The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Digital Twin: Origin to Future
Open accessAbstract
Digital Twin (DT) refers to the virtual copy or model of any physical entity (physical twin) both of which are interconnected via exchange of data in real time. Conceptually, a DT mimics the state of its physical twin in real time and vice versa. Application of DT includes real-time monitoring, designing/planning, optimization, maintenance, remote access, etc. Its implementation is expected to grow exponentially in the coming decades. The advent of Industry 4.0 has brought complex industrial systems that are more autonomous, smart, and highly interconnected. These systems generate considerable amounts of data useful for several applications such as improving performance, predictive maintenance, training, etc. A sudden influx in the number of publications related to ‘Digital Twin’ has led to confusion between different terminologies related to the digitalization of industries. Another problem that has arisen due to the growing popularity of DT is a lack of consensus on the description of DT as well as so many different types of DT, which adds to the confusion. This paper intends to consolidate the different types of DT and different definitions of DT throughout the literature for easy identification of DT from the rest of the complimentary terms such as ‘product avatar’, ‘digital thread’, ‘digital model’, and ‘digital shadow’. The paper looks at the concept of DT since its inception to its predicted future to realize the value it can bring to certain sectors. Understanding the characteristics and types of DT while weighing its pros and cons is essential for any researcher, business, or sector before investing in the technology.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In accordance with DT characteristics, the DTP is able to simulate manufacturing scenarios and perform validation testing, evaluation and even quality control testing prior to the actual manufacturing process itself.This approach effectively reduces production costs and operational time by identifying flaws or possible risks of the physical twin before production.”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [10]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“It is important to note that the development and widespread implementation of DTs depend on the advances in research and enabling technologies such as AI, ML, big data, 5G communication and cloud/edge computing [10,83].”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [10]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The proposed approach involved utilizing DT technology to replicate the aircraft's physical and mechanical characteristics, aiming to forecast potential fatigue or structural cracks.This strategy would effectively extend the operational lifespan of the aircraft [21].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [21]
The cited source says what the claim attributes to it.
In the citing paper
“Furthermore, the continuous transfer of information between DT and its physical counterpart enables ongoing validation and optimization of the system's processes [21].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [21]
The cited source says what the claim attributes to it.
In the citing paper
“However, the modern state of the art classifies and defines DTs based on the application domain [10] and their levels of integration.”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“High costs of implementation due to the increased amount of sensors and computational resources needed [10,18].”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“Issues related to data (trust, privacy, cybersecurity, convergence and governance, acquisition and large-scale analysis) [10].”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [10]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Digital Twin in Industry: State-of-the-Art
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391
Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems
Grieves, M., & Vickers, J. (2017) · Transdisciplinary Perspectives on Complex Systems · doi:10.1007/978-3-319-38756-7_4 · Cited by 3,727
Digital Twin in manufacturing: A categorical literature review and classification
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.474 · Cited by 3,612
Digital twin-driven product design, manufacturing and service with big data
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920
The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles
Glaessgen, E., & Stargel, D. (2012) · 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA · doi:10.2514/6.2012-1818 · Cited by 2,404 · cited 4×
“…literature have referred to DT as a virtual or digital model [21,23,27–35], layout [36], counterpart [7,9,35,37], doppelganger [38], clone [39], footprint [40], software analogue [41], representation [42–46], information constructs [2,47], or simulation [20,26,48–51] of its physical counterpart.”
BackgroundInfluentialReview of digital twin about concepts, technologies, and industrial applications
Liu, M., Fang, S., Dong, H., & Xu, C. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.06.017 · Cited by 2,075
Lu, Y., Liu, C., Wang, K. I., Huang, H., & Xu, X. W. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101837 · Cited by 1,753
Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison
Qi, Q., & Tao, F. (2018) · IEEE Access · doi:10.1109/access.2018.2793265 · Cited by 1,716
A Review of the Roles of Digital Twin in CPS-based Production Systems
Negri, E., Fumagalli, L., & Macchi, M. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.07.198 · Cited by 1,669
Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019) · Engineering · doi:10.1016/j.eng.2019.01.014 · Cited by 1,589
Research works printed but not linked: 11
- Parrott, A., and Warshaw, L. (2017). Industry 4.0 and the Digital Twin: Manufacturing Meets its Match, Deloitte University Press.
- Gelernter, D. (1993). Mirror Worlds: Or: The Day Software Puts the Universe in a Shoebox. How it Will Happen and What it Will Mean, Oxford University Press.
- Leiva, 2016, Demystifying the digital thread and digital twin concepts, Ind. Week. August, 1, 2016
- Almeida, H.A., and Vasco, J.C. (2020). The digital twin of production, the ultimate tool to achieve first-time-right in metal additive manufacturing. Progress in Digital and Physical Manufacturing: Proceedings of ProDPM’19, Springer.
