Abstract

Using data from various Internet of Things (IoT) sensors attached to any object, system, or process, which is integrated by a specialized model, a digital/virtual blueprint of the physical entity can be attained. Such blueprints that can mirror their real-world counterparts in real-time are known as Digital Twins (DTs). These twins heavily rely on diverse forms of data, and the ones that use the Systems Modelling Language (SysML) to streamline the same are referred to as SysML-driven Digital Twins (SysML-drivenDT’s). Such model based twins are revolutionizing industrial processes such as predictive maintenance and operational optimization in plants through a structured, model-based approach to capturing and simulating complex systems. Here we show the technology behind the functioning of a DT, its vast applications and advantages in the industrial world, and ways to boost efficiency and unlock a massive potential, along with some of the limitations regarding its use. This paper explains the integration of visualization and human interaction tools such as Virtual reality(VR); how SysML-driven DT’s can function in various industries, including robotization of product lines, predictive maintenance, monitoring, cost reduction, and minimization of emissions and waste, and the ways in which it is able to benefit industries, along with limitations such as high initial cost and usage difficulty due to technical complexity. In this paper we also discuss how DTs can play a major role in sustainability. The paper also goes over a case study conducted in China related to a manufacturing company and DTs. Our discussions provide a solid overall view on this technology and explains in depth the key aspects of its functioning and usage. The paper highlights that DTs can revolutionize the industrial sector, while acknowledging some of its technical constraints. It demonstrates how DTs have a major scope of implementation and how upgrades like integration with technologies like VR, artifact reuse, interoperability and a green twin model can upgrade the DT concept, making it better

References

1. Melesse, T. Y.; Di Pasquale, V.; Riemma, S. Digital Twin Models in Industrial Operations: State‐Of‐The‐Art and Future Research Directions. IET Collaborative Intelligent Manufacturing 2021. https://doi.org/10.1049/cim2.12010.
2. Attaran, M.; Attaran, S.; Celik, B. G. The Impact of Digital Twins on the Evolution of Intelligent Manufacturing and Industry 4.0. The impact of digital twins on the evolution of intelligent manufacturing and Industry 4.0 2023, 3 (3). https://doi.org/10.1007/s43674-023-00058-y.
3. Singh, M.; Fuenmayor, E.; Hinchy, E. P.; Qiao, Y.; Murray, N.; Devine, D. Digital Twin: Origin to Future. Applied System Innovation 2021, 4 (2), 36. https://doi.org/10.3390/asi4020036.
4. Hassani, H.; Huang, X.; MacFeely, S. Enabling Digital Twins to Support the UN SDGs. Big Data and Cognitive Computing 2022, 6 (4), 115. https://doi.org/10.3390/bdcc6040115.
5. Leng, J.; Wang, D.; Shen, W.; Li, X.; Liu, Q.; Chen, X. Digital Twins-Based Smart Manufacturing System Design in Industry 4.0: A Review. Journal of Manufacturing Systems 2021, 60, 119–137. https://doi.org/10.1016/j.jmsy.2021.05.011.
6. Sala, R.; Francalanza, E.; Arena, S. A Review on Three Decades of Manufacturing Maintenance Research: Past, Present and Future Directions. Production & Manufacturing Research 2025, 13 (1). https://doi.org/10.1080/21693277.2025.2469037.
7. Tao, F.; Zhang, H.; Liu, A.; Nee, A. Y. C. Digital Twin in Industry: State-of-The-Art. IEEE Transactions on Industrial Informatics 2019, 15 (4), 2405–2415. https://doi.org/10.1109/tii.2018.2873186.
8. Camarella, S.; Conway, M. P.; Goering, K.; Huntington, M. Transforming manufacturing with digital twins | McKinsey. www.mckinsey.com. https://www.mckinsey.com/capabilities/operations/our-insights/digital-twins-the-next-frontier-of-factory-optimization.
9. Ba, L.; Tangour, F.; El Abbassi, I.; Absi, R. Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review. Sustainability 2025, 17 (8), 3560. https://doi.org/10.3390/su17083560.
