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Modeling of the Master Production Schedule for the Digital Transition of Manufacturing SMEs in the Context of Industry 4.0

Author

Listed:
  • Estefania Tobon-Valencia

    (Flow&Co. (Groupe Square Management), Chercheure au Square Research Center, 173 Avenue Achille Peretti, 92200 Neuilly-sur Seine, France)

  • Samir Lamouri

    (Laboratoire d’Automatique, de Mécanique et d’Informatique Industrielles et Humaines (LAMIH)—UMR CNRS 8201, Arts et Métiers, 151 Boulevard de l’hôpital, 75013 Paris, France)

  • Robert Pellerin

    (Department of Mathematics and Industrial Engineering, École Polytechnique Montréal, Montreal, QC H3T AJ4, Canada)

  • Alexandre Moeuf

    (Pytho Performance Partners, 119 Chemin du Vacher, 38260 Thodure, France)

Abstract

The purpose of this article is to propose a guide for the digital transformation (4.0) of a manufacturing SME’s medium-term production planning process, the master production schedule (MPS). A model of the current MPS process of a group of SMEs is presented as a starting point toward digitization. The current state of this process reveals a lack of tools to support decision making and the need to increase the reliability of input data and to make the process more agile. Industry 4.0 technologies and process modeling could increase agility in the planning process. However, the digital transformation of medium-term planning activities in SMEs has not been studied. To fill this gap, a group of six experts was consulted. The novelty of this study was to identify the Industry 4.0 technologies that could improve medium-term planning and integrate them into a standardized MPS process model. This model is an ultimate point of digitization that cannot be achieved immediately by any SME, but only after several cycles of planning, deployment, and improvement. Therefore, this research also provides a method to help SMEs determine how to start the digitization of their MPS process.

Suggested Citation

  • Estefania Tobon-Valencia & Samir Lamouri & Robert Pellerin & Alexandre Moeuf, 2022. "Modeling of the Master Production Schedule for the Digital Transition of Manufacturing SMEs in the Context of Industry 4.0," Sustainability, MDPI, vol. 14(19), pages 1-28, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12562-:d:932161
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    References listed on IDEAS

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    1. Frédéric Rosin & Pascal Forget & Samir Lamouri & Robert Pellerin, 2022. "Enhancing the Decision-Making Process through Industry 4.0 Technologies," Sustainability, MDPI, vol. 14(1), pages 1-35, January.
    2. Osterrieder, Philipp & Budde, Lukas & Friedli, Thomas, 2020. "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 221(C).
    3. Alexandre Dolgui & Dmitry Ivanov & Suresh P. Sethi & Boris Sokolov, 2019. "Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications," International Journal of Production Research, Taylor & Francis Journals, vol. 57(2), pages 411-432, January.
    4. Hedenstierna, Carl Philip T. & Disney, Stephen M., 2018. "Avoiding the capacity cost trap: Three means of smoothing under cyclical production planning," International Journal of Production Economics, Elsevier, vol. 201(C), pages 149-162.
    5. Fateme Akhoondi & M.M. Lotfi, 2016. "A heuristic algorithm for master production scheduling problem with controllable processing times and scenario-based demands," International Journal of Production Research, Taylor & Francis Journals, vol. 54(12), pages 3659-3676, June.
    6. Jonsson, Patrik & Kjellsdotter Ivert, Linea, 2015. "Improving performance with sophisticated master production scheduling," International Journal of Production Economics, Elsevier, vol. 168(C), pages 118-130.
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    Cited by:

    1. Özköse, Hakan & Güney, Gül, 2023. "The effects of industry 4.0 on productivity: A scientific mapping study," Technology in Society, Elsevier, vol. 75(C).

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