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Beyond the Lab: Exploring the Socio-Technical Implications of Machine Learning in Biopharmaceutical Manufacturing

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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

In the data-rich but knowledge-poor domain of production management systems, the utilization of machine learning (ML) for lead-time prediction has gained increasing attention. Despite several efforts focusing on ML and regression techniques, the selection of features for lead-time prediction remains a challenge. The purpose of this study is to explore the socio-technical challenges and benefits of applying ML to predict lead-time in manually executed tasks in the biopharmaceutical industry, with a particular emphasis on the quality control of raw materials and semi-finished products. Through a case study and empirical analysis, the research identifies critical factors affecting lead-time prediction in manual tasks and evaluates the socio-technical implications of implementing ML-based solutions. Moreover, the study provides valuable insights into the practical challenges and potential advantages of adopting ML techniques for lead-time prediction in the biopharmaceutical sector, offering a comprehensive understanding of the complex interplay between technology and human factors. Finally, we discuss the implications of the findings for managers and staff responsible for the planning of manual tasks, providing actionable recommendations to improve production efficiency and lead-time prediction accuracy. This research contributes to the growing body of knowledge on the integration of ML in production management systems and highlights the need for further investigation to harness the full potential of ML in addressing the unique challenges of the biopharmaceutical industry.

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Acknowledgments

The authors acknowledge the support of Swedish Innovation Agency (VINNOVA) and its funding program Produktion2030. This study is part of the EXPLAIN project (Explainable and Learning Production Logistics by Artificial Intelligence). The study received support from the Korea Institute for Advancement of Technology (KIAT) through a grant funded by the Korea Government (MOTIE) (P0017304, Human Resource Development Program for Industrial Innovation).

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Authors and Affiliations

  1. KTH Royal Institute of Technology, Södertälje 151 36, Stockholm, Sweden

    Erik Flores-García, Yongkuk Jeong & Magnus Wiktorsson

  2. Seoul National University, Seoul, Republic of Korea

    So Hyun Nam & Jong Hun Woo

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  1. Erik Flores-García
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  3. Yongkuk Jeong
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  4. Magnus Wiktorsson
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  5. Jong Hun Woo
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Corresponding author

Correspondence to Erik Flores-García .

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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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Flores-García, E., Nam, S.H., Jeong, Y., Wiktorsson, M., Woo, J.H. (2023). Beyond the Lab: Exploring the Socio-Technical Implications of Machine Learning in Biopharmaceutical Manufacturing. 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_32

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  • DOI: https://doi.org/10.1007/978-3-031-43670-3_32

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-43670-3

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