Framework for a digital twin in manufacturing: Scope and requirements
Open accessAbstract
Digital twin has the potential to be an important concept for achieving smart manufacturing. However, there remains a lot of confusion about the concept and how it can be implemented in real manufacturing systems, especially among small-to-medium-sized enterprises. This paper synthesizes the different perspectives that have been reported on the digital twin to identify the key characteristics that must be understood when developing a digital twin for a specific use case. Example applications are provided and the need for a standardized framework, such as the one under development as ISO 23247 (Digital Twin Manufacturing Framework), is motivated. This framework can enable context-dependent implementations and promote composability and reusability of digital twin components.
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Digital Twins enhance real-time remote monitoring and predictive maintenance but also pose risks related to data security and interoperability when integrated with legacy systems.
“While they enhance real-time remote monitoring and predictive maintenance [39], they also pose risks related to data security and interoperability when integrated with legacy systems [41,42].”
From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [42]
The “Example Uses of Digital Twins in Manufacturing” section says a machine-health twin can monitor equipment and predict faults and failures, supporting the predictive-maintenance point. The “Perspectives on the Digital Twin” section discusses real-time visualization but does not establish remote monitoring as an application. The security and legacy-system risks are attached to different citations [41,42], not the citation assessed here; page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe development of Digital Twins is challenged by a lack of common data standards and tools, due to a lack of consensus on standards, technologies, and procedures.
“Lack of common data standards and tools Currently, the development of the Digital Twin is challenged due to a lack of consensus on the different standards, technologies, and procedures that can be used for the implementation of a Digital Twin [42].”
From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [42]
The “Digital Twins in Manufacturing” section says that implementation is complicated by a lack of consensus on the digital twin concept and on the standards, technologies, and procedures to use. The “Standardized Framework” section also argues for reusable components, but it does not specifically establish a lack of common data standards and tools, as the citing heading states. No page numbers are available in our copy. (LLM, conf=0.84)
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“DTs have been applied or proposed to a variety of industries, including smart cities [66], healthcare [67], manufacturing [68], aviation [69], smart grids [70], etc.”
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“Shao and Helu [69] synthesized different viewpoints of DT design models.”
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Digital twin predicting diet response before and after long-term fasting
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“Digital twins come in different categories, e.g. online twins , which are updated in real time by connecting with a sensor, and offline twins , which are only occasionally updated using new data [15].”
BackgroundSimulation-based Digital Twin for enhancing human-robot collaboration in assembly systems
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Maimour, M., Ahmed, A., & Rondeau, É. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101070 · Cited by 34 · see citing works
A Review of Digital Twinning for Rotating Machinery
Inturi, V., Ghosh, B., Rajasekharan, S., & Pakrashi, V. (2024) · Italian National Conference on Sensors · doi:10.3390/s24155002 · Cited by 29 · see citing works
“A standardized framework (ISO 23247) is being developed to offer guidelines, strategies, and evaluation methods for creating and executing DTs in the manufacturing industry [141,142].”
MethodGuidelines for designing a digital twin for Li-ion battery: A reference methodology
Semeraro, C., Aljaghoub, H., Abdelkareem, M., Alami, A., Dassisti, M., & Olabi, A. (2023) · Energy · doi:10.1016/j.energy.2023.128699 · Cited by 28 · see citing works
The Potential Use of Digital Twin Technology for Advancing CAR-T Cell Therapy
Aghamiri, S., & Amin, R. (2025) · Current Issues in Molecular Biology · doi:10.3390/cimb47050321 · Cited by 27 · see citing works
“Because the development of DTs in oncology is still in its early stages, standardized guidelines for defining data collection protocols, model validation criteria, model fusion, and clinical integration strategies have yet to be established [169,170].”
