Skip to main content
Springer Nature Link
Log in

Digital Twin for Industrial Applications – A Literature Review

  • Conference paper
  • First Online:
Transfer, Diffusion and Adoption of Next-Generation Digital Technologies (TDIT 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 699))

Included in the following conference series:

  • 772 Accesses

Abstract

The development of technology such as big data, internet of things, cloud, 5G, artificial intelligence plays a significant impact on industries. This promotes the integration of physical and digital worlds and led to the growth of Digital Twins. Digital Twin is the virtual representation of the physical entity that spans its lifecycle, performs simulations and helps in decision making. In this paper, we will study the applications of digital twins in various industries. A systematic literature review is conducted by analyzing the literature from 2017 to 2023. The findings are the applications of digital twins in various industries like agricultural, healthcare, smart cities, automotive, infrastructure, energy and transport. The article concludes by highlighting the conclusions and challenges of the technology. Our study offers insights into how Digital Twin technology plays vital role in shaping various industries and challenges that must be overcome for their widespread adoption.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Liu, M., Fang, S., Dong, H., Xu, C.: Review of DT about concepts, technologies, and industrial applications. J. Manuf. Syst. 58, 346–361 (2021)

    Article  Google Scholar 

  2. Grieves, M.: DT: manufacturing excellence through virtual factory replication. White Paper 1(2014), 1–7 (2014)

    Google Scholar 

  3. Grieves, M., Vickers, J.: DT: mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary perspectives on complex systems: New findings and approaches, pp. 85–113 (2017)

    Google Scholar 

  4. Fang, X., Wang, H., Liu, G., Tian, X., Ding, G., Zhang, H.: Industry application of DT: from concept to implementation. Int. J. Adv. Manuf. Technol. 121(7–8), 4289–4312 (2022)

    Article  Google Scholar 

  5. Gao, Y., Chang, D., Chen, C.H.: A DT-based approach for optimizing operation energy consumption at automated container terminals. J. Clean. Prod. 385, 135782 (2023)

    Article  Google Scholar 

  6. Barricelli, B.R., Casiraghi, E., Fogli, D.: A survey on DT: definitions, characteristics, applications, and design implications. IEEE Access 7, 167653–167671 (2019)

    Article  Google Scholar 

  7. Grover, P., Kar, A.K., Dwivedi, Y.: The evolution of social media influence-a literature review and research agenda. Int. J. Inf. Manage. Data Insights 2(2), 100116 (2022)

    Google Scholar 

  8. Deepu, T.S., Ravi, V.: A review of literature on implementation and operational dimensions of supply chain digitalization: framework development and future research directions. Int. J. Inf. Manage. Data Insights 3(1), 100156 (2023)

    Google Scholar 

  9. Kar, A.K., Navin, L.: Diffusion of blockchain in insurance industry: an analysis through the review of academic and trade literature. Telematics Inform. 58, 101532 (2021)

    Article  Google Scholar 

  10. Kar, A.K., Varsha, P.S.: Unravelling the techno-functional building blocks of Metaverse ecosystems–a review and research agenda. Int. J. Inf. Manage. Data Insights, 100176 (2023)

    Google Scholar 

  11. Votto, A.M., Valecha, R., Najafirad, P., Rao, H.R.: Artificial intelligence in tactical human resource management: a systematic literature review. Int. J. Inf. Manage. Data Insights 1(2), 100047 (2021)

    Google Scholar 

  12. Singh, V., Chen, S.S., Singhania, M., Nanavati, B., Gupta, A.: How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–a review and research agenda. Int. J. Inf. Manage. Data Insights 2(2), 100094 (2022)

    Google Scholar 

  13. Kushwaha, A.K., Kar, A.K., Dwivedi, Y.K.: Applications of big data in emerging management disciplines: a literature review using text mining. Int. J. Inform. Manage. Data Insights 1(2), 100017 (2021)

    Google Scholar 

  14. Jones, D., Snider, C., Nassehi, A., Yon, J., Hicks, B.: Characterising the DT: a systematic literature review. CIRP J. Manuf. Sci. Technol. 29, 36–52 (2020)

    Article  Google Scholar 

  15. Deon, B., et al.: DT and machine learning for decision support in thermal power plant with combustion engines. Knowl.-Based Syst.Based Syst. 253, 109578 (2022)

    Article  Google Scholar 

  16. Alves, R.G., Maia, R.F., Lima, F.: Development of a DT for smart farming: irrigation management system for water saving. J. Clean. Prod., 135920 (2023)

    Google Scholar 

  17. Pylianidis, C., Osinga, S., Athanasiadis, I.N.: Introducing DTs to agriculture. Comput. Electron. Agric.. Electron. Agric. 184, 105942 (2021)

    Article  Google Scholar 

  18. Pesapane, F., Rotili, A., Penco, S., Nicosia, L., Cassano, E.: DTs in radiology. J. Clin. Med. 11(21), 6553 (2022)

    Article  Google Scholar 

  19. Erol, T., Mendi, A.F., Doğan, D.: The DT revolution in healthcare. In: 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1–7. IEEE, October 2020

    Google Scholar 

  20. Manocha, A., Afaq, Y., Bhatia, M.: DT-assisted Blockchain-inspired irregular event analysis for eldercare. Knowl.-Based Syst..-Based Syst. 260, 110138 (2023)

