IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i2d10.1007_s10845-023-02278-y.html
   My bibliography  Save this article

Systematic comparison of software agents and Digital Twins: differences, similarities, and synergies in industrial production

Author

Listed:
  • Lasse M. Reinpold

    (Helmut Schmidt University/University of the Federal Armed Forces)

  • Lukas P. Wagner

    (Helmut Schmidt University/University of the Federal Armed Forces)

  • Felix Gehlhoff

    (Helmut Schmidt University/University of the Federal Armed Forces)

  • Malte Ramonat

    (Helmut Schmidt University/University of the Federal Armed Forces)

  • Maximilian Kilthau

    (Helmut Schmidt University/University of the Federal Armed Forces)

  • Milapji S. Gill

    (Helmut Schmidt University/University of the Federal Armed Forces)

  • Jonathan T. Reif

    (Helmut Schmidt University/University of the Federal Armed Forces)

  • Vincent Henkel

    (Helmut Schmidt University/University of the Federal Armed Forces)

  • Lena Scholz

    (Helmut Schmidt University/University of the Federal Armed Forces)

  • Alexander Fay

    (Helmut Schmidt University/University of the Federal Armed Forces)

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.

Suggested Citation

  • Lasse M. Reinpold & Lukas P. Wagner & Felix Gehlhoff & Malte Ramonat & Maximilian Kilthau & Milapji S. Gill & Jonathan T. Reif & Vincent Henkel & Lena Scholz & Alexander Fay, 2025. "Systematic comparison of software agents and Digital Twins: differences, similarities, and synergies in industrial production," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 765-800, February.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:2:d:10.1007_s10845-023-02278-y
    DOI: 10.1007/s10845-023-02278-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-023-02278-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-023-02278-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Khalil Tliba & Thierno M. L. Diallo & Olivia Penas & Romdhane Ben Khalifa & Noureddine Ben Yahia & Jean-Yves Choley, 2023. "Digital twin-driven dynamic scheduling of a hybrid flow shop," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2281-2306, June.
    2. Cinzia Giannetti & Aniekan Essien, 2022. "Towards scalable and reusable predictive models for cyber twins in manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 441-455, February.
    3. Terwiesch, Christian & E. Bohn, Roger, 2001. "Learning and process improvement during production ramp-up," International Journal of Production Economics, Elsevier, vol. 70(1), pages 1-19, March.
    4. Amon Göppert & Lea Grahn & Jonas Rachner & Dennis Grunert & Simon Hort & Robert H. Schmitt, 2023. "Pipeline for ontology-based modeling and automated deployment of digital twins for planning and control of manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2133-2152, June.
    5. Carlos Henrique dos Santos & José Antônio de Queiroz & Fabiano Leal & José Arnaldo Barra Montevechi, 2022. "Use of simulation in the industry 4.0 context: Creation of a Digital Twin to optimise decision making on non-automated process," Journal of Simulation, Taylor & Francis Journals, vol. 16(3), pages 284-297, May.
    6. Christie Etukudor & Benoit Couraud & Valentin Robu & Wolf-Gerrit Früh & David Flynn & Chinonso Okereke, 2020. "Automated Negotiation for Peer-to-Peer Electricity Trading in Local Energy Markets," Energies, MDPI, vol. 13(4), pages 1-19, February.
    7. A. J. H. Redelinghuys & A. H. Basson & K. Kruger, 2020. "A six-layer architecture for the digital twin: a manufacturing case study implementation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1383-1402, August.
    8. Eduardo Oliveira & Vera L. Miguéis & José L. Borges, 2023. "Automatic root cause analysis in manufacturing: an overview & conceptualization," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2061-2078, June.
    9. Alfonso González-Briones & Fernando De La Prieta & Mohd Saberi Mohamad & Sigeru Omatu & Juan M. Corchado, 2018. "Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review," Energies, MDPI, vol. 11(8), pages 1-28, July.
    10. Matthias Seitz & Felix Gehlhoff & Luis Alberto Cruz Salazar & Alexander Fay & Birgit Vogel-Heuser, 2021. "Automation platform independent multi-agent system for robust networks of production resources in industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2023-2041, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Agathe Gilain & Pascal Le Masson & Benoit Weil, 2018. "Managing Learning Curves In The Unknown: From ‘Learning By Doing’ To ‘Learning By Designing’," Post-Print hal-01900961, HAL.