10. Khan, T.; Khan, U.; Khan, A.; Mollan, C.; Morkvenaite-Vilkonciene, I.; Pandey, V. Data-Driven Digital Twin Framework for Predictive Maintenance of Smart Manufacturing Systems. Machines 2025, 13 (6), 481–481. https://doi.org/10.3390/machines13060481.
11. Mihai, S.; Yaqoob, M.; Hung, D. V.; Davis, W.; Towakel, P.; Raza, M.; Karamanoglu, M.; Barn, B.; Shetve, D.; Prasad, R. V.; Venkataraman, H.; Trestian, R.; Nguyen, H. X. Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects. IEEE Communications Surveys & Tutorials 2022, 24 (4), 2255–2291. https://doi.org/10.1109/comst.2022.3208773.
12. Gregory, C.; Mbolamananamalala, R.; Rabah, S.; Chapurlat, V. Model Based Systems Engineering Applied to Digital Twin Engineering: Why and How To? IFAC-PapersOnLine 2024, 58 (19), 157–162. https://doi.org/10.1016/j.ifacol.2024.09.126.
13. Götz, C. S.; Karlsson, P.; Yitmen, I. Exploring Applicability, Interoperability and Integrability of Blockchain-Based Digital Twins for Asset Life Cycle Management. Smart and Sustainable Built Environment 2020, ahead-of-print (ahead-of-print). https://doi.org/10.1108/sasbe-08-2020-0115.
14. Lutfi, M.; Valerdi, R. Integration of SysML and Virtual Reality Environment: A Ground Based Telescope System Example. Systems 2023, 11 (4), 189–189. https://doi.org/10.3390/systems11040189.
15. Ricci, A.; Croatti, A.; Mariani, S.; Montagna, S.; Picone, M. Web of Digital Twins. ACM Transactions on Internet Technology 2022, 22 (4), 1–30. https://doi.org/10.1145/3507909.
16. Singh, M.; Srivastava, R.; Fuenmayor, E.; Kuts, V.; Qiao, Y.; Murray, N.; Devine, D. Applications of Digital Twin across Industries: A Review. Applied Sciences 2022, 12 (11), 5727. https://doi.org/10.3390/app12115727.
17. Jiang, Y.; Yin, S.; Li, K.; Luo, H.; Kaynak, O. Industrial Applications of Digital Twins. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2021, 379 (2207), 20200360. https://doi.org/10.1098/rsta.2020.0360.
18. Hu, W.; Zhang, T.; Deng, X.; Liu, Z.; Tan, J. Digital Twin: A State-of-The-Art Review of Its Enabling Technologies, Applications and Challenges. Journal of Intelligent Manufacturing and Special Equipment 2021, 2 (1), 1–34. https://doi.org/10.1108/jimse-12-2020-010.
19. Huzzat, A.; Anpalagan, A.; Khwaja, A. S.; Woungang, I.; Alnoman, A. A.; Pillai, A. S. A Comprehensive Review of Digital Twin Technologies in Smart Cities. Digital Engineering 2025, 4, 100040. https://doi.org/10.1016/j.dte.2025.100040.
20. Nativi, S.; Mazzetti, P.; Craglia, M. Digital Ecosystems for Developing Digital Twins of the Earth: The Destination Earth Case. Remote Sensing 2021, 13 (11), 2119. https://doi.org/10.3390/rs13112119.
21. Shahzad, M.; Shafiq, M. T.; Douglas, D.; Kassem, M. Digital Twins in Built Environments: An Investigation of the Characteristics, Applications, and Challenges. Buildings 2022, 12 (2), 120. https://doi.org/10.3390/buildings12020120.
22. Wagner, M.; Sousa, F. J. P.; Glatt, M.; Aurich, J. C. Bridging the Gap: A Conceptual Framework for Developing and Operating Hybrid Modeled Digital Twins under Limited Model Input Conditions. Procedia CIRP 2024, 121, 31–36. https://doi.org/10.1016/j.procir.2023.09.226.
23. Klar, R.; Niklas Arvidsson; Angelakis, V. Digital Twins’ Maturity: The Need for Interoperability. IEEE systems journal 2023, 1–12. https://doi.org/10.1109/jsyst.2023.3340422.
