IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04252088.html

Towards an Analysis of the Adaptability Potential of a Collaborative Manufacturing System

Author

Listed:
  • Selma Ferhat

    (CGI - Centre Génie Industriel - IMT Mines Albi - IMT École nationale supérieure des Mines d'Albi-Carmaux - IMT - Institut Mines-Télécom [Paris] - Comue de Toulouse - Communauté d'universités et établissements de Toulouse)

  • Eric Ballot

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Matthieu Lauras

    (CGI - Centre Génie Industriel - IMT Mines Albi - IMT École nationale supérieure des Mines d'Albi-Carmaux - IMT - Institut Mines-Télécom [Paris] - Comue de Toulouse - Communauté d'universités et établissements de Toulouse)

  • Raphaël Oger

    (CGI - Centre Génie Industriel - IMT Mines Albi - IMT École nationale supérieure des Mines d'Albi-Carmaux - IMT - Institut Mines-Télécom [Paris] - Comue de Toulouse - Communauté d'universités et établissements de Toulouse)

Abstract

Due to unpredictable events, businesses have to enhance their adaptation potential. While traditional manufacturing systems have shown many advantages in stable environments, they struggle to cope with new events such those experienced during the COVID-19 pandemic. New approaches to adapting manufacturing systems are therefore to be designed, particularly with the desire to detect more simply and more quickly the potential to take charge unforeseen needs, especially in a Collaborative Network Organizations (CNOs) environment. This raises the research question of How to assess the adaptability potential of a manufacturing system in a context of (CNO)? In this regard, this paper proposes a functional framework of a decision making process that helps to identify the adaptability of manufacturing systems for a new product requirement within a collaborative network. An illustrative case is proposed to highlight the steps that constitute our functional framework.

Suggested Citation

  • Selma Ferhat & Eric Ballot & Matthieu Lauras & Raphaël Oger, 2023. "Towards an Analysis of the Adaptability Potential of a Collaborative Manufacturing System," Post-Print hal-04252088, HAL.
    • Handle: RePEc:hal:journl:hal-04252088
    DOI: 10.1007/978-3-031-42622-3_44
    Note: View the original document on HAL open archive server: https://hal.science/hal-04252088v1
    as

    Download full text from publisher

    File URL: https://hal.science/hal-04252088v1/document
    Download Restriction: no
    File URL: https://libkey.io/10.1007/978-3-031-42622-3_44?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
    ---><---

    References listed on IDEAS

    as
    1. Ahm Shamsuzzoha & Petri T. Helo, 2014. "Virtual business process management within collaborative manufacturing network: an implementation case," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 14(4), pages 319-339.
    2. May Tajima, 2005. "Adaptability and achieving supply chain agility," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 1(2), pages 134-146.
    3. Eeva Järvenpää & Niko Siltala & Otto Hylli & Minna Lanz, 2019. "The development of an ontology for describing the capabilities of manufacturing resources," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 959-978, February.
    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. Selma Ferhat & Raphaël Oger & Eric Ballot & Matthieu Lauras, 2024. "Building a collaborative manufacturing system’s network resilience through an adaptability potential analysis," Post-Print hal-04834992, HAL.
    2. Kai Zhang & Zhiying Tu & Dianhui Chu & Xiaoping Lu & Lucheng Chen, 2024. "Aic: an industrial knowledge graph with Abstraction-Instance-Capability reasoning abilities for personalized customization," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3419-3440, October.
    3. Wei Nie & Katharina Vita & Tariq Masood, 2024. "An ontology for defining and characterizing demonstration environments," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3501-3521, October.
    4. Hien Nguyen Ngoc & Ganix Lasa & Ion Iriarte, 2022. "Human-centred design in industry 4.0: case study review and opportunities for future research," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 35-76, January.
    5. Colin Reiff & Matthias Buser & Thomas Betten & Volkher Onuseit & Max Hoßfeld & Daniel Wehner & Oliver Riedel, 2021. "A Process-Planning Framework for Sustainable Manufacturing," Energies, MDPI, vol. 14(18), pages 1-28, September.
    6. Russell Tatenda Munodawafa & Satirenjit Kaur Johl, 2019. "Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies," Sustainability, MDPI, vol. 11(15), pages 1-21, August.
    7. Chenxi Yuan & Guoyan Li & Sagar Kamarthi & Xiaoning Jin & Mohsen Moghaddam, 2022. "Trends in intelligent manufacturing research: a keyword co-occurrence network based review," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 425-439, February.
    8. Patrick Sapel & Lina Molinas Comet & Iraklis Dimitriadis & Christian Hopmann & Stefan Decker, 2025. "A review and classification of manufacturing ontologies," Journal of Intelligent Manufacturing, Springer, vol. 36(6), pages 3669-3693, August.
    9. Muhammad Raza Naqvi & Linda Elmhadhbi & Arkopaul Sarkar & Bernard Archimede & Mohamed Hedi Karray, 2024. "Survey on ontology-based explainable AI in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3605-3627, December.
    10. 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.
    11. Xiaochen Zheng & Xiaodu Hu & Rebeca Arista & Jinzhi Lu & Jyri Sorvari & Joachim Lentes & Fernando Ubis & Dimitris Kiritsis, 2024. "A semantic-driven tradespace framework to accelerate aircraft manufacturing system design," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 175-198, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:hal:journl:hal-04252088. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.