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Towards Human-Centric Manufacturing: Exploring the Role of Human Digital Twins in Industry 5.0

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  • Ilaria Bucci

    (Department of Industrial Engineering, University of Florence, Viale Morgagni 40/44, 50134 Florence, Italy
    Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini 4/B, 20156 Milan, Italy)

  • Virginia Fani

    (Department of Industrial Engineering, University of Florence, Viale Morgagni 40/44, 50134 Florence, Italy)

  • Romeo Bandinelli

    (Department of Industrial Engineering, University of Florence, Viale Morgagni 40/44, 50134 Florence, Italy)

Abstract

Human-centricity, a cornerstone of Industry 5.0, emphasizes the central role of human needs and capabilities in the technological landscape of modern manufacturing. As Digital Twins (DTs) become a core technology of Industry 4.0, the evolution towards Human Digital Twins (HDTs) marks a significant shift to enhance human-system integration. HDTs serve as digital replicas that mirror human characteristics directly in system design and performance, facilitating a more nuanced approach to smart manufacturing. This paper addresses the critical need for deeper investigation into HDTs to fully leverage their potential in promoting human-centric manufacturing. Through a comprehensive review, the current state and rapid evolution of HDT frameworks and architectures within Industry 5.0 settings are explored. The enabling technologies that underpin HDTs, their applications across various industrial scenarios, and the challenges in their development are discussed. The analysis not only underscores the importance of HDTs in meeting the diverse needs of workers but also outlines future research directions to further empower individuals within the adaptive and intelligent manufacturing systems shaped by Industry 5.0.

Suggested Citation

  • Ilaria Bucci & Virginia Fani & Romeo Bandinelli, 2024. "Towards Human-Centric Manufacturing: Exploring the Role of Human Digital Twins in Industry 5.0," Sustainability, MDPI, vol. 17(1), pages 1-28, December.
    • Handle: RePEc:gam:jsusta:v:17:y:2024:i:1:p:129-:d:1554771
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    References listed on IDEAS

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    1. Weng Siew Lam & Weng Hoe Lam & Pei Fun Lee, 2023. "A Bibliometric Analysis of Digital Twin in the Supply Chain," Mathematics, MDPI, vol. 11(15), pages 1-24, July.
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