- (2020, October 12). Gartner Identifies the Top 10 Strategic Technology Trends for 2017. Available online: https://www.gartner.com/en/newsroom/press-releases/2016-10-18-gartner-identifies-the-top-10-strategic-technology-trends-for-2017.
- (2020, October 12). Gartner Identifies the Top 10 Strategic Technology Trends for 2018. Available online: https://www.gartner.com/en/newsroom/press-releases/2017-10-04-gartner-identifies-the-top-10-strategic-technology-trends-for-2018.
- (2020, October 12). Gartner Identifies the Top 10 Strategic Technology Trends for 2019. Available online: https://www.gartner.com/en/newsroom/press-releases/2018-10-15-gartner-identifies-the-top-10-strategic-technology-trends-for-2019.
- (2020, October 13). Gartner Survey Reveals 47% of Organizations Will Increase Investments in IoT Despite the Impact of COVID-19. Available online: https://www.gartner.com/en/newsroom/press-releases/2020-10-29-gartner-survey-reveals-47-percent-of-organizations-will-increase-investments-in-iot-despite-the-impact-of-covid-19-.
- Lamb, K. (2019). Principle-Based Digital Twins: A Scoping Review, Centre for Digital Built Britain.
- Parks, M. (2020, October 12). Digita Twinning: Types of Digital Twins. Method: Mouser’s Technology & Solutions Journal. Available online: https://eu.mouser.com/applications/digital-twinning-types/.
- Saracco, R. (2020, October 12). Digital Twins: Advantages & Issues of a Powerful Emerging Technology. Available online: https://cmte.ieee.org/futuredirections/2018/06/14/digital-twins-advantages-issues-of-a-powerful-emerging-technology/.
Cited by
Sorted by citations
Digital Twin Technology Challenges and Applications: A Comprehensive Review
Botín-Sanabria, D. M., Mihaita, A.-S., Peimbert-García, R. E., Ramírez-Moreno, M. A., Ramírez-Mendoza, R. A., & Lozoya-Santos, J. D. J. (2022) · Remote Sensing · doi:10.3390/rs14061335 · Cited by 878
Human Digital Twin in the context of Industry 5.0
Wang, B., Zhou, H., Li, X., Yang, G., Zheng, P., Song, C., Yuan, Y., Wuest, T., Yang, H., & Wang, L. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2023.102626 · Cited by 452
Digital Twin applications toward Industry 4.0: A Review
Javaid, M., Haleem, A., & Suman, R. (2023) · Cognitive Robotics · doi:10.1016/j.cogr.2023.04.003 · Cited by 466
Applications of Digital Twin across Industries: A Review
Singh, M., Srivastava, R., Fuenmayor, E. A., Kuts, V., Qiao, Y., Murray, N., & Devine, D. M. (2022) · Applied Sciences · doi:10.3390/app12115727 · Cited by 376
“To get a better understanding of DT, our previous review paper [2] can be referred to, which talks about the definitions of DT across the literature, its characteristics, types, advantages, and challenges.”
InfluentialSelf-citationDigital twin for smart manufacturing, A review
Soori, M., Behrooz Arezoo, B., & Dastres, R. (2023) · Sustainable Manufacturing and Service Economics · doi:10.1016/j.smse.2023.100017 · Cited by 411
Metadata
- Authors
- Singh, M., Fuenmayor, E., Hinchy, E., Qiao, Y., Murray, N., & Devine, D.
- Affiliations
- Athlone Institute of Technology (IE)
- University of Limerick (IE)
- Corresponding author
- Devine, Declan
- Venue
- Applied System Innovation
- Publisher
- MDPI AG
- Published
- 2021-05-24
- Volume
- 4
- Issue
- 2
- Pages
- 36
- ISSN
- 2571-5577 (ISSN-L)
- OpenAlex
- W3164800436
- Cited by
- 1,038
- References
- 72
- Citations by year
- 2026: 189 · 2025: 275 · 2024: 260 · 2023: 198 · 2022: 102 · 2021: 14
- FWCI
- 74.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied System Innovation (publishedVersion, cc-by)
- University of Limerick Institutional Repository (University of Limerick) (acceptedVersion, other-oa)
- Arrow@dit (Dublin Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Research@THEA (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by-nc-sa)
- Funders
- Science Foundation Ireland (16/RC/3918)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Digital Innovation in Industries
- Keywords
- digital twin, physical twin, Industry 4.0, real-time data exchange, predictive maintenance, digital thread, digital models, digital shadow, terminology confusion, industrial digitalization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
