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A Procedural Method to Build Decision Support Systems for Effective Interventions in Manufacturing – A Predictive Maintenance Example from the Spring Industry

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Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action (APMS 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 663))

Abstract

Predictive maintenance as one of the most prominent data-driven approaches enables companies to not only maximize the reliability of production processes but also to improve their efficiency. This is especially valuable in today’s volatile environment. Nevertheless, companies still struggle to implement digital technologies to track and improve their manufacturing processes, which includes data driven decision support systems. Based on practitioner interviews we identified the lack of guidance as a root cause. Additionally, literature reveals a shortcoming of methods especially suited for the needs of the manufacturing industry. This study contributes to this field by answering the question of how a procedural method can look like to guide practitioners to build decision support systems for effective interventions in manufacturing. Applying a design science research approach, the manuscript presents a seven-step procedural method to build decision support systems in manufacturing. The approach was designed and field tested at the example of a predictive maintenance model for a spring production process. The findings indicate that the incorporation of all stakeholders and the uncovering and use of implicit process knowledge in humans is of utmost importance for success.

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Authors and Affiliations

  1. University of St. Gallen, 9008, St. Gallen, Switzerland

    Ferdinand Deitermann, Lukas Budde & Thomas Friedli

  2. University of Applied Sciences OST, St. Gallen, Switzerland

    Roman Hänggi

Authors
  1. Ferdinand Deitermann
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  2. Lukas Budde
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  3. Thomas Friedli
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  4. Roman Hänggi
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Correspondence to Ferdinand Deitermann .

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Editors and Affiliations

  1. Pohang University of Science and Technology, Pohang, Korea (Republic of)

    Duck Young Kim

  2. ZF Friedrichshafen AG, Friedrichshafen, Germany

    Gregor von Cieminski

  3. Tecnológico de Monterrey, Mexico City, Mexico

    David Romero

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Deitermann, F., Budde, L., Friedli, T., Hänggi, R. (2022). A Procedural Method to Build Decision Support Systems for Effective Interventions in Manufacturing – A Predictive Maintenance Example from the Spring Industry. In: Kim, D.Y., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action. APMS 2022. IFIP Advances in Information and Communication Technology, vol 663. Springer, Cham. https://doi.org/10.1007/978-3-031-16407-1_24

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  • DOI: https://doi.org/10.1007/978-3-031-16407-1_24

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  • Online ISBN: 978-3-031-16407-1

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