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Identification of Criteria for Enabling the Adoption of Sustainable Maintenance Practice: An Umbrella Review

Author

Listed:
  • Stana Vasić

    (University of Novi Sad, Faculty of Technical Sciences, Department of Industrial Engineering and Engineering Management, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia)

  • Marko Orošnjak

    (University of Novi Sad, Faculty of Technical Sciences, Department of Industrial Engineering and Engineering Management, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia)

  • Nebojša Brkljač

    (University of Novi Sad, Faculty of Technical Sciences, Department of Industrial Engineering and Engineering Management, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia)

  • Vijoleta Vrhovac

    (University of Novi Sad, Faculty of Technical Sciences, Department of Industrial Engineering and Engineering Management, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia)

  • Kristina Ristić

    (University of Novi Sad, Faculty of Technical Sciences, Department of Industrial Engineering and Engineering Management, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia)

Abstract

The evolution from traditional industrial maintenance to sustainable maintenance (SM) is pivotal within an existing industrial ecosystem. This study, utilising an umbrella review (UR), critically examines this transition, highlighting its increased importance in maintenance decision-making (MDM). Using a sample (n = 20) of reviews, we synthesised meta-, methodological-, and content-based evidence and performed bibliometric, thematic and statistical analyses. For the bibliometric and thematic/conceptual analyses, we used the R bibliometrix package. The results show that the early research focuses mainly on theoretical aspects, while recent studies examine the practical implications. Also, comprehensive studies evaluating the benefits of implementing environmental and social aspects within MDM are still lacking. For that reason, we switched the attention to content-based data, from which we identified 43 distinct criteria discussed. For the analysis of criteria, the Bayesian Network Analysis with Gaussian Copula Graphical Model (BNA-GCGM) method was used. Although the evidence shows that environmental pollution, energy consumption and health and safety of workers are the most discussed criteria, the BNA-GCGM suggests that labour costs, resource consumption, employee satisfaction and energy consumption, among others, are the most influential criteria in the network analysis. Interestingly, after distinguishing studies into pre- and post-2021 research, the results show that pre-2021 research is primarily focused on economic and technical factors, reflecting a profit-oriented approach. The post-2021 analysis suggests a discernible shift towards more balanced considerations by incorporating social and environmental factors, suggesting a more socially responsible approach. Finally, while SM is gaining momentum, further empirical and practical research are required to demonstrate the advantages that SM offers in the light of the upcoming Industry 5.0.

Suggested Citation

  • Stana Vasić & Marko Orošnjak & Nebojša Brkljač & Vijoleta Vrhovac & Kristina Ristić, 2024. "Identification of Criteria for Enabling the Adoption of Sustainable Maintenance Practice: An Umbrella Review," Sustainability, MDPI, vol. 16(2), pages 1-35, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:767-:d:1320058
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    References listed on IDEAS

    as
    1. Marko Orošnjak & Dragoljub Šević, 2023. "Benchmarking Maintenance Practices for Allocating Features Affecting Hydraulic System Maintenance: A West-Balkan Perspective," Mathematics, MDPI, vol. 11(18), pages 1-30, September.
    2. Vrignat, Pascal & Kratz, Frédéric & Avila, Manuel, 2022. "Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    3. Lê, Sébastien & Josse, Julie & Husson, François, 2008. "FactoMineR: An R Package for Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i01).
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