IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p6251-d1116652.html
   My bibliography  Save this article

A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered On-The-Fly Replanning Capabilities

Author

Listed:
  • Jiří Vyskočil

    (Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslávských Partyzánů 1580/3, Dejvice, 160 00 Prague 6, Czech Republic
    These authors contributed equally to this work.)

  • Petr Douda

    (Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslávských Partyzánů 1580/3, Dejvice, 160 00 Prague 6, Czech Republic
    These authors contributed equally to this work.)

  • Petr Novák

    (Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslávských Partyzánů 1580/3, Dejvice, 160 00 Prague 6, Czech Republic
    These authors contributed equally to this work.)

  • Bernhard Wally

    (Office of the Austrian Council for Research and Technology Development, Pestalozzigasse 4/D1, 1010 Vienna, Austria)

Abstract

Industry 4.0 smart production systems comprise industrial systems and subsystems that need to be integrated in such a way that they are able to support high modularity and reconfigurability of all system components. In today’s industrial production, manufacturing execution systems (MESs) and supervisory control and data acquisition (SCADA) systems are typically in charge of orchestrating and monitoring automated production processes. This article explicates an MES architecture that is capable of autonomously composing, verifying, interpreting, and executing production plans using digital twins and symbolic planning methods. To support more efficient production, the proposed solution assumes that the manufacturing process can be started with an initial production plan that may be relatively inefficient but quickly found by an AI. While executing this initial plan, the AI searches for more efficient alternatives and forwards better solutions to the proposed MES, which is able to seamlessly switch between the currently executed plan and the new plan, even during production. Further, this on-the-fly replanning capability is also applicable when newly identified production circumstances/objectives appear, such as a malfunctioning robot, material shortage, or a last-minute change to a customizable product. Another feature of the proposed MES solution is its distributed operation with multiple instances. Each instance can interpret its part of the production plan, dedicated to a location within the entire production site. All of these MES instances are continuously synchronized, and the actual global or partial (i.e., from the instance perspective) progress of the production is handled in real-time within one common digital twin. This article presents three main contributions: (i) an execution system that is capable of switching seamlessly between an original and a subsequently introduced alternative production plan, (ii) on-the-fly AI-powered planning and replanning of industrial production integrated into a digital twin, and (iii) a distributed MES, which allows for running multiple instances that may depend on topology or specific conditions of a real production plant. All of these outcomes are demonstrated and validated on a use-case utilizing an Industry 4.0 testbed, which is equipped with an automated transport system and several industrial robots. While our solution is tested on a lab-sized production system, the technological base is prepared to be scaled up to larger systems.