24. Haße, H.; van der Valk, H.; Möller, F.; Otto, B. Design Principles for Shared Digital Twins in Distributed Systems. Business & Information Systems Engineering 2022. https://doi.org/10.1007/s12599-022-00751-1.
25. Oberhauser, R. VR-SysML: SysML Model Visualization and Immersion in Virtual Reality. https://opus-htw-aalen.bsz-bw.de/frontdoor/deliver/index/docId/2474/file/MODERNSYS22-OberhauserCR2.pdf (accessed 2025-07-04).
26. Ma, S.; Ding, W.; Liu, Y.; Ren, S.; Yang, H. Digital Twin and Big Data-Driven Sustainable Smart Manufacturing Based on Information Management Systems for Energy-Intensive Industries. Applied Energy 2022, 326 (119986), 119986. https://doi.org/10.1016/j.apenergy.2022.119986.
27. Timperi, M.; Kokkonen, K.; Hannola, L. Digital Twins for Environmentally Sustainable and Circular Manufacturing Sector: Visions from Industry Professionals. Production & Manufacturing Research 2024, 12 (1). https://doi.org/10.1080/21693277.2024.2428249.
28. Barricelli, B. R.; Casiraghi, E.; Fogli, D. A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications. IEEE Access 2019, 7, 167653–167671. https://doi.org/10.1109/access.2019.2953499.
29. Jagla, P.; Jacobs, G.; Siebrecht, J.; Wischmann, S.; Sprehe, J. Using SysML to Support Impact Analysis on Structural Dynamics Simulation Models. Procedia CIRP 2021, 100, 91–96. https://doi.org/10.1016/j.procir.2021.05.015.
30. Wilking, F.; Kaup, S.; Fett, M.; Goetz, S.; Kirchner, E.; Wartzack, S. Utilization of SysML System Models for Smart Assembly Using Digital Twins. Proceedings of NordDesign 2024 2024, 61–70. https://doi.org/10.35199/norddesign2024.7.
31. Hause, M. The OMG Modelling Language (SYSML). https://www.researchgate.net/publication/260359652_The_OMG_Modelling_Language_SYSML.
32. Roehm, B.; Anderl, R.; Schleich, B. Development of an Information Model for Simulation Data Management in the Digital Twin. Procedia CIRP 2023, 119, 681–686. https://doi.org/10.1016/j.procir.2023.03.120.
33. Md. Shezad Dihan; Anwar Islam Akash; Zinat Tasneem; Das, P.; Sajal Kumar Das; Md. Robiul Islam; Md. Manirul Islam; Badal, F. R.; Md. Firoj Ali; Md. Hafiz Ahmed; Sarafat Hussain Abhi; Subrata Kumar Sarker; Md. Mehedi Hasan. Digital Twin: Data Exploration, Architecture, Implementation and Future. Heliyon 2024, 10 (5), e26503–e26503. https://doi.org/10.1016/j.heliyon.2024.e26503.
34. Castilla, M.; Redondo, J. L.; A. Martínez; J.D. Álvarez. Artificial Neural Network-Based Digital Twin for a Flat Plate Solar Collector Field. Engineering Applications of Artificial Intelligence 2024, 133, 108387–108387. https://doi.org/10.1016/j.engappai.2024.108387.
35. David, I.; Shao, G.; Gomes, C.; Tilbury, D.; Zarkout, B. Interoperability of Digital Twins: Challenges, Success Factors, and Future Research Directions. Lecture Notes in Computer Science 2024, 27–46. https://doi.org/10.1007/978-3-031-75390-9_3.
36. Agrawal, A.; Singh, V.; Thiel, R.; Pillsbury, M.; Knoll, H.; Puckett, J. A.; Fischer, M. Digital Twin in Practice: Emergent Insights from an Ethnographic-Action Research Study. Construction Research Congress 2022 2022. https://doi.org/10.1061/9780784483961.131.
37. Ion Barosan; Juan. Integration of SysML Models in a 3D Environment for Virtual Testing and Validation. 2022, 39–45. https://doi.org/10.1145/3531056.3542784.
