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Data-Driven Analysis and Assignment of Manual Assembly Production Lines

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Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures (APMS 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 691))

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Abstract

Industry 5.0 envisions a future of manufacturing that realizes personalized production, increased efficiency, and sustainability with a focus on human-centric production. While there have been numerous studies aimed at improving the efficiency of assembly processes, there is a need for smart manufacturing technologies that consider the individual characteristics of workers. This study explores manufacturing shop-floor data collected through wearable devices to analyze the characteristics of processes and workers quantitatively, which have traditionally been evaluated qualitatively. In addition, this study proposes a mathematical method to define the process difficulty and the process proficiency of workers based on data, and presents a heuristic algorithm for worker assignment using a process proficiency matrix of workers by processes. A case study is conducted in a laboratory environment mimicking a home appliance assembly production line in the United States to validate the feasibility of the proposed methodology. This study presents a novel approach to examining human-centric assembly production lines and provides empirical evidence supporting the efficiency of data-driven analysis and improvement techniques for human-centric assembly lines.

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Acknowledgements

This research was supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the Virtual Engineering Service Platform program (P0022335) and the International Cooperative R&D program. (Project No. 0022929).

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Authors and Affiliations

  1. Sungkyunkwan University, Suwon, 16419, Republic of Korea

    Changha Lee, Jongpil Yun, Goo-Young Kim, Junwoo Lim & Sang Do Noh

  2. LG Electronics, Pyeongtaek, 17709, Republic of Korea

    Yongjin Kim

Authors
  1. Changha Lee
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  2. Jongpil Yun
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  3. Goo-Young Kim
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  4. Junwoo Lim
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  5. Sang Do Noh
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  6. Yongjin Kim
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Corresponding author

Correspondence to Sang Do Noh .

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Erlend Alfnes

  2. Norwegian University of Science and Technology, Trondheim, Norway

    Anita Romsdal

  3. Norwegian University of Science and Technology, Trondheim, Norway

    Jan Ola Strandhagen

  4. ZF Friedrichshafen AG, Friedrichshafen, Germany

    Gregor von Cieminski

  5. Tecnológico de Monterrey, Mexico City, Mexico

    David Romero

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Lee, C., Yun, J., Kim, GY., Lim, J., Do Noh, S., Kim, Y. (2023). Data-Driven Analysis and Assignment of Manual Assembly Production Lines. In: Alfnes, E., Romsdal, A., Strandhagen, J.O., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. APMS 2023. IFIP Advances in Information and Communication Technology, vol 691. Springer, Cham. https://doi.org/10.1007/978-3-031-43670-3_37

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  • DOI: https://doi.org/10.1007/978-3-031-43670-3_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43669-7

  • Online ISBN: 978-3-031-43670-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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