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Industry 5.0 use cases development framework

Article dans une revue avec comité de lecture
Author
GOUJON, Alexandre
1067790 Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
ccROSIN, Frédéric
1067790 Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
ccMAGNANI, Florian
108098 Centre d'Études et de Recherche en Gestion d'Aix-Marseille [CERGAM]
300415 École Centrale de Marseille [ECM]
ccLAMOURI, Samir
1067790 Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 [LAMIH]
ccPELLERIN, Robert
57241 École Polytechnique de Montréal [EPM]
ccJOBLOT, Laurent
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]

URI
http://hdl.handle.net/10985/24785
DOI
10.1080/00207543.2024.2307505
Date
2024-01-31
Journal
International Journal of Production Research

Abstract

The Industry 5.0 concept has placed human needs at the heart of industrial processes. This raises the question of how new technologies can enhance employee decision-making processes and influence the evolution of team autonomy. Recent studies have shown that the best way to measure these impacts is to conduct experiments in complex and realistic environmental settings. However, the main methods cannot satisfy this requirement while controlling the events and associated variables, whereas a set of use cases can. Therefore, a model should be defined to generate and structure these use cases while validating their relevance. Following the decomposition of the global research objective and case-definition recommendations, this study proposes a framework for designing complementary use cases to evaluate the impact of new technologies on emerging autonomy models in a structured, realistic, and global manner. Based on widely recognised related work, the 6-step framework helps define a coherent context specifying the business process model, agent, autonomy, technologies to be implemented, their fields of action, detailed variable collection protocol, and experimental setup. A cross-analysis of existing cases from the literature and empirical use of the framework validated the relevance of the model in designing experimental environments that are close to real-world settings.

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