38. Lutfi, M.; Valerdi, R. Evaluating the Benefits and Drawbacks of Visualizing Systems Modeling Language (SysML) Diagrams in the 3D Virtual Reality Environment. Systems 2025, 13 (4), 221. https://doi.org/10.3390/systems13040221.
39. Oberhauser, R. VR-SysML+Traceability: Immersive Requirements Traceability and Test Traceability with SysML to Support Verification and Validation in Virtual Reality. https://opus-htw-aalen.bsz-bw.de/frontdoor/deliver/index/docId/3257/file/IARIAJ23-VR-SysML_Trace_OberhauserCR.pdf (accessed 2025-07-05).
40. Dembski, F.; Wössner, U.; Letzgus, M.; Ruddat, M.; Yamu, C. Urban Digital Twins for Smart Cities and Citizens: The Case Study of Herrenberg, Germany. Sustainability 2020, 12 (6), 2307. https://doi.org/10.3390/su12062307.
41. Klar, R.; Fredriksson, A.; Angelakis, V. Digital Twins for Ports: Derived from Smart City and Supply Chain Twinning Experience. IEEE Access 2023, 11, 71777–71799. https://doi.org/10.1109/access.2023.3295495.
42. Mylonas, G.; Kalogeras, A.; Kalogeras, G.; Anagnostopoulos, C.; Alexakos, C.; Munoz, L. Digital Twins from Smart Manufacturing to Smart Cities: A Survey. IEEE Access 2021, 1–1. https://doi.org/10.1109/access.2021.3120843.
43. Reiche, L. T.; Gundlach, C. S.; Mewes, G. F.; Fay, A. The Digital Twin of a System: A Structure for Networks of Digital Twins. 2021, 1–8. https://doi.org/10.1109/etfa45728.2021.9613594.
44. Choi, T.-M.; Cai, Y.-J.; Shen, B. Sustainable Fashion Supply Chain Management: A System of Systems Analysis. IEEE Transactions on Engineering Management 2019, 66 (4), 730–745. https://doi.org/10.1109/tem.2018.2857831.
45. Ferré-Bigorra, J.; Casals, M.; Gangolells, M. The Adoption of Urban Digital Twins. Cities 2022, 131, 103905. https://doi.org/10.1016/j.cities.2022.103905.
46. Perno, M.; Hvam, L.; Haug, A. Implementation of Digital Twins in the Process Industry: A Systematic Literature Review of Enablers and Barriers. Computers in Industry 2022, 134, 103558. https://doi.org/10.1016/j.compind.2021.103558.
47. VanDerHorn, E.; Mahadevan, S. Digital Twin: Generalization, Characterization and Implementation. Decision Support Systems 2021, 145 (0167-9236), 113524. https://doi.org/10.1016/j.dss.2021.113524.
48. Wilkinson, M. A. The Copyright Regime and Data Protection Legislation. Ssrn.com. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2079624 (accessed 2025-07-05).
49. Peebles, P. Unicorn Notebook; 2019.
50. EUR-Lex - 52020DC0066 - EN - EUR-Lex. Europa.eu. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52020DC0066.
51. Ferko, E.; Alessio Bucaioni; Pelliccione, P.; Behnam, M. Standardisation in Digital Twin Architectures in Manufacturing. 2023. https://doi.org/10.1109/icsa56044.2023.00015.
52. The Pathway towards an Information Management Framework a “Commons” for Digital Built Britain. https://www.cdbb.cam.ac.uk/files/the_pathway_towards_an_imf.pdf.
53. Melesse, T. Y.; Pasquale, V. D.; Riemma, S. Digital Twin Models in Industrial Operations: A Systematic Literature Review. Procedia Manufacturing 2020, 42, 267–272. https://doi.org/10.1016/j.promfg.2020.02.084.
54. Qi, Q.; Tao, F. Digital Twin and Big Data towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access 2018, 6, 3585–3593. https://doi.org/10.1109/access.2018.2793265.
55. Hananto, A. L.; Tirta, A.; Herawan, S. G.; Idris, M.; Soudagar, M. E. M.; Djamari, D. W.; Veza, I. Digital Twin and 3D Digital Twin: Concepts, Applications, and Challenges in Industry 4.0 for Digital Twin. Computers 2024, 13 (4), 100. https://doi.org/10.3390/computers13040100.
