The DOI matches its Crossref record; 213 of the 214 references it cites were found in a registry or an index.
Systematic comparison of software agents and Digital Twins: differences, similarities, and synergies in industrial production
Open accessAbstract
Abstract To achieve a highly agile and flexible production, a transformational shift is envisioned whereby industrial production systems evolve to be more decentralized, interconnected, and intelligent. Within this vision, production assets collaborate with each other, exhibiting a high degree of autonomy. Furthermore, information about individual production assets is accessible throughout their entire life-cycles. To realize this vision, the use of advanced information technology is required. Two commonly applied software paradigms in this context are Software Agents (referred to as Agents) and Digital Twins (DTs). This work presents a systematic comparison of Agents and DTs in industrial applications. The goal of the study is to determine the differences, similarities, and potential synergies between the two paradigms. The comparison is based on the purposes for which Agents and DTs are applied, the properties and capabilities exhibited by these software paradigms, and how they can be allocated within the Reference Architecture Model Industry 4.0. The comparison reveals that Agents are commonly employed in the collaborative planning and execution of production processes, while DTs are generally more applied to monitor production resources and process information. Although these observations imply characteristic sets of capabilities and properties for both Agents and DTs, a clear and definitive distinction between the two paradigms cannot be made. Instead, the analysis indicates that production assets utilizing a combination of Agents and DTs would demonstrate high degrees of intelligence, autonomy, sociability, and fidelity. To achieve this, further standardization is required, particularly in the field of DTs.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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
“Reinpold et al. (2024) emphasize that digital twins, equipped with machine learning algorithms, can perform predictive analysis based on simulated attack scenarios, enabling ICS to implement preventive measures against potential vulnerabilities in advance [53].”
From Towards enhanced cybersecurity in industrial control systems: a systematic review of context-based modeling, digital twins, and machine learning approaches (2025) · cites this paper as [53]
Citations
Cites
1 retracted
She, M. (2021) · Wireless Personal Communications · doi:10.1007/s11277-021-09072-0 · Cited by 2
RetractedThe PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283
Intelligent agents: theory and practice
Wooldridge, M., & Jennings, N. R. (1995) · The Knowledge Engineering Review · doi:10.1017/s0269888900008122 · Cited by 6,810
An Introduction to MultiAgent Systems
Wooldridge, M. (2002) · Cited by 5,394
Understanding and Using Context
Dey, A. K. (2001) · Personal and Ubiquitous Computing · doi:10.1007/s007790170019 · Cited by 5,035
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 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 · cited 8×
“In this work, the definitions introduced by Tao et al. (2018) and Kritzinger et al. (2018) are referred to and used as a guideline in order to establish a common understanding of this concept.”
InfluentialDigital 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
Characterising the Digital Twin: A systematic literature review
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020) · CIRP Journal of Manufacturing Science and Technology · doi:10.1016/j.cirpj.2020.02.002 · Cited by 2,422
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
Flagged first, then by citations
1 of the 214 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- Cohen, Y., Aperstein, Y. & Reis, J. (2023). A framework for integrating artificial intelligence in digital twins of manufacturing systems. In: Proceedings of the 22nd IFAC world congress.
- Dario, O., Nagore, I., Arantza, B., et al. (2019). Fault injection in Digital Twin as a means to test the response to process faults at virtual commissioning: Paraninfo Building, University of Zaragoza, 10–13 September, 2019: Proceedings. 2019 24th IEEE international conference on emerging technologies and factory automation (ETFA).
- Elsevier B.V. (2022) Scopus. https://www.scopus.com/search/form.uri?display=advanced
- Froese, A., & Kleinblotekamp, J. (2019). SEPDiT—Handbuch Lehrmaterial. Froese.
- Han, Z. (2012). Game theory in wireless and communication networks: Theory, models, and applications. Cambridge University Press. https://doi.org/10.1017/CBO9780511895043 · DOI
Cited by
Sorted by citations
Kilthau, M., Henkel, V., Wagner, L. P., Gehlhoff, F., & Fay, A. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124606 · Cited by 30
Self-citationJuarez, M. G. J., Giret, A., & Botti, V. J. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100959 · Cited by 12
Kiangala, K. S., & Wang, Z. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16434-6 · Cited by 9
A Multi-Agent Approach for the Optimized Operation of Modular Electrolysis Plants
Henkel, V., Wagner, L. P., Kilthau, M., Gehlhoff, F., & Fay, A. (2024) · Energies · doi:10.3390/en17143370 · Cited by 7
Self-citationAbraham, D., Erceylan, G., Gkioulos, V., & Houmb, S. H. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-01158-1 · Cited by 10
Metadata
- Authors
- Reinpold, L. M., Wagner, L. P., Gehlhoff, F., Ramonat, M., Kilthau, M., Gill, M. S., Reif, J. T., Henkel, V., Scholz, L., & Fay, A.
- Affiliations
- Helmut Schmidt University (DE)
- Corresponding author
- Reinpold, Lasse M.
- ORCID
- Venue
- Journal of Intelligent Manufacturing
- Published
- 2024-01-04
- Volume
- 36
- Issue
- 2
- Pages
- 765-800
- ISSN
- 0956-5515 (ISSN-L)
- 1572-8145
- OpenAlex
- W4390576566
- Cited by
- 27
- References
- 214
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 9
- FWCI
- 5.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Intelligent Manufacturing (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Econstor (Econstor) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Funders
- Digitalization and Technology Research Center of the Bundeswehr
- Bundesministerium für Wirtschaft und Klimaschutz (03EI6035A)
- VDI/VDE Innovation + Technik GmbH (16KN102724)
- Bundesministerium für Bildung und Forschung (03HY116)
- Helmut-Schmidt-Universität Universität der Bundeswehr Hamburg
- 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
- software agents, digital twin, industrial production, Industry 4.0, reference architecture, production monitoring
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
