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Unlocking the Potential of Predictive Maintenance for Intelligent Manufacturing: a Case Study On Potentials, Barriers, and Critical Success Factors

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  • Marcel André Hoffmann

    (Technische Universität Dresden)

  • Rainer Lasch

    (Technische Universität Dresden)

Abstract

Predictive maintenance (PdM) is a data-driven maintenance strategy that aims to avoid unplanned downtimes by predicting the remaining lifetime of maintenance objects. Thus, unnecessary replacements of spare parts and critical process disturbances due to breakdowns can be avoided. Despite the widely recognized advantages of this technology, the number of successful applications in practice is still very limited. Our study aims to address the theory-practice gap by conducting a comprehensive case study involving 15 expert interviews with industry professionals to uncover critical factors that hinder the successful implementation of PdM. Our findings shed light on the underlying reasons for a hesitant PdM implementation, including challenges related to digital readiness, data quality and accessibility, technological integration, and maintenance organization. By providing an in-depth analysis of these factors, our study offers valuable insights and guidelines to improve the implementation success rate of PdM in the industrial context. Based on the empirical findings, we present critical implementation factors and develop a framework with ten propositions that aim to dismantle barriers in the industrial application process of PdM and stimulate further research in academia.

Suggested Citation

  • Marcel André Hoffmann & Rainer Lasch, 2025. "Unlocking the Potential of Predictive Maintenance for Intelligent Manufacturing: a Case Study On Potentials, Barriers, and Critical Success Factors," Schmalenbach Journal of Business Research, Springer, vol. 77(1), pages 27-55, March.
    • Handle: RePEc:spr:sjobre:v:77:y:2025:i:1:d:10.1007_s41471-024-00204-3
    DOI: 10.1007/s41471-024-00204-3
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    References listed on IDEAS

    as
    1. Gregory W. Vogl & Brian A. Weiss & Moneer Helu, 2019. "A review of diagnostic and prognostic capabilities and best practices for manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 79-95, January.
    2. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    3. Shin, Won & Han, Jeongyun & Rhee, Wonjong, 2021. "AI-assistance for predictive maintenance of renewable energy systems," Energy, Elsevier, vol. 221(C).
    4. Jens Passlick & Sonja Dreyer & Daniel Olivotti & Lukas Grützner & Dennis Eilers & Michael H. Breitner, 2021. "Predictive maintenance as an internet of things enabled business model: A taxonomy," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 67-87, March.
    5. E. Traini & G. Bruno & F. Lombardi, 2021. "Tool condition monitoring framework for predictive maintenance: a case study on milling process," International Journal of Production Research, Taylor & Francis Journals, vol. 59(23), pages 7179-7193, December.
    6. Nishit Kumar Srivastava & Sandeep Mondal, 2016. "Development of framework for predictive maintenance in Indian manufacturing sector," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 24(1), pages 73-98.
    7. Abdenour Soualhi & Mourad Lamraoui & Bilal Elyousfi & Hubert Razik, 2022. "PHM SURVEY: Implementation of Prognostic Methods for Monitoring Industrial Systems," Energies, MDPI, vol. 15(19), pages 1-24, September.
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    10. Hamed Sadegh Kouhestani & Xiaoping Yi & Guoqing Qi & Xunliang Liu & Ruimin Wang & Yang Gao & Xiao Yu & Lin Liu, 2022. "Prognosis and Health Management (PHM) of Solid-State Batteries: Perspectives, Challenges, and Opportunities," Energies, MDPI, vol. 15(18), pages 1-26, September.
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