Human Factors and Ergonomics in Manufacturing & Service Industries最新文献

Manual operations remain crucial in critical human-machine interactions due to limitations in automatic control algorithms and technologies. While ergonomic analysis and evaluation techniques for interactive interfaces are advancing, recent approaches emphasize integrating graphical interface experiences with intuitive controls. Conventional methods often lack precision or overlook the interaction effect of different influencing factors, leading to inadequate assessment of essential manual operations for intricate interfaces, such as multifunctional operation panels. To address these challenges, this study aimed to investigate the interaction effects of various factors on the manual operation performance of operators when using a multifunctional operation panel and aims to develop a more comprehensive and broadly applicable evaluation model for such panels. An experiment was designed to consider the type, size, layout of controls, and operational task type as the main factors affecting manual operation performance. Multivariate analysis of variance was used to identify significant interaction effects among the operation factors. The findings underscored the importance of these interactions in evaluating manual operation performance. Multivariate linear regression further examined the influence of these factors, enhancing the evaluation methodology. The study emphasizes the critical role of understanding interaction effects in assessing the manual operation performance of multifunctional operation panels, particularly in improving the design of the panel or operation tasks.

The COVID-19 pandemic challenged health care organizations to cope with major disruptions, especially in the first waves. Several investigations were undertaken to understand how to support resilience during similar unexpected events. In this study, we attempted to unveil the resilient performance of health care organizations during the first waves of the COVID-19 pandemic from the viewpoint of resources for action. Thus, the research objectives are twofold: (i) to evaluate organizational resilience in facing COVID-19 by hospitals in Brazil and (ii) to evaluate the relationship between resources for action and resilient performance. Firstly, an online survey was sent to front-line health care workers, resulting in 111 responses. Then, a questerview was undertaken through online interviews with some participants of the previous phase. Resources for action were interpreted as five aspects supporting decision-making in health care organizations: information and communication; team, equipment, and tools; standard operating procedure (SOP); training; and built environment. Each resource was then unfolded based on the four potentials for resilient performance (i.e., anticipate, monitor, respond and learn). Respondents strongly agreed that their institutions are resilient (M = 4.15; standard deviation [SD] = 0.91). The potentials to learn (M = 4.23; SD = 0.96) and respond (M = 4.08; SD = 1.02) stood out, followed by monitoring (M = 3.85; SD = 1.07) and anticipating (M = 3.70; SD = 1.11). Although some differences stand out, findings corroborate with the joint performance of the resources for action to support resilience performance. Information and communication were the most present among the resources for action (M = 4.20). Making resources for action visible is a strategy for designing resilient systems, as it can be considered a bridge linking different resilience levels (micro, meso, and macro). Suggestions for future studies point out the need to promote the development and evaluation of resources for action in health care institutions.

Incorporating ergonomic considerations into an assembly-line balancing problem (ALBP) enhances productivity and minimizes ergonomic concerns. The assembly process, characterized by repetitive motions and handling numerous components, can lead to worker overload. Consequently, the inclusion of ergonomic aspects results in an appropriate distribution of assembly operations and relative workloads. This study investigates a multi-objective ALBP aimed at minimizing the number of workstations, overall skill level required, and variance in workers' energy expenditure across workstations. To address the ALBP while considering the ergonomic aspects, this study proposes an approach based on the non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective simulated annealing (MOSA) using Pareto optimality. A comparative analysis of the NSGA-II and MOSA is conducted in single- and multiproduct production scenarios, and a computational study involving various factors is performed to identify the dominant algorithm. The computational analysis indicates that the runtime performance of MOSA is 73.287% better than that of NSGA-II; therefore, MOSA outperforms NSGA-II. This study aims at applying scientific knowledge concerning manufacturing ergonomics to assist manufacturing industries in enhancing their productivity.

In this paper, we determined the adjustment levels of the human–machine layout under the preferred driving posture for individuals with different body sizes. We also comparatively analyzed the maximum activation levels of various muscles under straight-line driving and steering conditions. To increase the accuracy of the results, AnyBody biomechanics software was used to establish a human skeletal muscle mechanics model, which we analyzed for consistency with rig test results. The results showed that people with larger body sizes preferred a driving position with the seat reclined back. Steered driving was associated with a significant (p < .05) increase in the maximum activation of the wrist extensors, serratus anterior, deltoid, and triceps brachii, which are the main force-generating muscle groups for steered driving, compared with straight driving. Moreover, the measured and simulated results of maximum muscle activation were relatively consistent, with the error between them within a 15% margin. In summary, this study explored the relationship between different driving conditions and preferred driving postures from a biomechanical perspective. A combined experimental and simulation approach was adopted to ensure the reliability of the findings. The insights from this study can inform ergonomic considerations for the comfort and health of Chinese drivers with varying physical characteristics.

