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This study investigates visual fatigue in a real-world pharmaceutical packaging environment, where operators perform repetitive inspection and packing tasks under frequently suboptimal lighting conditions. A human-centered methodology was adopted, combining adapted self-report questionnaires, high-frequency eye-tracking data collected with Tobii Pro Glasses 3, and lux-level measurements. Key eye-movement metrics—including fixation duration, visit patterns, and pupil diameter—were analyzed within defined work zones (Areas of Interest). To reduce data complexity and uncover latent patterns of visual behavior, Principal Component Analysis was applied. Results revealed a progressive increase in visual fatigue across the workweek and throughout shifts, particularly during night work, and showed a strong association with inadequate lighting. Tasks involving high physical workload under poor illumination emerged as critical risk scenarios. This integrated approach not only confirmed the presence of visual fatigue but also identified high-risk conditions in the workflow, enabling targeted ergonomic interventions. The findings provide a practical framework for improving operator well-being and inspection performance through sensor-based monitoring and environment-specific design enhancements, in alignment with the goals of Industry 5.0.

Human factors play a relevant role in the dynamic work environments of the manufacturing sector in terms of production efficiency, safety, and sustainable performance. This is particularly relevant in assembly lines where humans are widely employed alongside automated and robotic agents. In this situation, operators’ ability to adapt to different levels of task complexity and variability in each workstation has a strong impact on the safety, reliability, and efficiency of the overall production process. This paper presents an application of a theoretical and empirical method used to assess the matching of different workers to various workstations based on a quantified comparison between the workload associated with the tasks and the human capability of the workers that can rotate among them. The approach allowed for the development of an algorithm designed to operationalise indicators for workload and task complexity requirements, considering the skills and capabilities of individual operators. This led to the creation of human performance (HP) indices. The HP indices were utilized to ensure a good match between requirements and capabilities, aiming to minimise the probability of human error and injuries. The developed and customised model demonstrated encouraging results in the specific case studies where it was applied but also offers a generalizable approach that can extend to other contexts and situations where job rotations can benefit from effectively matching operators to suitable task requirements.