    Article  Google Scholar 

  21. Ferré-Bigorra, J., Casals, M., Gangolells, M.: The adoption of urban DTs. Cities 131, 103905 (2022)

    Article  Google Scholar 

  22. Hämäläinen, M.: Smart city development with DT technology. In 33rd Bled eConference-Enabling Technology for a Sustainable Society: June 28–29, 2020, Online Conference Proceedings. University of Maribor (2020)

    Google Scholar 

  23. White, G., Zink, A., Codecá, L., Clarke, S.: A DT smart city for citizen feedback. Cities 110, 103064 (2021)

    Article  Google Scholar 

  24. Huang, J., Zhao, L., Wei, F., Cao, B.: The application of DT on power industry. In: IOP Conference Series: Earth and Environmental Science, vol. 647, No. 1, p. 012015. IOP Publishing (2021)

    Google Scholar 

  25. Venkatesh, K.P., Raza, M.M., Kvedar, J.C.: Health DTs as tools for precision medicine: Considerations for computation, implementation, and regulation. npj Digital Med. 5(1), 150 (2022)

    Google Scholar 

  26. Najafi, P., Mohammadi, M., van Wesemael, P., Le Blanc, P.M.: A user-centred virtual city information model for inclusive community design: state-of-art. Cities 134, 104203 (2023)

    Article  Google Scholar 

  27. Hassani, H., Huang, X., MacFeely, S.: Impactful DT in the healthcare revolution. Big Data Cogn. Comput. 6(3), 83 (2022)

    Article  Google Scholar 

  28. Ahmadi, M., Kaleybar, H. J., Brenna, M., Castelli-Dezza, F., Carmeli, M.S.: Adapting DT technology in electric railway power systems. In: 2021 12th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), pp. 1–6. IEEE, February 2021

    Google Scholar 

  29. Sleiti, A.K., Kapat, J.S., Vesely, L.: DT in energy industry: Proposed robust DT for power plant and other complex capital-intensive large engineering systems. Energy Rep. 8, 3704–3726 (2022)

    Article  Google Scholar 

  30. Dhar, S., Tarafdar, P., Bose, I.: Understanding the evolution of an emerging technological paradigm and its impact: the case of DT. Technol. Forecast. Soc. Chang. 185, 122098 (2022)

    Article  Google Scholar 

  31. Rizwan, A., Ahmad, R., Khan, A.N., Xu, R., Kim, D.H.: Intelligent DT for federated learning in AIoT networks. Internet of Things, 100698 (2023)

    Google Scholar 

  32. Ghenai, C., Husein, L.A., Al Nahlawi, M., Hamid, A.K., Bettayeb, M.: Recent trends of DT technologies in the energy sector: a comprehensive review. Sustainable Energy Technol. Assess. 54, 102837 (2022)

    Article  Google Scholar 

  33. You, M., Wang, Q., Sun, H., Castro, I., Jiang, J.: DTs based day-ahead integrated energy system scheduling under load and renewable energy uncertainties. Appl. Energy 305, 117899 (2022)

    Article  Google Scholar 

  34. Meske, C., Osmundsen, K.S., Junglas, I.: Designing and implementing DTs in the energy grid sector. J. Manuf. Sci. Technol. 29, 36–52 (2020)

    Google Scholar 

  35. Schlappa, M., Hegemann, J., Spinler, S.: Optimizing control of waste incineration plants using reinforcement learning and DTs. IEEE Trans. Eng. Manage. (2022)

    Google Scholar 

  36. Erol, T., Mendi, A.F., Doğan, D.: Digital transformation revolution with DT technology. In: 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1–7. IEEE, October 2020

    Google Scholar 

  37. Negri, E., Fumagalli, L., Macchi, M.: A review of the roles of DT in CPS-based production systems. Procedia Manuf. 11, 939–948 (2017)

    Article  Google Scholar 

  38. Mukherjee, T., DebRoy, T.: A DT for rapid qualification of 3D printed metallic components. Appl. Mater. Today 14, 59–65 (2019)

    Article  Google Scholar 

  39. Qi, Q., Tao, F., Zuo, Y., Zhao, D.: DT service towards smart manufacturing. Procedia Cirp 72, 237–242 (2018)

    Article  Google Scholar 

  40. Glaessgen, E., Stargel, D.: The DT paradigm for future NASA and US Air Force vehicles. Inº 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA¬ p. 1818, April 2012

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Management Studies, IIT Delhi, New Delhi, India

    Rashmi Pant Joshi, Surbhi Gulati & Arpan Kumar Kar

Authors
  1. Rashmi Pant Joshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Surbhi Gulati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arpan Kumar Kar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rashmi Pant Joshi .

Editor information

Editors and Affiliations

  1. IIM Nagpur, Nagpur, India

    Sujeet K. Sharma

  2. Swansea University, Swansea, UK

    Yogesh K. Dwivedi

  3. IIM Nagpur, Nagpur, India

    Bhimaraya Metri

  4. University of Bradford, Bradford, UK

    Banita Lal

  5. Royal Holloway, University of London, Egham, UK

    Amany Elbanna

Rights and permissions

Reprints and permissions

Copyright information

© 2024 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Joshi, R.P., Gulati, S., Kar, A.K. (2024). Digital Twin for Industrial Applications – A Literature Review. In: Sharma, S.K., Dwivedi, Y.K., Metri, B., Lal, B., Elbanna, A. (eds) Transfer, Diffusion and Adoption of Next-Generation Digital Technologies. TDIT 2023. IFIP Advances in Information and Communication Technology, vol 699. Springer, Cham. https://doi.org/10.1007/978-3-031-50204-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50204-0_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50203-3

  • Online ISBN: 978-3-031-50204-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships