Optimizing Human Performance to Enhance Safety: A Case Study in an Automotive Plant

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

Neuroergonomics method for measuring the influence of mental workload modulation on cognitive state of manual assembly worker

In this study, we simulated a manual assembly operation, where participants were exposed to two distinct ways of information presentation, reflecting two task conditions (monotonous and more demanding task condition). We investigated how changes in mental workload (MWL) modulate the P300 component of event-related potentials (ERPs), recorded from wireless electroencephalography (EEG), reaction times (RTs) and quantity of task unrelated movements (retrieved from Kinect). We found a decrease in P300 amplitude and an increase in the quantity of the task unrelated movements, both indicating a decrease in attention level during a monotonous task (lower MWL). During the more demanding task, where a slightly higher MWL was imposed, these trends were not obvious. RTs did not show any dependency on the level of workload applied. These results suggest that a wireless EEG, but also Kinect, can be used to measure the influence of MWL variation on the cognitive state of the workers