An HRA-based simulation model for the optimization of the rest breaks configurations in human-intensive working activities

Pasquale, Valentina Di; Miranda, Salvatore; Iannone, Raffaele; Riemma, Stefano

doi:10.1016/j.ifacol.2015.06.103

Cited by 17 publications

(5 citation statements)

References 15 publications

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“…Then, where possible, the PSF labels were classified according to the eight PSF categories of the SHERPA model described in Table 1 (Di Pasquale, Miranda, Iannone & Riemma, 2015a, 2015b. The final classification was agreed upon by all the authors in different meeting sessions.…”

Section: Stressmentioning

confidence: 99%

A taxonomy of performance shaping factors for human reliability analysis in industrial maintenance

Franciosi

Pasquale

Iannone

et al. 2019

JIEM

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Purpose: 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.

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Section: Stressmentioning

confidence: 99%

A taxonomy of performance shaping factors for human reliability analysis in industrial maintenance

Franciosi

Pasquale

Iannone

et al. 2019

JIEM

Self Cite

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“…adopted by numerous studies (Setayesh et al, 2022;Di Pasquale et al, 2015a, 2015b. Additionally, some research has expanded on the standard PSF categories to include various subcategories or more nuanced, open-ended PSFs (Aju Kumar et al, 2015;Besco, 2004;Velmurugan et al, 2022).…”

Section: The State Of the Art In Psf Taxonomy (Rq1)mentioning

confidence: 99%

Taxonomy of performance shaping factors in manufacturing: A systematic literature review

Jha,

Di Pasquale,

Saleem

et al. 2024

Hum Ftrs & Erg Mfg Svc

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Human error in manufacturing can have substantial consequences, including loss of life, injuries, productivity, and financial losses. Human reliability analysis (HRA) methods can be used to evaluate the likelihood of human error in manufacturing tasks and identify potential sources of error. Performance shaping factors (PSFs) are internal and external factors that influence human performance and can affect the likelihood of human reliability estimates in HRA methods. Understanding the impact of PSFs on human performance in manufacturing is essential for developing effective strategies to minimize the likelihood of human error and improve the safety and efficiency of manufacturing processes. This systematic review scrutinizes the literature on PSFs within manufacturing, highlighting HRA applications. Using the PRISMA protocol, studies from 2000 to 2024 across engineering and psychology were examined, culminating in the analysis of 35 pertinent works. The review identifies and contrasts various PSF taxonomies from established HRA methods like SPAR‐H, HEART, CREAM, and THERP, revealing their diverse applications in different manufacturing settings. The review also uncovers a tendency to devise taxonomies through the lens of experts' domain knowledge, particularly tailored to discrete manufacturing contexts. A critical gap is observed in the lack of a uniform PSF framework, with the current literature reflecting a disparate understanding of PSFs' roles, definitions, and interrelations. This absence is further pronounced by the inadequate integration of human factors in the dialogue surrounding Industry 4.0. The analysis points to the necessity of harmonizing PSFs to better assess human reliability amid technological integration. The findings emphasize the need for an industry‐specific PSF framework that aligns with the intricacies of manufacturing operations, thus enabling more accurate HRA outcomes and informing strategies for error reduction and process optimization.

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“…Caputo et al [26] defined an HRA model that, besides mapping error types and analyzing the logic conditions in which the errors occurred, was also able to identify the errors causing the highest economic impact and to provide a quantitative assessment of the economic impact of the errors. Di Pasquale et al used an HRA method to simulate the optimal breaks during a work shift in a manual assembly line [27]. Concerning the causes of errors, Torres et al highlighted that, during the execution of manual assembly tasks, some operations are the most likely to cause errors because the level of difficulty is very high [28].…”

Section: Literature Reviewmentioning

confidence: 99%

A Human Error Analysis in Human–Robot Interaction Contexts: Evidence from an Empirical Study

et al. 2023

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More than 60 years has passed since the installation of the first robot in an industrial context. Since then, industrial robotics has seen great advancements and, today, robots can collaborate with humans in executing a wide range of working activities. Nevertheless, the impact of robots on human operators has not been deeply investigated. To address this problem, we conducted an empirical study to measure the errors performed by two groups of people performing a working task through a virtual reality (VR) device. A sample of 78 engineering students participated in the experiments. The first group worked with a robot, sharing the same workplace, while the second group worked without the presence of a robot. The number of errors made by the participants was collected and analyzed. Although statistical results show that there are no significant differences between the two groups, qualitative analysis proves that the presence of the robot led to people paying more attention during the execution of the task, but to have a worse learning experience.

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An HRA-based simulation model for the optimization of the rest breaks configurations in human-intensive working activities

Cited by 17 publications

References 15 publications

A taxonomy of performance shaping factors for human reliability analysis in industrial maintenance

A taxonomy of performance shaping factors for human reliability analysis in industrial maintenance

Taxonomy of performance shaping factors in manufacturing: A systematic literature review

A Human Error Analysis in Human–Robot Interaction Contexts: Evidence from an Empirical Study

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