IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i13p9874-d1176050.html
   My bibliography  Save this article

Digital Twin: An Added Value for Digital CONWIP in the Context of Industry 4.0

Author

Listed:
  • Latifa Benhamou

    (Laboratory of Automation, Mechanics, and Industrial and Human Computer Science, National Center for Scientific Research, Arts et Métiers Institute of Technology, 75013 Paris, France)

  • Samir Lamouri

    (Laboratory of Automation, Mechanics, and Industrial and Human Computer Science, National Center for Scientific Research, Arts et Métiers Institute of Technology, 75013 Paris, France)

  • Patrick Burlat

    (WIPSIM, Systems and Software Consulting Firm, 42000 Saint-Étienne, France)

  • Vincent Giard

    (Laboratory of Analysis and Modeling of Decision Support Systems, Université Paris-Dauphine, PSL Research University, 75006 Paris, France)

Abstract

Despite technological progress and a large amount of research on Industry 4.0, digital transformation remains a complex process that most manufacturers are hesitant to invest in. Interest in digital Kanban, for example, remains low compared with traditional Kanban, which is widely used. This applies to the other card-based production control systems, including CONstant Work-In-Process (CONWIP), which is the focus of this paper. In an industrial context where digitization and Industry 4.0 are the main trends, one may wonder why traditional CONWIP is preferred to digital CONWIP. Following a praxeological approach (i.e., study of practice and instrumentation), this article explores the strengths and weaknesses of the CONWIP practice, in both its paper and electronic versions, while taking into account the human dimension. The aim is to motivate potential CONWIP users to implement it in its digital mode and to show them how a Digital Twin-based solution can overcome the managerial problems that arise with digitization while enabling improved performance. As an illustration, experience feedback from several companies using Digital Twin with CONWIP is provided.

