (2023).

    Article  Google Scholar 

  3. Ricondo, I., Porto, A. & Ugarte, M. A digital twin framework for the simulation and optimization of production systems. Procedia CIRP 104, 762–767 (2021).

    Article  Google Scholar 

  4. Hernandez-Boussard, T. et al. Digital twins for predictive oncology will be a paradigm shift for precision cancer care. Nat. Med. 27, 2065–2066 (2021).

    Article  Google Scholar 

  5. Coorey, G., Figtree, G. A., Fletcher, D. F. & Redfern, J. The health digital twin: advancing precision cardiovascular medicine. Nat. Rev. Cardiol. 18, 803–804 (2021).

    Article  Google Scholar 

  6. Coorey, G. et al. The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field. npj Digit. Med. 5, 126 (2022).

    Article  Google Scholar 

  7. Shrivastava, C., Berry, T., Cronje, P., Schudel, S. & Defraeye, T. Digital twins enable the quantification of the trade-offs in maintaining citrus quality and marketability in the refrigerated supply chain. Nat. Food 3, 413–427 (2022).

    Article  Google Scholar 

  8. Schrotter, G. & Hürzeler, C. The digital twin of the city of Zurich for urban planning. J. Photogramm. Remote Sens. Geoinf. Sci. 88, 99–112 (2020).

    Google Scholar 

  9. Aydemir, H., Zengin, U. & Durak, U. The digital twin paradigm for aircraft review and outlook. In AIAA SciTech Forum 0553 (AIAA, 2020).

  10. Coraddu, A. et al. Data-driven ship digital twin for estimating the speed loss caused by the marine fouling. Ocean Eng. 186, 106063 (2019).

    Article  Google Scholar 

  11. Mauro, F. & Kana, A. A. Digital twin for ship life-cycle: a critical systematic review. Ocean Eng. 269, 113479 (2023).

    Article  Google Scholar 

  12. Bauer, P., Stevens, B. & Hazeleger, W. A digital twin of Earth for the green transition. Nat. Clim. Change 11, 80–83 (2021).

    Article  Google Scholar 

  13. Uhlemann, T. H.-J., Lehmann, C. & Steinhilper, R. The digital twin: realizing the cyber-physical production system for Industry 4.0. Procedia CIRP 61, 335–340 (2017).

    Article  Google Scholar 

  14. Wang, K. et al. A review of the technology standards for enabling digital twin. Digit. Twin 2, 4 (2022).

    Article  Google Scholar 

  15. Lo, C. K., Chen, C. H. & Zhong, R. Y. A review of digital twin in product design and development. Adv. Eng. Inform. 48, 101297 (2021).

    Article  Google Scholar 

  16. Pei, F. Q., Tong, Y. F., Yuan, M. H., Ding, K. & Chen, X. H. The digital twin of the quality monitoring and control in the series solar cell production line. J. Manuf. Syst. 59, 127–137 (2021).

    Article  Google Scholar 

  17. Magnanini, M. C. & Tolio, T. A. M. A model-based digital twin to support responsive manufacturing systems. CIRP Ann. 70, 353–356 (2021).

    Article  Google Scholar 

  18. Toothman, M. et al. A digital twin framework for prognostics and health management. Comput. Ind. 150, 103948 (2023).

    Article  Google Scholar 

  19. Michael, G. & Vickers, J. in Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches (eds Kahlen, F. J. et al.) 85–113 (Springer, 2017).

  20. Tao, F. et al. Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 94, 3563–3576 (2018).

    Article  Google Scholar 

  21. Negri, E., Berardi, S., Fumagalli, L. & Macchi, M. MES-integrated digital twin frameworks. J. Manuf. Syst. 56, 58–71 (2020).

    Article  Google Scholar 

  22. Negri, E. et al. Field-synchronized digital twin framework for production scheduling with uncertainty. J. Intell. Manuf. 32, 1207–1228 (2021).

    Article  Google Scholar 

  23. Souza, V., Cruz, R., Silva, W., Lins, S. & Lucena, V. A digital twin architecture based on the Industrial Internet of Things technologies. In 2019 IEEE International Conference on Consumer Electronics (ICCE) 1–2 (IEEE, 2019).

  24. Gopal, L. et al. Digital twin and IOT technology for secure manufacturing systems. Meas. Sens. 25, 100661 (2023).

    Article  Google Scholar 

  25. Redelinghuys, A. J. H., Basson, A. H. & Kruger, K. A six-layer architecture for the digital twin: a manufacturing case study implementation. J. Intell. Manuf. 31, 1383–1402 (2020).

    Article  Google Scholar 

  26. Ghosh, A. K., Ullah, A. S., Teti, R. & Kubo, A. Developing sensor signal-based digital twins for intelligent machine tools. J. Ind. Inf. Integr. 24, 100242 (2021).

