The DOI matches its Crossref record; 43 of the 49 references it cites were found in a registry or an index.
Digital Twin in manufacturing: A categorical literature review and classification
Open accessAbstract
The Digital Twin (DT) is commonly known as a key enabler for the digital transformation, however, in literature is no common understanding concerning this term. It is used slightly different over the disparate disciplines. The aim of this paper is to provide a categorical literature review of the DT in manufacturing and to classify existing publication according to their level of integration of the DT. Therefore, it is distinct between Digital Model (DM), Digital Shadow (DS) and Digital Twin. The results are showing, that literature concerning the highest development stage, the DT, is scarce, whilst there is more literature about DM and DS.
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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?
In the citing paper
“a single process [34,36,44,45]”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [36]
In the citing paper
“Dealing with a constant flux of data makes it also possible to apply Artificial Intelligence (AI) and Machine Learning (ML) techniques to IoT systems based on Digital Twin [51][52][53] [54].”
From Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020) · cites this paper as [53]
Citations
Cites
The NIST definition of cloud computing
Mell, P., & Grance, T. (2011) · doi:10.6028/nist.sp.800-145 · Cited by 12,595
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems
Lee, J., Bagheri, B., & Kao, H.-A. (2015) · Manufacturing Letters · doi:10.1016/j.mfglet.2014.12.001 · Cited by 5,113
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-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 · cited 4×
“(Tao et al. 2017; Lee et al. 2013a; Rosen et al. 2015) To get a more common understanding of the Digital Twin, the level of integration are discussed in the following section.”
BackgroundInfluentialThe 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
About The Importance of Autonomy and Digital Twins for the Future of Manufacturing
Rosen, R., von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.06.141 · Cited by 1,474 · cited 7×
“(Kuhn 2017; Boschert, Rosen 2016; Rosen et al. 2015) In terms of manufacturing, (Garetti et al. 2012) has the following definition for a Digital Twin: “The DT consists of a virtual representation of a production system that is able to run on different simulation disciplines that is characterized by…”
BackgroundInfluentialShaping the digital twin for design and production engineering
Schleich, B., Anwer, N., Mathieu, L., & Wartzack, S. (2017) · CIRP Annals · doi:10.1016/j.cirp.2017.04.040 · Cited by 1,305 · cited 3×
“While some researchers, especially coming from the class Digital Shadow, see a further research need in developing the optimization and simulation methods, others see the data connectivity as main problem.(Yang et al. 2016; Um et al. 2017b; Vachálek et al. 2017; Schleich et al. 2017; Frazzon et al. 2016; Kuhn 2017; St…”
BackgroundMethodDigital Twin—The Simulation Aspect
Boschert, S., & Rosen, R. (2016) · Mechatronic Futures · doi:10.1007/978-3-319-32156-1_5 · Cited by 1,158
Recent advances and trends in predictive manufacturing systems in big data environment
Lee, J., Lapira, E., Bagheri, B., & Kao, H.-A. (2013) · Manufacturing Letters · doi:10.1016/j.mfglet.2013.09.005 · Cited by 1,090 · cited 7×
“…stages of machine life cycle to handle data/information more efficiently and further achieve better transparency of a machine’s health condition (Lee et al. 2013b) Layout planning (Uhlemann et al. 2017a) o continuous production system evaluation and planning o Automatic and application…”
BackgroundInfluentialThe Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0
Uhlemann, T. H.-J., Lehmann, C., & Steinhilper, R. (2017) · Procedia CIRP · doi:10.1016/j.procir.2016.11.152 · Cited by 994
Research works printed but not linked: 4
- Lee, Jay; Lapira, Edzel; Bagheri, Behrad; Kao, Hung-an (2013b). Recent advances and trends in predictive manufacturing systems in big data environment. · DOI · Paper page
- Negri, 2017, A Review of the Roles of Digital Twin in CPS-based Production Systems, Procedia Manufacturing, 11, 939, 10.1016/j.promfg.2017.07.198 · DOI · Paper page
- Reeves, 2017, Spotlight on the Digital Twin, In ANSYS Advantage, 2017
- Zhang, Hao; Liu, Qiang; Chen, Xin; Zhang, Ding; Leng, Jiewu (2017). A digital twin-based approach for designing and decoupling of hollow glass production line. In IEEE Access, p. 1. · DOI · Paper page
Cited by
Sorted by citations
Items 1,001–1,004 of 1,004
Design and implementation of a digital twin for a biomass-to-gas plant
Jankovic, S., Stanger, L., Bartik, A., Hammerschmid, M., Benedikt, F., Mittermayr, M., Binder, M., Kozek, M., & Müller, S. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126861 · Cited by 13
Ljubisavljević, T., Vujko, A., Arsić, M., & Mirčetić, V. (2025) · World · doi:10.3390/world6040148 · Cited by 13
“Digital-twin credibility (accuracy, real-time integration) relied on DT classification/assurance literature [66].”
Interplay of Digital Twins and Cyber Deception: Unraveling Paths for Technological Advancements
Heluany, J., Amro, A., Gkioulos, V., & Katsikas, S. (2024) · Proceedings of the 2024 ACM/IEEE 4th International Workshop on Engineering and Cybersecurity of Critical Systems (EnCyCriS) and 2024 IEEE/ACM Second International Workshop on Software Vulnerability · doi:10.1145/3643662.3643955 · Cited by 8
“The aspects of synchronization frequency, and simulation fidelity can vary according to the needs of each use case, and even result in sub-terminologies, as suggested by Kritzinger [21] when defining Digital Model , Digital Shadow , and Digital Twin based on the level of data integration.”
BackgroundPrasath, N., Arun, A., Saravanan, B., & Kamaraj, K. (2025) · Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology · doi:10.3233/jifs-233495 · Cited by 3
“0andareincreasinglybeing adoptedinvariousindustries,includingmanufactur-ing,energy,andhealthcare[15].”
Retracted
Metadata
- Authors
- Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W.
- Affiliations
- Fraunhofer Austria (AT)
- Corresponding author
- Kritzinger, Werner
- Venue
- IFAC-PapersOnLine
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 51
- Issue
- 11
- Pages
- 1016-1022
- ISSN
- 2405-8963 (ISSN-L)
- OpenAlex
- W2890904471
- Cited by
- 3,612
- References
- 49
- Citations by year
- 2026: 761 · 2025: 639 · 2024: 576 · 2023: 577 · 2022: 437 · 2021: 356 · 2020: 194 · 2019: 71 · 2018: 1
- FWCI
- 128.89
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- IFAC-PapersOnLine (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- European Commission
- Horizon 2020 Framework Programme
- 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 › Applied Advanced Technologies
- Keywords
- digital twin, manufacturing industry, digital models, digital shadow, digital transformation, terminology, levels of integration
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