Virtually Intelligent Product Systems: Digital and Physical Twins
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Interoperability with existing software used in a production lifecycle is a difficult problem to resolve, which could lead to delays in implementations.
“Interoperability with existing software used in a production lifecycle Industries use various types of software for tasks such as inventory, product management, and operations.DTs' compatibility with these is a difficult problem to resolve, which could lead to delays in implementations [45].”
From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [45]
Cites
Internet of Things in industries: A survey
Xu, L. D., He, W., & Li, S. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2300753 · Cited by 5,172 · see citing works
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 · cited 2× · see citing works
“Using Grieves and Vickers Categories of Systems Behavior model (Grieves & Vickers, 2016), (Figure 4), we can predict that Physical Twins, i.e., SCPS, increase system complexity.”
MethodSelf-citationProduct lifecycle management: the new paradigm for enterprises
Grieves, M. W. (2005) · International Journal of Product Development · doi:10.1504/ijpd.2005.006669 · Cited by 741 · see citing works
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Rathore, M. M., Shah, S. A., Shukla, D., Bentafat, E., & Bakiras, S. (2021) · IEEE Access · doi:10.1109/access.2021.3060863 · Cited by 616 · see citing works
Digital Twins: State of the art theory and practice, challenges, and open research questions
Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100383 · Cited by 587 · see citing works
Generative artificial intelligence empowers digital twins in drug discovery and clinical trials
Bordukova, M., Makarov, N., Rodriguez-Esteban, R., Schmich, F., & Menden, M. P. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2023.2273839 · Cited by 235 · see citing works
Digital Twins: Past, Present, and Future
Grieves, M. W. (2023) · The Digital Twin · doi:10.1007/978-3-031-21343-4_4 · Cited by 209 · see citing works
Self-citationAnalysis of Digital Twin Applications in Energy Efficiency: A Systematic Review
Ba, L., Tangour, F., El Abbassi, I., & Absi, R. (2025) · Sustainability · doi:10.3390/su17083560 · Cited by 96 · see citing works
A comprehensive review of Digital Twin technologies in smart cities
Huzzat, A., Anpalagan, A., Khwaja, A. S., Woungang, I., Alnoman, A. A., & Pillai, A. S. (2025) · Digital Engineering · doi:10.1016/j.dte.2025.100040 · Cited by 65 · see citing works
Noll, P., & Henkel, M. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.10.018 · Cited by 60 · see citing works
Lee, J. Y., Pyon, C. U., & Woo, J. (2023) · Electronics · doi:10.3390/electronics12153207 · Cited by 50 · see citing works
- Authors
- Grieves, M. W.
- Corresponding author
- Grieves, Michael W.
- ORCID
- Grieves, Michael W.
- Venue
- Complex Systems Engineering: Theory and Practice
- Published
- 2019-01
- Pages
- 175-200
- OpenAlex
- W2966928317
- Cited by
- 356
- References
- 3
- Citations by year
- 2026: 18 · 2025: 49 · 2024: 69 · 2023: 82 · 2022: 71 · 2021: 42 · 2020: 21 · 2019: 4
- FWCI
- 76.31
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- American Institute of Aeronautics and Astronautics eBooks (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Digital Innovation in Industries
- Physical Sciences › Engineering › Control and Systems Engineering › Systems Engineering Methodologies and Applications
- Keywords
- digital twin, physical twin, product lifecycle management
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.
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