    Google Scholar 

  27. Cai, Y., Starly, B., Cohen, P. & Lee, Y.-S. Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing. Procedia Manuf. 10, 1031–1042 (2017).

    Article  Google Scholar 

  28. Shahriar, M. R. et al. MTComm based virtualization and integration of physical machine operations with digital-twins in cyber-physical manufacturing cloud. In 2018 5th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2018 4th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom) 46–51 (IEEE, 2018).

  29. Wang, K. J., Lee, Y. H. & Angelica, S. Digital twin design for real-time monitoring—a case study of die cutting machine. Int. J. Prod. Res. 59, 6471–6485 (2021).

    Article  Google Scholar 

  30. Luo, W., Hu, T., Zhang, C. & Wei, Y. Digital twin for CNC machine tool: modeling and using strategy. J. Ambient Intell. Hum. Comput. 10, 1129–1140 (2019).

    Article  Google Scholar 

  31. Urbina Coronado, P. D. et al. Part data integration in the shop floor digital twin: mobile and cloud technologies to enable a manufacturing execution system. J. Manuf. Syst. 48, 25–33 (2018).

    Article  Google Scholar 

  32. Bao, J., Guo, D., Li, J. & Zhang, J. The modelling and operations for the digital twin in the context of manufacturing. Enterp. Inf. Syst. 13, 534–556 (2019).

    Article  Google Scholar 

  33. Koulouris, A., Misailidis, N. & Petrides, D. Applications of process and digital twin models for production simulation and scheduling in the manufacturing of food ingredients and products. Food Bioprod. Process. 126, 317–333 (2021).

    Article  Google Scholar 

  34. Melesse, T. Y., Pasquale, V. D. & Riemma, S. Digital twin models in industrial operations: state-of-the-art and future research directions. IET Collab. Intell. Manuf. 3, 37–47 (2021).

    Article  Google Scholar 

  35. Tao, F., Xiao, B., Qi, Q., Cheng, J. & Ji, P. Digital twin modeling. J. Manuf. Syst. 64, 372–389 (2022).

    Article  Google Scholar 

  36. Rasheed, A., San, O. & Kvamsdal, T. Digital twin: values, challenges and enablers from a modeling perspective. IEEE Access 8, 21980–22012 (2020).

    Article  Google Scholar 

  37. Hürkamp, A. et al. Combining simulation and machine learning as digital twin for the manufacturing of overmolded thermoplastic composites. J. Manuf. Mater. Process 4, 92 (2020).

    Google Scholar 

  38. Tripura, T., Desai, A. S., Adhikari, S. & Chakraborty, S. Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems. Comput. Struct. 281, 107008 (2023).

    Article  Google Scholar 

  39. Balu, A., Sarkar, S., Ganapathysubramanian, B. & Krishnamurthy, A. Physics-aware machine learning surrogates for real-time manufacturing digital twin. Manuf. Lett. 34, 71–74 (2022).

    Article  Google Scholar 

  40. Tabar, R. S., Wärmefjord, K., Söderberg, R. & Lindkvist, L. Efficient spot welding sequence optimization in a geometry assurance digital twin. J. Mech. Des. 142, 102001 (2020).

    Article  Google Scholar 

  41. Namiot, D., Pokusaev, O., Kupriyanovsky, V. & Zhabitskii, M. Digital twins and discrete-event simulation systems. Int. J. Open Inf. Technol. 9, 70–75 (2021).

    Google Scholar 

  42. Morabito, L., Ippolito, M., Pastore, E., Alfieri, A. & Montagna, F. A discrete event simulation based approach for digital twin implementation. IFAC Pap. 54, 414–419 (2021).

    Google Scholar 

  43. Ganguli, R. & Adhikari, S. The digital twin of discrete dynamic systems: initial approaches and future challenges. Appl. Math. Model. 77, 1110–1128 (2020).

    Article  MathSciNet  Google Scholar 

  44. Söderberg, R., Wärmefjord, K., Carlson, J. S. & Lindkvist, L. Toward a digital twin for real-time geometry assurance in individualized production. CIRP Ann. 66, 137–140 (2017).

    Article  Google Scholar 

  45. Yan, Q., Wang, H. & Wu, F. Digital twin-enabled dynamic scheduling with preventive maintenance using a double-layer Q-learning algorithm. Comput. Oper. Res. 144, 105823 (2022).

    Article  Google Scholar 

  46. Guo, X., Peng, G. & Meng, Y. A modified Q-learning algorithm for t path planning in a digital twin assembly system. Int. J. Adv. Manuf. Technol. 119, 3951–3961 (2022).

