Population ageing is acknowledged as a global trend affecting the manufacturing workforce. The progressive age-related decline of human capabilities may lead to an increase in human-system errors (HSEs) in production environments; hence, the study conducted a scoping literature review to determine the relationship between ageing and HSEs in manufacturing contexts. The review identified only 26 relevant studies, which showed that age is associated with HSEs in complex ways. With increasing age, the number, frequency, and probability of HSEs tend to increase, but this trend may be countered by experience effects. The review results suggested that it is necessary to consider the impact of ageing on operators’ performance. A theoretical framework for addressing the relationship between ageing and HSEs in manufacturing contexts was proposed. The framework highlighted the absence of studies reporting on perceptual, cognitive, and/or physical task demands, and the limited application of existing error typologies in the current literature. The results highlighted that important safety, productivity, and quality benefits accrue from paying attention to HSEs in system design and management, which should motivate further research in this area of growing importance.

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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The evolving landscape of industrial manufacturing is increasingly embracing automation within smart factories. However, the critical role of human operators, particularly in manual assembly and disassembly tasks, remains undiminished. This paper explores the complexities arising from mass customization and remanufacturing, which significantly enhance the intricacy of these manual tasks. Human involvement is essential in these tasks due to their complexity, necessitating a structured learning process to enhance efficiency and mitigate the learning–forgetting cycle. This study focuses on the utilization of virtual reality (VR) as an innovative training tool to address these challenges. By conducting a systematic literature review (SLR) on the impact of VR on training operators for assembly and disassembly tasks, this paper evaluates the current level of VR application, the used technologies, the operator performance, and the VR benefits and limitations. The analysis reveals a limited but promising application of VR in training, highlighting its potential to improve learning outcomes, productivity, and safety while reducing costs. However, the research also identifies gaps in the practical application of VR for training purposes suggesting a future research agenda to explore its full potential.

Maintenance contributes to gaining high business performance, guarantees system availability and reliability as well as safe and sustainable operations. Maintenance activity effectiveness depends on competences and the skills of operators whose performance strongly affects maintenance and production operations. The research field of human issues in industrial maintenance was deeply addressed in the literature; however, the current industrial paradigm, which focusses on the integration of new technologies in conventional manufacturing operations to support human performance, sheds light on new challenges for enterprises and opportunities for research in this field. While some literature reviews in the field of human errors and human factors are available, no study investigated the main topics, research trends and challenges related to the role of maintenance operators in manufacturing systems. This paper addresses the current state-of-the-art role of maintenance operators in manufacturing systems, providing an overview of the main studies. A systematic literature review was carried out to identify significant papers. Then, a topic modelling algorithm was used to detect the main topics of the selected papers to provide the research trends of the subject. The identified topics provided interesting research insights on the human role in industrial maintenance. Research trends and further research opportunities were defined.

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.