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Protocole expérimental visant l'étude de l’IA centrée sur l'humain dans le contexte de l'Industrie 5.0 : Application en réalité augmentée

Author

Listed:
  • Laurent Joblot

    (Arts et Métiers Sciences et Technologies - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université)

  • Magnani Florian

    (CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon, ECM - École Centrale de Marseille)

  • Frédéric Rosin

    (Arts et Métiers Sciences et Technologies - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université)

  • Robert Pellerin

    (MAGI - Département de Mathématiques et de Génie Industriel - EPM - École Polytechnique de Montréal)

  • Mario Passalacqua

    (MAGI - Département de Mathématiques et de Génie Industriel - EPM - École Polytechnique de Montréal)

Abstract

Industry 4.0's primary goal is generally to create a learning and agile enterprise, capable of adapting continuously to changing conditions through new technologies' utilization. However, the results of previous developments remain mitigated, mainly due to a primarily techno-centric approach. In contrast, the concept of Industry 5.0 is now defined as a human-centred approach, including social, societal, and environmental considerations. The evolution towards new models of agile organizations implies, in particular, greater autonomy for teams based on improved and accelerated decision-making. However, I4.0 technology's influence on the performance, motivation, engagement, and cognitive load of employees in a production setting remains largely under-researched. In this article, we present an experimental methodology to address this gap. We discuss its future application to a use case in which artificial intelligence (AI) and augmented reality (AR) are implemented to aid the operator in an error-detection manufacturing task. Finally, the methodological choices are elucidated, in preparation for the upcoming testing and operational implementation phases of the system. Results from the application of the experimental methodology will be used to identify the key factors contributing to the success and failure of AI and AR system design and implementation. Ultimately, we aim to understand how to promote positive outcomes for the employees using the system, in terms of performance, engagement, motivation, and autonomy.

Suggested Citation

  • Laurent Joblot & Magnani Florian & Frédéric Rosin & Robert Pellerin & Mario Passalacqua, 2023. "Protocole expérimental visant l'étude de l’IA centrée sur l'humain dans le contexte de l'Industrie 5.0 : Application en réalité augmentée," Post-Print hal-04142374, HAL.
    • Handle: RePEc:hal:journl:hal-04142374
    Note: View the original document on HAL open archive server: https://hal.science/hal-04142374v1
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    References listed on IDEAS

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    1. 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.
    2. Berna Haktanirlar Ulutas & N. Fırat Özkan & Rafał Michalski, 2020. "Application of hidden Markov models to eye tracking data analysis of visual quality inspection operations," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(2), pages 761-777, June.
    3. Zheng, Ting & Glock, C. H. & Grosse, E. H., 2022. "Opportunities for using eye tracking technology in manufacturing and logistics: Systematic literature review and research agenda," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 133749, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Andrzej Szajna & Mariusz Kostrzewski, 2022. "AR-AI Tools as a Response to High Employee Turnover and Shortages in Manufacturing during Regular, Pandemic, and War Times," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
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