Sweat absorbed by clothing forms a moisture film on fabric surfaces, reducing fabric breathability and disrupting thermoregulation during exercise in a hot environment. We investigated whether T-shirts made from a newly developed fabric with hydrophobic and water-repellent fibers near the through holes could prevent sweat film formation, thereby enhancing intraclothing microclimate and thermoregulatory responses. Thirteen male runners completed 30-min treadmill sessions at moderate and high intensities while wearing either the new fabric T-shirt (Dry Aeroflow, DAF) or a polyester T-shirt (CONT) at an ambient temperature of 32°C and a relative humidity of 50% with an air velocity of 0.8 m/s. Compared with CONT, intraclothing humidity was decreased on the chest and back, and chest but not back skin temperature was decreased by 2°C in DAF. However, thermoregulatory key variables such as rectal temperature, mean skin temperature, sweat rates, and heart rate did not differ between the T-shirts. We concluded that the a T-shirt with enhanced breathability does not affect overall thermoregulatory response during exercise in the heat despite the partial improvements in intraclothing microclimate and in reducing local skin temperature.

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.

This paper investigates the usage of a systemic neutralized taxonomy for understanding human factors to foster a restorative culture when studying adverse events. The available studies supporting accident analysis from a system-theoretic perspective with human factors-based methods are grounded on error taxonomies and do not reinforce the non-judgemental dimension that is encouraged by modern safety science. We propose a methodology to integrate the system-theoretic accident analysis technique, Causal Analysis based on System Theory, with a neutralized taxonomy of human explanatory factors. The proposed methodology has been applied to an aviation accident involving a military aircraft and a glider. This case revealed various critical interactions among system components, which require dedicated safety recommendations that go beyond the identification of single points of failures and root causes, leading to a deeper understanding of socio-technical orchestrations. Despite the use case in aviation, the proposed methodology remains suitable for various types of safety occurrences in diverse domains and industrial settings. It also represents the basis for supporting future proactive safety-related decision-making processes.

More than half of the population in impoverished areas around the world, which lack sufficient energy and water, do not have washing machines. Due to a lack of resources and economic reasons, the mechanical task of modern washing machines requiring electrical energy can only be done through hand washing. Additionally, water fetching and laundry work are predominately assigned to women due to socio-cultural dynamics in disadvantaged areas. The repetitive motions involved in washing over extended periods of time, coupled with awkward body positions, often lead to musculoskeletal disorders in women. This study aims to improve women's work safety and living conditions through a product design for laundry washing. First, the ergonomic risk associated with women's body postures while washing clothes on the floor was evaluated to better understand the impact of the intervention and rapid upper extremity assessment (RULA) method was used for this purpose. Subsequently, the design process for a manual washing product, which operates without electricity, can handle bulk laundry and facilitates easier and more ergonomic work instead of directly scrubbing clothes, was initiated as a remedial intervention. Recognizing that laundry work is a multidimensional experience in women's lives beyond its ergonomic aspects, a holistic design methodology was adopted, incorporating various design approaches to create concepts that better integrate with the lives of woman users. Insights gained from these design approaches were then combined with the conceptual design method to generate design proposals. At the end of the study, the developed manual washing machine design performed improved RULA analysis scores and biomechanical loading outcomes compared to hand washing.

Workers' health and safety, and equality between men and women are two of the main challenges currently facing the labor framework in Spain. Despite the fact that the different risks faced by men and women have been widely studied, the way in which gender can affect the working conditions and career development of occupational health and safety (OHS) technicians has hardly been investigated. To delve into this aspect, a study has been carried out by conducting semistructured interviews with six women who work as OHS technicians and a survey among OHS technicians in the Basque Country (Spain) with 124 responses. The results suggest that there are differences between men and women both in the conditions and in the career development of the OHS technician profession. These differences appear in various thematic blocks such as empowerment, recognition, or aspirations. Women tend to be more skeptical than men on gender equality issues such as the idea of equal promotion opportunities for men and women, or the notion that men's and women's ideas are equally valued, or the way in which having children affects men and women's aspirations. It is very important to reduce these gender differences to achieve a more efficient work environment, which can lead to more efficient OHS management.

Visual inspection tasks often require humans to cooperate with artificial intelligence (AI)-based image classifiers. To enhance this cooperation, explainable artificial intelligence (XAI) can highlight those image areas that have contributed to an AI decision. However, the literature on visual cueing suggests that such XAI support might come with costs of its own. To better understand how the benefits and cost of XAI depend on the accuracy of AI classifications and XAI highlights, we conducted two experiments that simulated visual quality control in a chocolate factory. Participants had to decide whether chocolate molds contained faulty bars or not, and were always informed whether the AI had classified the mold as faulty or not. In half of the experiment, they saw additional XAI highlights that justified this classification. While XAI speeded up performance, its effects on error rates were highly dependent on (X)AI accuracy. XAI benefits were observed when the system correctly detected and highlighted the fault, but XAI costs were evident for misplaced highlights that marked an intact area while the actual fault was located elsewhere. Eye movement analyses indicated that participants spent less time searching the rest of the mold and thus looked at the fault less often. However, we also observed large interindividual differences. Taken together, the results suggest that despite its potentials, XAI can discourage people from investing effort into their own information analysis.