Suggested Citation

  • Jiří Vyskočil & Petr Douda & Petr Novák & Bernhard Wally, 2023. "A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered On-The-Fly Replanning Capabilities," Sustainability, MDPI, vol. 15(7), pages 1-27, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6251-:d:1116652
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/6251/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/7/6251/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Krzysztof Ejsmont & Bartlomiej Gladysz & Aldona Kluczek, 2020. "Impact of Industry 4.0 on Sustainability—Bibliometric Literature Review," Sustainability, MDPI, vol. 12(14), pages 1-29, July.
    2. Flavio Tonelli & Steve Evans & Paolo Taticchi, 2013. "Industrial sustainability: challenges, perspectives, actions," International Journal of Business Innovation and Research, Inderscience Enterprises Ltd, vol. 7(2), pages 143-163.
    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. Julia Reisinger & Patrick Hollinsky & Iva Kovacic, 2021. "Design Guideline for Flexible Industrial Buildings Integrating Industry 4.0 Parameters," Sustainability, MDPI, vol. 13(19), pages 1-24, September.
    2. Taryn Renatta De Mendonca & Yan Zhou, 2019. "Environmental Performance, Customer Satisfaction, and Profitability: A Study among Large U.S. Companies," Sustainability, MDPI, vol. 11(19), pages 1-15, September.
    3. Pablo Pérez-Gosende & Josefa Mula & Manuel Díaz-Madroñero, 2020. "Overview of Dynamic Facility Layout Planning as a Sustainability Strategy," Sustainability, MDPI, vol. 12(19), pages 1-16, October.
    4. Chavatip Chindavijak & Kongkiti Phusavat & Pekka Kess & Suparerk Sooksamarn, 2016. "Transformation to Enterprise Sustainability Case Studies of Manufacturing and Service Enterprise in Thailand," Managing Innovation and Diversity in Knowledge Society Through Turbulent Time: Proceedings of the MakeLearn and TIIM Joint International Conference 2016,, ToKnowPress.
    5. Marta Negri & Alessandra Neri & Enrico Cagno & Gabriele Monfardini, 2021. "Circular Economy Performance Measurement in Manufacturing Firms: A Systematic Literature Review with Insights for Small and Medium Enterprises and New Adopters," Sustainability, MDPI, vol. 13(16), pages 1-27, August.
    6. Ana Teresa Tavares-Lehmann & Celeste Varum, 2021. "Industry 4.0 and Sustainability: A Bibliometric Literature Review," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    7. Peerally, Jahan Ara & Santiago, Fernando & De Fuentes, Claudia & Moghavvemi, Sedigheh, 2022. "Towards a firm-level technological capability framework to endorse and actualize the Fourth Industrial Revolution in developing countries," Research Policy, Elsevier, vol. 51(10).
    8. Albachiara Boffelli & Stefano Dotti & Paolo Gaiardelli & Giorgia Carissimi & Barbara Resta, 2019. "Corporate Environmental Management for the Textile Industry: Toward an Empirical Typology," Sustainability, MDPI, vol. 11(23), pages 1-25, November.
    9. Dinara Dikhanbayeva & Sabit Shaikholla & Zhanybek Suleiman & Ali Turkyilmaz, 2020. "Assessment of Industry 4.0 Maturity Models by Design Principles," Sustainability, MDPI, vol. 12(23), pages 1-22, November.
    10. Rui Zhao & Yiyun Liu & Zhenyan Zhang & Sidai Guo & Ming-Lang Tseng & Kuo-Jui Wu, 2018. "Enhancing Eco-Efficiency of Agro-Products’ Closed-Loop Supply Chain under the Belt and Road Initiatives: A System Dynamics Approach," Sustainability, MDPI, vol. 10(3), pages 1-15, March.
    11. Geyi, Dan’Asabe Godwin & Yusuf, Yahaya & Menhat, Masha S. & Abubakar, Tijjani & Ogbuke, Nnamdi J., 2020. "Agile capabilities as necessary conditions for maximising sustainable supply chain performance: An empirical investigation," International Journal of Production Economics, Elsevier, vol. 222(C).
    12. Guillermo Fuertes & Jorge Zamorano & Miguel Alfaro & Manuel Vargas & Jorge Sabattin & Claudia Duran & Rodrigo Ternero & Ricardo Rivera, 2022. "Opportunities of the Technological Trends Linked to Industry 4.0 for Achieve Sustainable Manufacturing Objectives," Sustainability, MDPI, vol. 14(18), pages 1-36, September.
    13. Abbate, Stefano & Centobelli, Piera & Cerchione, Roberto, 2023. "The digital and sustainable transition of the agri-food sector," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    14. Azemeraw Tadesse Mengistu & Roberto Panizzolo, 2023. "Analysis of indicators used for measuring industrial sustainability: a systematic review," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(3), pages 1979-2005, March.
    15. Demartini, Melissa & Bertani, Filippo & Tonelli, Flavio & Raberto, Marco & Cincotti, Silvano, 2021. "An investigation into modelling approaches for industrial symbiosis: a literature review," MPRA Paper 107448, University Library of Munich, Germany.
    16. Alexandre André Feil & Dusan Schreiber & Claus Haetinger & Virgílio José Strasburg & Claudia Luisa Barkert, 2019. "Sustainability Indicators for Industrial Organizations: Systematic Review of Literature," Sustainability, MDPI, vol. 11(3), pages 1-15, February.
    17. Favi, Claudio & Marconi, Marco & Mandolini, Marco & Germani, Michele, 2022. "Sustainable life cycle and energy management of discrete manufacturing plants in the industry 4.0 framework," Applied Energy, Elsevier, vol. 312(C).
    18. Rebeca B. Sánchez-Flores & Samantha E. Cruz-Sotelo & Sara Ojeda-Benitez & Ma. Elizabeth Ramírez-Barreto, 2020. "Sustainable Supply Chain Management—A Literature Review on Emerging Economies," Sustainability, MDPI, vol. 12(17), pages 1-27, August.
    19. Calabrese, Armando & Costa, Roberta & Tiburzi, Luigi & Brem, Alexander, 2023. "Merging two revolutions: A human-artificial intelligence method to study how sustainability and Industry 4.0 are intertwined," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    20. Domicián Máté & Ni Made Estiyanti & Adam Novotny, 2024. "How to support innovative small firms? Bibliometric analysis and visualization of start-up incubation," Journal of Innovation and Entrepreneurship, Springer, vol. 13(1), pages 1-26, December.

    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:gam:jsusta:v:15:y:2023:i:7:p:6251-:d:1116652. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.