3D Model-Based Large-Volume Metrology Supporting Smart Manufacturing and Digital Twin Concepts
Lindqvist, R., Strand, D., Nilsson, M., Collins, V., Torstensson, J., Kressin, J., Spensieri, D., & Archenti, A. (2023) · Metrology · doi:10.3390/metrology3010002 · Cited by 27
Hadi, N. U., Almessabi, B., & Khan, M. I. (2025) · Journal of Open Innovation: Technology, Market and Complexity · doi:10.1016/j.joitmc.2025.100545 · Cited by 24 · see citing works
Digital twins in manufacturing: A unified conceptual framework
Villegas, L. F., Macchi, M., & Polenghi, A. (2025) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2025.101031 · Cited by 28 · see citing works
Lee, J., Lee, Y., Park, S., & Hong, C. (2023) · Applied Sciences · doi:10.3390/app13169137 · Cited by 22 · see citing works
The Future of Digital Twin Research and Development
Bossuyt, D. L. V., Allaire, D., Bickford, J. F., Bozada, T. A., Chen, W., Cutitta, R. P., Cuzner, R. M., Fletcher, K. M., Giachetti, R. E., Hale, B., et al. (2025) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4068082 · Cited by 24 · see citing works
Lopes, P. V., Silveira, L., Aquino, R. D. G., Ribeiro, C. H., Skoogh, A., & Verri, F. (2024) · International journal of computer integrated manufacturing (Print) · doi:10.1080/0951192x.2024.2322981 · Cited by 21 · see citing works
“Thus, these artificial databases enable implementations for resource (such as materials labour and equipment) optimisation, cycle-time reduction and inventory cost reductions (Shao and Helu 2020).”
BackgroundModeling and Simulation of Shape Control Based on Digital Twin Technology in Hot Strip Rolling
Sun, Y.-Z., Li, J.-D., Sun, Y.-M., Song, L.-B., Yang, Q., & Wang, X.-C. (2024) · Italian National Conference on Sensors · doi:10.3390/s24020614 · Cited by 21 · see citing works
“In addition, DT technology can also realize the real-time visualization of physical objects to make the manufacturing process more transparent [17].”
BackgroundSaxby, D., Pizzolato, C., & Diamond, L. (2023) · Journal of Applied Biomechanics · doi:10.1123/jab.2023-0114 · Cited by 20
Loaiza, J. H., Cloutier, R. J., & Lippert, K. J. (2023) · Systems · doi:10.3390/systems11010041 · Cited by 28 · see citing works
Bégout, P., Kubicki, S., Bricard, E., & Duval, T. (2022) · Frontiers in Virtual Reality · doi:10.3389/frvir.2022.918685 · Cited by 27 · see citing works
“It should be noted that the implementation constraints also depend on the application (e.g., simulations, predictions) that will be made of the DTs (Shao and Helu, 2020).”
BackgroundSkoogh, A., Thürer, M., Subramaniyan, M., Matta, A., & Roser, C. (2023) · Production & Manufacturing Research · doi:10.1080/21693277.2023.2283031 · Cited by 19
Clausen, C. S. B., Ma, Z., & Jørgensen, B. N. (2022) · Energy Inform. · doi:10.1186/s42162-022-00222-7 · Cited by 19 · see citing works
“This issue propagates challenges of scoping (Shao and Helu 2020), ontology (D’Amico et al. 2019), and increased development costs (Felemban et al. 2021; Landahl et al. 2018; Shah et al. 2021).”
BackgroundZhang, Y., Jingyu, L., & Hongwei, Y. (2022) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1177/09544054221110960 · Cited by 19 · see citing works
“29 DT not only has academic research outcomes but also attracted strong interest from industry practitioners.”
Digital Twin in Practice: Emergent Insights from an ethnographic-action research study
Agrawal, A., Singh, V., Thiel, R., Pillsbury, M., Knoll, H., Puckett, J., & Fischer, M. (2022) · Construction Research Congress 2022 · doi:10.1061/9780784483961.131 · Cited by 19 · see citing works
Starly, B., Koprov, P., Bharadwaj, A. G., Batchelder, T. H., & Breitenbach, B. (2023) · Manufacturing Letters · doi:10.1016/j.mfglet.2023.07.021 · Cited by 18
Digital Twin Implementation for Manufacturing of Adjuvants
Phalak, P., Tomba, E., Jehoulet, P., Kapitan-Gnimdu, A., Soladana, P. M., Vagaggini, L., Brochier, M., Stevens, B., Peel, T., Strodiot, L., et al. (2023) · Processes · doi:10.3390/pr11061717 · Cited by 18 · see citing works
“In the digital twin of a manufacturing process, the real plant is connected to the virtual plant and there is a two-way connectivity between them [9,10].”