56. Botkina, D.; Hedlind, M.; Olsson, B.; Henser, J.; Lundholm, T. Digital Twin of a Cutting Tool. Procedia CIRP 2018, 72, 215–218. https://doi.org/10.1016/j.procir.2018.03.178.
57. Li, L.; Aslam, S.; Wileman, A.; Perinpanayagam, S. Digital Twin in Aerospace Industry: A Gentle Introduction. IEEE Access 2021, 10, 1–1. https://doi.org/10.1109/access.2021.3136458.
58. Laukotka, F.; Hanna, M.; Krause, D. Digital Twins of Product Families in Aviation Based on an MBSE-Assisted Approach. Procedia CIRP 2021, 100, 684–689. https://doi.org/10.1016/j.procir.2021.05.144.
59. Minerva, R.; Lee, G. M.; Crespi, N. Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models. Proceedings of the IEEE 2020, 108 (10), 1–40. https://doi.org/10.1109/jproc.2020.2998530.
60. Piromalis, D.; Kantaros, A. Digital Twins in the Automotive Industry: The Road toward Physical-Digital Convergence. Applied System Innovation 2022, 5 (4), 65. https://doi.org/10.3390/asi5040065.
61. Liu, H.; Zhang, B.; Wu, V.; Yang, X.; Wang, L. Review of Digital Twin in the Automotive Industry on Products, Processes and Systems. International Journal of Automotive Manufacturing and Materials 2025, 6. https://doi.org/10.53941/ijamm.2025.100006.
62. The Industrial Revolution. Google Books. https://books.google.co.in/books?hl=en&lr=&id=Naz_DQAAQBAJ&oi=fnd&pg=PP1&dq=%22The+Industrial+Revolution:+A+Very+Short+Introduction%22+by+Robert+C.+Allen&ots=abk3OQzpyK&sig=JwckNx5NvIyPjiEyYR5-T3EIaGs&redir_esc=y#v=onepage&q&f=false (accessed 2025-07-05).
63. Carvalho, R.; da Silva, A. R. Sustainability Requirements of Digital Twin-Based Systems: A Meta Systematic Literature Review. Applied Sciences 2021, 11 (12), 5519. https://doi.org/10.3390/app11125519.
64. Soori, M.; Arezoo, B.; Dastres, R. Digital Twin for Smart Manufacturing, a Review. Sustainable Manufacturing and Service Economics 2023, 2 (1), 100017. https://doi.org/10.1016/j.smse.2023.100017.
65. Popescu, D.; Dragomir, M.; Popescu, S.; Dragomir, D. Building Better Digital Twins for Production Systems by Incorporating Environmental Related Functions—Literature Analysis and Determining Alternatives. Applied Sciences 2022, 12 (17), 8657. https://doi.org/10.3390/app12178657.
66. He, B.; Bai, K.-J. Digital Twin-Based Sustainable Intelligent Manufacturing: A Review. Advances in Manufacturing 2020, 9, 1–21. https://doi.org/10.1007/s40436-020-00302-5.
67. Rojek, I.; Mikołajewski, D.; Dostatni, E. Digital Twins in Product Lifecycle for Sustainability in Manufacturing and Maintenance. Applied Sciences 2020, 11 (1), 31. https://doi.org/10.3390/app11010031.
68. L. D. Xu, W. He and S. Li, "Internet of Things in Industries: A Survey," in IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2233-2243, Nov. 2014, https://doi.org/10.1109/TII.2014.2300753
69. Van Bossuyt, D.L.; Beery, P.; O’Halloran, B.M.; Hernandez, A.; Paulo, E. The Naval Postgraduate School’s Department of Systems Engineering Approach to Mission Engineering Education through Capstone Projects. Systems 2019, 7, 38. https://doi.org/10.3390/systems7030038
70. Hartikainen, H., Salonen, T., & Korhonen, T. (2023). Digital Twins for Sustainability Assessment: A Systematic Review. Journal of Cleaner Production, 398, 136744.https://doi.org/10.1016/j.jclepro.2023.136744