Suggested Citation

  • Latifa Benhamou & Samir Lamouri & Patrick Burlat & Vincent Giard, 2023. "Digital Twin: An Added Value for Digital CONWIP in the Context of Industry 4.0," Sustainability, MDPI, vol. 15(13), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:9874-:d:1176050
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/13/9874/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/13/9874/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yann Jaegler & Anicia Jaegler & Patrick Burlat & Samir Lamouri & Damien Trentesaux, 2018. "The ConWip production control system: a systematic review and classification," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5736-5757, September.
    2. Samayita Guha & Subodha Kumar, 2018. "Emergence of Big Data Research in Operations Management, Information Systems, and Healthcare: Past Contributions and Future Roadmap," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1724-1735, September.
    3. Kessler, Melanie & Arlinghaus, Julia C. & Rosca, Eugenia & Zimmermann, Manuel, 2022. "Curse or Blessing? Exploring risk factors of digital technologies in industrial operations," International Journal of Production Economics, Elsevier, vol. 243(C).
    4. Huang, Min & Wang, Dingwei & Ip, W. H., 1998. "Simulation study of CONWIP for a cold rolling plant," International Journal of Production Economics, Elsevier, vol. 54(3), pages 257-266, May.
    5. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    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. Padhi, Sidhartha S. & Mukherjee, Soumyatanu & Edwin Cheng, T.C., 2024. "Optimal investment decision for industry 4.0 under uncertainties of capability and competence building for managing supply chain risks," International Journal of Production Economics, Elsevier, vol. 267(C).
    2. Fernando José Gómez Paredes & Moacir Godinho Filho & Matthias Thürer & Nuno O. Fernandes & Charbel José Chiappeta Jabbour, 2022. "Factors for choosing production control systems in make-to-order shops: a systematic literature review," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 639-674, March.
    3. Yang, Yefei & Yee, Rachel W.Y., 2022. "The effect of process digitalization initiative on firm performance: A dynamic capability development perspective," International Journal of Production Economics, Elsevier, vol. 254(C).
    4. Bag, Surajit & Dhamija, Pavitra & Singh, Rajesh Kumar & Rahman, Muhammad Sabbir & Sreedharan, V. Raja, 2023. "Big data analytics and artificial intelligence technologies based collaborative platform empowering absorptive capacity in health care supply chain: An empirical study," Journal of Business Research, Elsevier, vol. 154(C).
    5. Hamann-Lohmer, Jacob & Bendig, Miriam & Lasch, Rainer, 2023. "Investigating the impact of digital transformation on relationship and collaboration dynamics in supply chains and manufacturing networks – A multi-case study," International Journal of Production Economics, Elsevier, vol. 262(C).
    6. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    7. Lai, Kee-hung & Feng, Yunting & Zhu, Qinghua, 2023. "Digital transformation for green supply chain innovation in manufacturing operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    8. Unai Apaolaza & Aitor Orue & Aitor Lizarralde & Aitor Oyarbide-Zubillaga, 2022. "Competitive Improvement through Integrated Management of Sales and Operations," Sustainability, MDPI, vol. 14(5), pages 1-18, February.
    9. Maximilian Klöckner & Christoph G. Schmidt & Stephan M. Wagner, 2022. "When Blockchain Creates Shareholder Value: Empirical Evidence from International Firm Announcements," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 46-64, January.
    10. Di Zio, Simone & Bolzan, Mario & Marozzi, Marco, 2021. "Classification of Delphi outputs through robust ranking and fuzzy clustering for Delphi-based scenarios," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    11. Suyuan Luo & Tsan-Ming Choi, 2024. "Great partners: how deep learning and blockchain help improve business operations together," Annals of Operations Research, Springer, vol. 339(1), pages 53-78, August.
    12. Behl, Abhishek & Gaur, Jighyasu & Pereira, Vijay & Yadav, Rambalak & Laker, Benjamin, 2022. "Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19 – A multi-theoretical approach," Journal of Business Research, Elsevier, vol. 148(C), pages 378-389.
    13. Suyuan Luo & Tsan‐Ming Choi, 2022. "E‐commerce supply chains with considerations of cyber‐security: Should governments play a role?," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2107-2126, May.
    14. Saveria Olga Murielle Boulanger, 2022. "The Roadmap to Smart Cities: A Bibliometric Literature Review on Smart Cities’ Trends before and after the COVID-19 Pandemic," Energies, MDPI, vol. 15(24), pages 1-19, December.
    15. Ortiz-Barrios, Miguel & Arias-Fonseca, Sebastián & Ishizaka, Alessio & Barbati, Maria & Avendaño-Collante, Betty & Navarro-Jiménez, Eduardo, 2023. "Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study," Journal of Business Research, Elsevier, vol. 160(C).
    16. Belhadi, Amine & Venkatesh, Mani & Kamble, Sachin & Abedin, Mohammad Zoynul, 2024. "Data-driven digital transformation for supply chain carbon neutrality: Insights from cross-sector supply chain," International Journal of Production Economics, Elsevier, vol. 270(C).
    17. Eva Labro & Mark Lang & Jim Omartian, 2019. "Predictive Analytics and Organizational Architecture: Plant-Level Evidence from Census Data," Working Papers 19-02, Center for Economic Studies, U.S. Census Bureau.
    18. Liangfei Qiu & Yili (Kevin) Hong & Andrew Whinston, 2022. "Special Issue of Production and Operations Management “Social Technologies in Operations”," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 868-869, February.
    19. Xiangyu Chang & Yinghui Huang & Mei Li & Xin Bo & Subodha Kumar, 2021. "Efficient Detection of Environmental Violators: A Big Data Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1246-1270, May.
    20. Jason R. W. Merrick & Claire A. Dorsey & Bo Wang & Martha Grabowski & John R. Harrald, 2022. "Measuring Prediction Accuracy in a Maritime Accident Warning System," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 819-827, February.

    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:13:p:9874-:d:1176050. 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.