BackgroundÁlvares, A. J., Rodriguez, E., & Figueroa, B. S. (2025) · Processes · doi:10.3390/pr13082335 · Cited by 17 · see citing works
Method for the development and application of digital twins in manufacturing
Fuhrländer-Völker, D., Lindner, M. S., von Elling, M., Frieß, T., Karnapp, S., & Weigold, M. (2025) · Production Engineering · doi:10.1007/s11740-025-01346-x · Cited by 18 · see citing works
“In [18] a framework for implementing digital twins in manufacturing is outlined, emphasizing the need for standardized approaches to ensure interoperability and reusability.”
Generative AI-Driven Digital Twin in the Manufacturing Internet of Things: A Comprehensive Survey
Fu, X., Qin, M., Pace, P., Savaglio, C., Li, W., & Fortino, G. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3655343 · Cited by 13 · see citing works
“However, this increased cost is justified by its superior scalability and the substantial operational value derived from deep system integration and real-time robustness [41], [42].”
Guerra-Zubiaga, D. A., dos Santos, M. D., Voicu, R., Richards, G., Gosnell, S., & Barbosa, G. (2024) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-024-05114-3 · Cited by 16 · see citing works
“To bridge this discrepancy, ISO 23247 emerged, offering guiding principles for employing digital twins in manufacturing setups [22] [23].”
Mohandes, S. R., Singh, A. K., Fazeli, A., Banihashemi, S., Arashpour, M., Cheung, C. M., EJOHWOMU, O. A., & Zayed, T. M. (2024) · Smart and Sustainable Built Environment · doi:10.1108/sasbe-11-2023-0344 · Cited by 28 · see citing works
Characterisation and evaluation of identicality for digital twins for the manufacturing domain
Su, S., Nassehi, A., Hicks, B., & Ross, J. (2023) · Journal of manufacturing systems · doi:10.1016/j.jmsy.2023.09.004 · Cited by 16 · see citing works
Rossi, A., Moretti, M., & Senin, N. (2022) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2022.09.048 · Cited by 16
Kim, J., & Jeong, J. (2022) · Applied Sciences · doi:10.3390/app12157534 · Cited by 16 · see citing works
“Digital Twins have been described as the key enablers of Industry 4.0 and Smart Manufacturing [2,3].”
MethodInfluentialModelling cycle for simulation digital twins
Reed, S., Löfstrand, M., & Andrews, J. (2021) · Manufacturing Letters · doi:10.1016/j.mfglet.2021.04.004 · Cited by 16 · see citing works
Multisensory and BIM-Integrated Digital Twin to Improve Urban Excavation Safety
Liu, D.-H., Sun, C., Chen, J.-J., & Liu, L. (2023) · Journal of computing in civil engineering · doi:10.1061/jccee5.cpeng-5354 · Cited by 15 · see citing works
Patterns for Visual Management in Industry 4.0
Fenza, G., Loia, V., & Nota, G. (2021) · Italian National Conference on Sensors · doi:10.3390/s21196440 · Cited by 15 · see citing works
“, machine, cell, line, facility or supply chain) such as optimizing production planning and scheduling, minimizing the impact of equipment downtime or enabling virtual commissioning [45].”
Background
- Authors
- Shao, G., & Helu, M.
- Affiliations
- National Institute of Standards and Technology (US)
- Corresponding author
- Shao, Guodong
- Venue
- Manufacturing Letters
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 24
- Pages
- 105-107
- ISSN
- 2213-8463 (ISSN-L)
- PMC
- PMC7431924
- OpenAlex
- W3015896415
- Cited by
- 292
- References
- 16
- Citations by year
- 2026: 29 · 2025: 45 · 2024: 60 · 2023: 66 · 2022: 54 · 2021: 32 · 2020: 6
- FWCI
- 25.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Manufacturing Letters (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Standards and Technology
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Flexible and Reconfigurable Manufacturing Systems
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Manufacturing Process and Optimization
- Keywords
- digital twin, smart manufacturing, ISO 23247, small and medium-sized enterprises, digital twin implementation, composability
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