    Article  Google Scholar 

  47. Chen, R., Shen, H. & Lai, Y. A metaheuristic optimization algorithm for energy efficiency in digital twins. Internet Things Cyber Phys. Syst. 2, 159–169 (2022).

    Article  Google Scholar 

  48. Bazaz, S. M., Lohtander, M. & Varis, J. The prediction method of tool life on small lot turning process—development of digital twin for production. Procedia Manuf. 51, 288–295 (2020).

    Article  Google Scholar 

  49. Zhang, H., Qi, Q., Ji, W. & Tao, F. An update method for digital twin multi-dimension models. Robot Comput. Integr. Manuf. 80, 102481 (2023).

    Article  Google Scholar 

  50. Eckhart, M. & Ekelhart, A. A specification-based state replication approach for digital twins. In Proc. 2018 Workshop on Cyber-Physical Systems Security and Privacy 36–47 (Association for Computing Machinery, 2018).

  51. Akbarian, F., Fitzgerald, E. & Kihl, M. Synchronization in digital twins for industrial control systems. Preprint at https://arxiv.org/abs/2006.03447 (2020).

  52. Seok, M. G., Tan, W. J., Cai, W. & Park, D. Digital-twin consistency checking based on observed timed events with unobservable transitions in smart manufacturing. IEEE Trans. Ind. Inform. 19, 6208–6219 (2023).

    Article  Google Scholar 

  53. Talkhestani, B. A., Jazdi, N., Schloegl, W. & Weyrich, M. Consistency check to synchronize the digital twin of manufacturing automation based on anchor points. Procedia CIRP 72, 159–164 (2018).

    Article  Google Scholar 

  54. Huang, S., Wang, G., Lei, D. & Yan, Y. Toward digital validation for rapid product development based on digital twin: a framework. Int. J. Adv. Manuf. Technol. 119, 2509–2523 (2022).

    Article  Google Scholar 

  55. Qamsane, Y. et al. A unified digital twin framework for real-time monitoring and evaluation of smart manufacturing systems. In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) 1394–1401 (IEEE, 2019).

  56. Agostino, Í. R. S., Broda, E., Frazzon, E. M. & Freitag, M. in Scheduling in Industry 4.0 and Cloud Manufacturing (eds Sokolov, B. et al.) 39–60 (Springer, 2020); https://doi.org/10.1007/978-3-030-43177-8_3

  57. Aheleroff, S., Xu, X., Zhong, R. Y. & Lu, Y. Digital twin as a service (DTaaS) in Industry 4.0: an architecture reference model. Adv. Eng. Inform. 47, 101225 (2021).

    Article  Google Scholar 

  58. Fang, Y. et al. Digital-twin-based job shop scheduling toward smart manufacturing. IEEE Trans. Ind. Inform. 15, 6425–6435 (2019).

    Article  Google Scholar 

  59. Liu, D., Du, Y., Chai, W., Lu, C. & Cong, M. Digital twin and data-driven quality prediction of complex die-casting manufacturing. IEEE Trans. Ind. Inform. 18, 8119–8128 (2022).

    Article  Google Scholar 

  60. Aivaliotis, P., Georgoulias, K. & Chryssolouris, G. The use of digital twin for predictive maintenance in manufacturing. Int. J. Comput. Integr. Manuf. 32, 1067–1080 (2019).

    Article  Google Scholar 

  61. Errandonea, I., Beltrán, S. & Arrizabalaga, S. Digital twin for maintenance: a literature review. Comput. Ind. 123, 103316 (2020).

    Article  Google Scholar 

  62. van Dinter, R., Tekinerdogan, B. & Catal, C. Predictive maintenance using digital twins: a systematic literature review. Inf. Softw. Technol. 151, 107008 (2022).

    Article  Google Scholar 

  63. Ribeiro da Silva, E., Assad Neto, A. & Nielsen, C. P. in The Future of Smart Production for SMEs: A Methodological and Practical Approach Towards Digitalization in SMEs (eds Madsen, O. et al.) 343–348 (Springer, 2023).

  64. Wanasinghe, T. R. et al. Digital twin for the oil and gas industry: overview, research trends, opportunities, and challenges. IEEE Access 8, 104175–104197 (2020).

    Article  Google Scholar 

  65. Faraway, J. J. & Augustin, N. H. When small data beats big data. Stat. Probab. Lett. 136, 142–145 (2018).

    Article  MathSciNet  Google Scholar 

  66. Yu, J., Song, Y., Tang, D. & Dai, J. A digital twin approach based on nonparametric Bayesian network for complex system health monitoring. J. Manuf. Syst. 58, 293–304 (2021).

