Show simple item record

dc.contributor.authorFranciosi, Chiara
dc.contributor.authorDi Pasquale, Valentina
dc.contributor.authorLannone, Raffaele
dc.contributor.authorMiranda, Salvatore
dc.identifier.citationFranciosi, C. [et al.]. A taxonomy of performance shaping factors for human reliability analysis in industrial maintenance. "Journal of Industrial Engineering and Management", Abril 2019, vol. 12, núm. 1, p. 115-132.
dc.description.abstractPurpose: Human factors play an inevitable role in maintenance activities, and the occurrence of Human Errors (HEs) affects system reliability and safety, equipment performance and economic results. The high HE rate increased researchers’ attention towards Human Reliability Analysis (HRA) and HE assessment approaches. In these approaches, various environmental and individual factors influence the performance of maintenance operators affecting Human Error Probability (HEP) with a consequent variability in the success of intervention. However, a deep analysis of such factors in the maintenance field, often called Performance Shaping Factors (PSFs), is still missing. This has led the authors to systematically evaluate the literature on Human Error in Maintenance (HEM) and on the PSFs, in order to provide a shared PSF taxonomy. Design/methodology/approach: A Systematic Literature Review (SLR) was conducted to identify and select peer-reviewed papers that provided evidence on the relationship between maintenance activities and human performance. The obtained results provided a wide overview in the field of interest, shedding light on three main research areas of investigation: methodologies for human error analysis in maintenance, performance shaping factors and maintenance error consequences. In particular, papers belonging to the area of PSFs were analysed in-depth in order to identify and classify the PSFs, with the aim of achieving the PSF taxonomy for maintenance activities. The effects of each PSF on human reliability were defined and detailed. Findings: A total of 63 studies were selected and then analysed through a systematic methodology. 46% of these studies presented a qualitative/quantitative assessment of PSFs through application in different maintenance activities. Starting from the findings of the aforementioned papers, a PSF taxonomy specific for maintenance activities was proposed. This taxonomy represents an important contribution for researchers and practitioners towards the improvement of HRA methods and their applications in industrial maintenance. Originality/value: The analysis outlines the relevance of considering HEM because different error types occur during the maintenance process with non-negligible effects on the system. Despite a growing interest in HE assessment in maintenance, a deep analysis of PSFs in this field and a shared PSF taxonomy are missing. This paper fills the gap in the literature with the creation of a PSF taxonomy in industrial maintenance. The proposed taxonomy is a valuable contribution for growing the awareness of researchers and practitioners about factors influencing maintainers’ performance.
dc.format.extent18 p.
dc.rightsCreative Commons Attribution-NonCommercial 4.0 International License
dc.subjectÀrees temàtiques de la UPC::Economia i organització d'empreses::Gestió de la qualitat
dc.subject.lcshReliability (Engineering)
dc.subject.lcshMaintainability (Engineering)
dc.subject.otherHuman error
dc.subject.otherHuman reliability analysis
dc.subject.otherPerformance shaping factors
dc.subject.otherInfluencing factors
dc.titleA taxonomy of performance shaping factors for human reliability analysis in industrial maintenance
dc.subject.lemacFiabilitat (Enginyeria)
dc.subject.lemacEmpreses -- Factor humà
dc.subject.lemacManteniment (Enginyeria)
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
upcommons.citation.publicationNameJournal of Industrial Engineering and Management

Files in this item


This item appears in the following Collection(s)

Show simple item record

Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution-NonCommercial 4.0 Generic