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Smart retrofitting in maintenance: a systematic literature review

Author

Listed:
  • David Sanchez-Londono

    (Universidad de los Andes
    Politecnico di Milano)

  • Giacomo Barbieri

    (Universidad de los Andes)

  • Luca Fumagalli

    (Politecnico di Milano)

Abstract

The last decade saw the rise of digitalization and data-supported decision making in the manufacturing industry: the Fourth Industrial Revolution. This trend, also known as Industry 4.0, allows manufacturing enterprises to discover manufacturing uncertainties and measure their real manufacturing capability. One of the ways in which Industry 4.0 trends have been exploited is in the improvement of maintenance, which went from following planning-focused paradigms to more proactive-focused stances. Enabling the Industry 4.0 vision for maintenance purposes has historically required companies to either replace or upgrade their existing legacy devices. It is through the latter course of action that Smart retrofitting in maintenance (SRM) intends to bring value to enterprises. This work aims to present a systematic literature review on SRM, utilizing the oft-cited PRISMA methodology. Through this analysis, a definition of SRM that reflects the current state of the art is proposed. Furthermore, the research in SRM applied in the context of different maintenance strategies is assessed (i.e. reactive, planned, proactive and strategic maintenance), and the most common drivers and challenges in SRM are presented. Finally, a roadmap for the implementation of SRM is proposed. The analysis of the SRM literature reveals that there are important research opportunities in the exploitation of SRM for strategic maintenance and asset management. The authors hope that this document leads to the consolidation of a new research area that aims to add value to maintenance in enterprises through the application of smart retrofitting in preexisting legacy devices.

Suggested Citation

  • David Sanchez-Londono & Giacomo Barbieri & Luca Fumagalli, 2023. "Smart retrofitting in maintenance: a systematic literature review," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 1-19, January.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:1:d:10.1007_s10845-022-02002-2
    DOI: 10.1007/s10845-022-02002-2
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    References listed on IDEAS

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    1. Muchiri, Peter & Pintelon, Liliane & Gelders, Ludo & Martin, Harry, 2011. "Development of maintenance function performance measurement framework and indicators," International Journal of Production Economics, Elsevier, vol. 131(1), pages 295-302, May.
    2. Wo Jae Lee & Kevin Xia & Nancy L. Denton & Bruno Ribeiro & John W. Sutherland, 2021. "Development of a speed invariant deep learning model with application to condition monitoring of rotating machinery," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 393-406, February.
    3. Alessandro Liberati & Douglas G Altman & Jennifer Tetzlaff & Cynthia Mulrow & Peter C Gøtzsche & John P A Ioannidis & Mike Clarke & P J Devereaux & Jos Kleijnen & David Moher, 2009. "The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-28, July.
    4. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
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    Cited by:

    1. Giacomo Barbieri & Jose Daniel Hernandez, 2024. "Sustainability Indices and RAM Analysis for Maintenance Decision Making Considering Environmental Sustainability," Sustainability, MDPI, vol. 16(3), pages 1-23, January.

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