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Modeling production disorder: Procedures for digital twins of flexibility-driven manufacturing systems

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  • Neto, Anis Assad
  • Ribeiro da Silva, Elias
  • Deschamps, Fernando
  • do Nascimento Junior, Laercio Alves
  • Pinheiro de Lima, Edson

Abstract

While the achievement of mix flexibility is a key driver of competitive advantage for a manufacturing organization, it also represents a source of disruption on the shop floor that may compromise managers' ability to diagnose problems, predict behavior, and make decisions adequately. Given this challenge, the digital twin emerges as a potential tool to reestablish managers’ operational visibility. Its architecture allows for a manufacturing system model to be continuously updated and for management support services to be promptly delivered. Although some studies explore the use of the digital twin to solve specific flexibility-related challenges, the literature lacks a normative and systematic approach to guide the application of this technological architecture in a comprehensive set of flexibility scenarios. To address this gap, based on the normative knowledge acquired from the literature and subject-matter experts, we present procedures to design, implement, and use the digital twin within organizations that suffer flexibility-driven disruptions. We demonstrate the application of these procedures through a case in a large automotive parts manufacturer. Results show that the procedures effectively operationalize a digital twin architecture aimed at contributing to the achievement of a flexible production strategy.

Suggested Citation

  • Neto, Anis Assad & Ribeiro da Silva, Elias & Deschamps, Fernando & do Nascimento Junior, Laercio Alves & Pinheiro de Lima, Edson, 2023. "Modeling production disorder: Procedures for digital twins of flexibility-driven manufacturing systems," International Journal of Production Economics, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:proeco:v:260:y:2023:i:c:s0925527323000786
    DOI: 10.1016/j.ijpe.2023.108846
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    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Da Silveira, Giovani & Borenstein, Denis & Fogliatto, Flavio S., 2001. "Mass customization: Literature review and research directions," International Journal of Production Economics, Elsevier, vol. 72(1), pages 1-13, June.
    3. 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.
    4. Marshall L. Fisher & Christopher D. Ittner, 1999. "The Impact of Product Variety on Automobile Assembly Operations: Empirical Evidence and Simulation Analysis," Management Science, INFORMS, vol. 45(6), pages 771-786, June.
    5. Yongxin Liao & Fernando Deschamps & Eduardo de Freitas Rocha Loures & Luiz Felipe Pierin Ramos, 2017. "Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal," International Journal of Production Research, Taylor & Francis Journals, vol. 55(12), pages 3609-3629, June.
    6. Elisa Negri & Vibhor Pandhare & Laura Cattaneo & Jaskaran Singh & Marco Macchi & Jay Lee, 2021. "Field-synchronized Digital Twin framework for production scheduling with uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1207-1228, April.
    7. Guanghui Zhou & Chao Zhang & Zhi Li & Kai Ding & Chuang Wang, 2020. "Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(4), pages 1034-1051, February.
    8. Kai Ding & Felix T.S. Chan & Xudong Zhang & Guanghui Zhou & Fuqiang Zhang, 2019. "Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors," International Journal of Production Research, Taylor & Francis Journals, vol. 57(20), pages 6315-6334, October.
    9. Guo, Z.X. & Ngai, E.W.T. & Yang, Can & Liang, Xuedong, 2015. "An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment," International Journal of Production Economics, Elsevier, vol. 159(C), pages 16-28.
    10. Jinjiang Wang & Lunkuan Ye & Robert X. Gao & Chen Li & Laibin Zhang, 2019. "Digital Twin for rotating machinery fault diagnosis in smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3920-3934, June.
    11. Gansterer, Margaretha & Almeder, Christian & Hartl, Richard F., 2014. "Simulation-based optimization methods for setting production planning parameters," International Journal of Production Economics, Elsevier, vol. 151(C), pages 206-213.
    12. Fogliatto, Flavio S. & da Silveira, Giovani J.C. & Borenstein, Denis, 2012. "The mass customization decade: An updated review of the literature," International Journal of Production Economics, Elsevier, vol. 138(1), pages 14-25.
    13. Jan L.G. Dietz & Jan A.P. Hoogervorst & Antonia Albani & David Aveiro & Eduard Babkin & Joseph Barjis & Artur Caetano & Philip Huysmans & Junichi Iijima & Steven J.H. Van Kervel & Hans Mulder & Martin, 2013. "The discipline of enterprise engineering," International Journal of Organisational Design and Engineering, Inderscience Enterprises Ltd, vol. 3(1), pages 86-114.
    14. Dalenogare, Lucas Santos & Benitez, Guilherme Brittes & Ayala, Néstor Fabián & Frank, Alejandro Germán, 2018. "The expected contribution of Industry 4.0 technologies for industrial performance," International Journal of Production Economics, Elsevier, vol. 204(C), pages 383-394.
    15. Donald Gerwin, 1993. "Manufacturing Flexibility: A Strategic Perspective," Management Science, INFORMS, vol. 39(4), pages 395-410, April.
    16. Buzacott, John A., 2000. "Service system structure," International Journal of Production Economics, Elsevier, vol. 68(1), pages 15-27, October.
    17. Culot, Giovanna & Orzes, Guido & Sartor, Marco & Nassimbeni, Guido, 2020. "The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    18. Konstantinos Mykoniatis & Gregory A. Harris, 2021. "A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1899-1911, October.
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