IDEAS home Printed from https://ideas.repec.org/a/spr/sjobre/v77y2025i1d10.1007_s41471-024-00204-3.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s41471-024-00204-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1007/s41471-024-00204-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Arnaldo Rabello de Aguiar Vallim Filho & Daniel Farina Moraes & Marco Vinicius Bhering de Aguiar Vallim & Leilton Santos da Silva & Leandro Augusto da Silva, 2022. "A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case," Energies, MDPI, vol. 15(10), pages 1-41, May.
    6. 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).
    7. 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.
    8. Shin, Won & Han, Jeongyun & Rhee, Wonjong, 2021. "AI-assistance for predictive maintenance of renewable energy systems," Energy, Elsevier, vol. 221(C).
    9. 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.
    10. Qi Feng & J. George Shanthikumar, 2018. "How Research in Production and Operations Management May Evolve in the Era of Big Data," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1670-1684, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lewis, Austin D. & Groth, Katrina M., 2022. "Metrics for evaluating the performance of complex engineering system health monitoring models," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    2. Maximilian Klöckner & Christoph G. Schmidt & Stephan M. Wagner, 2022. "When Blockchain Creates Shareholder Value: Empirical Evidence from International Firm Announcements," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 46-64, January.
    3. Mingfei Li & Jiajian Wu & Zhengpeng Chen & Jiangbo Dong & Zhiping Peng & Kai Xiong & Mumin Rao & Chuangting Chen & Xi Li, 2022. "Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning," Energies, MDPI, vol. 15(17), pages 1-20, August.
    4. Kamei, Sayaka & Taghipour, Sharareh, 2023. "A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    5. Liangfei Qiu & Yili (Kevin) Hong & Andrew Whinston, 2022. "Special Issue of Production and Operations Management “Social Technologies in Operations”," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 868-869, February.
    6. Jason R. W. Merrick & Claire A. Dorsey & Bo Wang & Martha Grabowski & John R. Harrald, 2022. "Measuring Prediction Accuracy in a Maritime Accident Warning System," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 819-827, February.
    7. Zuo, Jian & Cadet, Catherine & Li, Zhongliang & Bérenguer, Christophe & Outbib, Rachid, 2024. "A deterioration-aware energy management strategy for the lifetime improvement of a multi-stack fuel cell system subject to a random dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    8. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
    10. Jiabao Zou & Ping Lin, 2025. "Multichannel Attention-Based TCN-GRU Network for Remaining Useful Life Prediction of Aero-Engines," Energies, MDPI, vol. 18(8), pages 1-15, April.
    11. Li, Xiao Yan & Cheng, De Jun & Fang, Xi Feng & Zhang, Chun Yan & Wang, Yu Feng, 2024. "A novel data augmentation strategy for aeroengine multitask prognosis based on degradation behavior extrapolation and diversity-usability trade-off," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    12. Yuchen Liu & Chaoxu Li & Man Li, 2025. "Data-Driven Online State Prediction Method for the Traction Motors of Electric Multiple Units (EMUs)," Sustainability, MDPI, vol. 17(9), pages 1-22, May.
    13. Zheng, Shuwen & Wang, Chong & Zio, Enrico & Liu, Jie, 2024. "Fault detection in complex mechatronic systems by a hierarchical graph convolution attention network based on causal paths," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    14. Yiwei Wang & Jian Zhou & Lianyu Zheng & Christian Gogu, 2022. "An end-to-end fault diagnostics method based on convolutional neural network for rotating machinery with multiple case studies," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 809-830, March.
    15. Zaitseva, Elena & Levashenko, Vitaly & Rabcan, Jan, 2023. "A new method for analysis of Multi-State systems based on Multi-valued decision diagram under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    16. repec:dar:wpaper:132320 is not listed on IDEAS
    17. Yao Li & Zhengcai Zhao & Yucan Fu & Qingliang Chen, 2024. "A novel approach for tool condition monitoring based on transfer learning of deep neural networks using time–frequency images," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 1159-1171, March.
    18. Lyu, Dongzhen & Niu, Guangxing & Liu, Enhui & Zhang, Bin & Chen, Gang & Yang, Tao & Zio, Enrico, 2022. "Time space modelling for fault diagnosis and prognosis with uncertainty management: A general theoretical formulation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    19. Mikko Ketokivi & Joseph T. Mahoney, 2020. "Transaction Cost Economics As a Theory of Supply Chain Efficiency," Production and Operations Management, Production and Operations Management Society, vol. 29(4), pages 1011-1031, April.
    20. Subodha Kumar & Rakesh R. Mallipeddi, 2022. "Impact of cybersecurity on operations and supply chain management: Emerging trends and future research directions," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4488-4500, December.
    21. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sjobre:v:77:y:2025:i:1:d:10.1007_s41471-024-00204-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.