    2. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2025. "Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 61-93, January.
    3. Ahmed Ktari & Mohamed El Mansori, 2022. "Digital twin of functional gating system in 3D printed molds for sand casting using a neural network," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 897-909, March.
    4. S. Göttlich & S. Kühn & J. A. Schwarz & R. Stolletz, 2016. "Approximations of time-dependent unreliable flow lines with finite buffers," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 83(3), pages 295-323, June.
    5. Ayman AboElHassan & Soumaya Yacout, 2023. "A digital shadow framework using distributed system concepts," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3579-3598, December.
    6. Cosmin Aron & Fabio Sgarbossa & Eric Ballot & Dmitry Ivanov, 2024. "Cloud material handling systems: a cyber-physical system to enable dynamic resource allocation and digital interoperability," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3815-3836, December.
    7. Zander, Bennet & Lange, Kerstin & Haasis, Hans-Dietrich, 2021. "Designing the data supply chain of a smart construction factory," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 41-62, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    8. Jyrki Savolainen & Michele Urbani, 2021. "Maintenance optimization for a multi-unit system with digital twin simulation," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1953-1973, October.
    9. Sara Bysko & Jolanta Krystek & Andrzej Świerniak, 2024. "Nash equilibrium as a tool for the Car Sequencing Problem 4.0," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 1037-1053, March.
    10. Chi Ma & Hongquan Gui & Jialan Liu, 2023. "Self learning-empowered thermal error control method of precision machine tools based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 695-717, February.
    11. Chen-Fu Chien & Jia-Yu Peng, 2025. "Bayesian inference for multi-label classification for root cause analysis and probe card maintenance decision support and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1943-1958, March.
    12. Capper, Timothy & Gorbatcheva, Anna & Mustafa, Mustafa A. & Bahloul, Mohamed & Schwidtal, Jan Marc & Chitchyan, Ruzanna & Andoni, Merlinda & Robu, Valentin & Montakhabi, Mehdi & Scott, Ian J. & Franci, 2022. "Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    13. Morgan, Horatio M. & Ngwenyama, Ojelanki, 2015. "Real options, learning cost and timing software upgrades: Towards an integrative model for enterprise software upgrade decision analysis," International Journal of Production Economics, Elsevier, vol. 168(C), pages 211-223.
    14. Jaber, Mohamad Y. & Sikstrom, Sverker, 2004. "A numerical comparison of three potential learning and forgetting models," International Journal of Production Economics, Elsevier, vol. 92(3), pages 281-294, December.
    15. Weckenborg, Christian & Schumacher, Patrick & Thies, Christian & Spengler, Thomas S., 2024. "Flexibility in manufacturing system design: A review of recent approaches from Operations Research," European Journal of Operational Research, Elsevier, vol. 315(2), pages 413-441.
    16. Vivek Warke & Satish Kumar & Arunkumar Bongale & Ketan Kotecha, 2021. "Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis," Sustainability, MDPI, vol. 13(18), pages 1-49, September.
    17. Shin, Jang-Sup, 2017. "Dynamic catch-up strategy, capability expansion and changing windows of opportunity in the memory industry," Research Policy, Elsevier, vol. 46(2), pages 404-416.
    18. Alaa A. F. Husain & Maryam Huda Ahmad Phesal & Mohd Zainal Abidin Ab Kadir & Ungku Anisa Ungku Amirulddin & Abdulhadi H. J. Junaidi, 2021. "A Decade of Transitioning Malaysia toward a High-Solar PV Energy Penetration Nation," Sustainability, MDPI, vol. 13(17), pages 1-16, September.
    19. Filla, Patrick & Klingebiel, Katja, 2014. "A Risk Management Approach for the Pre-Series Logistics in Production Ramp-Up," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Next Generation Supply Chains: Trends and Opportunities. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 18, volume 18, pages 407-422, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    20. Edgar Chacón & Luis Alberto Cruz Salazar & Juan Cardillo & Yenny Alexandra Paredes Astudillo, 2021. "A control architecture for continuous production processes based on industry 4.0: water supply systems application," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2061-2081, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:36:y:2025:i:2:d:10.1007_s10845-023-02278-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.