    Article  Google Scholar 

  67. Dang, S. et al. What should 6G be? Nat. Electron. 3, 20–29 (2020).

    Article  Google Scholar 

  68. Saad, W., Bennis, M. & Chen, M. A vision of 6G wireless systems: applications, trends, technologies, and open research problems. IEEE Netw. 34, 134–142 (2020).

    Article  Google Scholar 

  69. Priyanka, E. B., Thangavel, S., Gao, X.-Z. & Sivakumar, N. S. Digital twin for oil pipeline risk estimation using prognostic and machine learning techniques. J. Ind. Inf. Integr. 26, 100272 (2022).

    Google Scholar 

  70. Chen, Z., Zou, J. & Wang, W. Digital twin-oriented collaborative optimization of fuzzy flexible job shop scheduling under multiple uncertainties. Sādhanā 48, 78 (2023).

    Article  MathSciNet  Google Scholar 

  71. Singh, R. & Gill, S. S. Edge AI: a survey. Internet Things Cyber Phys. Syst. 3, 71–92 (2023).

    Article  Google Scholar 

  72. Olortegui-Yume, J. A. & Kwon, P. Y. Tool wear mechanisms in machining. Int. J. Mach. Mach. Mater. 2, 316–334 (2007).

    Google Scholar 

  73. Suo, S. et al. Encryption technology in information system security. In 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019) 495–499 (Springer, 2019).

  74. Darabseh, A. et al. SDStorage: a software defined storage experimental framework. In 2015 IEEE International Conference on Cloud Engineering 341–346 (IEEE, 2015).

  75. Gu, M., Li, X. & Cao, Y. Optical storage arrays: a perspective for future big data storage. Light. Sci. Appl. 3, e177 (2014).

    Article  Google Scholar 

  76. Lv, Z. & Xie, S. Artificial intelligence in the digital twins: state of the art, challenges, and future research topics. Digit. Twin 1, 12 (2022).

    Article  Google Scholar 

  77. Carabantes, M. Black-box artificial intelligence: an epistemological and critical analysis. AI Soc. 35, 309–317 (2020).

    Article  Google Scholar 

  78. Blazek, P. J. & Lin, M. M. Explainable neural networks that simulate reasoning. Nat. Comput. Sci. 1, 607–618 (2021).

    Article  Google Scholar 

  79. Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).

    Article  Google Scholar 

  80. Gunning, D. et al. XAI—explainable artificial intelligence. Sci. Robot. 4, eaay7120 (2019).

    Article  Google Scholar 

  81. Khan, A., Shahid, F., Maple, C., Ahmad, A. & Jeon, G. Toward smart manufacturing using spiral digital twin framework and twinchain. IEEE Trans. Ind. Inform. 18, 1359–1366 (2022).

    Article  Google Scholar 

  82. Suhail, S. et al. Blockchain-based digital twins: research trends, issues, and future challenges. ACM Comput. Surv. 54, 240:1–240:34 (2022).

    Article  Google Scholar 

  83. Yaqoob, I. et al. Blockchain for digital twins: recent advances and future research challenges. IEEE Netw. 34, 290–298 (2020).

    Article  Google Scholar 

  84. Tao, F. et al. makeTwin: a reference architecture for digital twin software platform. Chin. J. Aeronaut. https://doi.org/10.1016/j.cja.2023.05.002 (2023).

    Article  Google Scholar 

  85. Niederer, S. A. et al. Scaling digital twins from the artisanal to the industrial. Nat. Comput. Sci. 1, 313–320 (2021).

    Article  Google Scholar 

  86. Suhail, S., Jurdak, R. & Hussain, R. Security attacks and solutions for digital twins. Preprint at https://doi.org/10.48550/arXiv.2202.12501 (2023).

Download references

Acknowledgements

This work is financially supported by the National Natural Science Foundation of China (NSFC) under grants 52120105008 and 52275471, the National Key Research and Development Program of China under grant 2020YFB1708400, and the New Cornerstone Science Foundation through the XPLORER PRIZE.

Author information

Authors and Affiliations

  1. Digital Twin International Research Center, International Research Institute for Multidisciplinary Science, Beihang University, Beijing, China

    Fei Tao

  2. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

    Fei Tao, He Zhang & Chenyuan Zhang

  3. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China

    Fei Tao

  4. Tianmushan Laboratory, Hangzhou, China

    Fei Tao & He Zhang

Authors
  1. Fei Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. He Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chenyuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the writing and editing of this paper.

Corresponding author

Correspondence to Fei Tao.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Computational Science thanks Michael Grieves, George Q. Huang and Jay Lee for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.

Additional information

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tao, F., Zhang, H. & Zhang, C. Advancements and challenges of digital twins in industry. Nat Comput Sci 4, 169–177 (2024). https://doi.org/10.1038/s43588-024-00603-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-024-00603-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics