Functional Resonance Analysis of Experts’ Monitoring Features in Steel Plate Processing
Naruki Yasue, Tetsuo Sawaragi · IFAC-PapersOnLine · 2022
The advancement of automation technologies in the manufacturing industry has transformed human work into monitoring complex processes and intervening as needed. These operations necessitate the ability to adapt to variabilities that arise during the process. This ability concerns interactions between operators, automated machines, and the environment that constitutes socio-technical systems becoming more complex as technology advances. However, the process of demonstrating adaptive skills is unknown because the operators have acquired those skills as tacit knowledge. In this paper, we investigate the experts’ adaptive skills to cope with multitasking focusing on the monitoring feature using the Functional Resonance Analysis Method (FRAM). First, we formulate a hypothesis based on the attention allocation characteristics during multitasking, using eye-tracking experiments and interviews. Next, we examine the hypothesis by incorporating the attention characteristics into FRAM models and conducting a simulation study that envisions behavior change caused by the encounter of multitasking. Compared with previous studies on approaches to tacit knowledge, this research's novel point is to analyze experts’ features concerning interactions emerging with the environment using the function-based modeling method. The results show that the expert's attention features represented by the FRAM model structure are essential to the adaptive skill to manage variabilities in the working environment. Our research will contribute to elucidating the process of demonstrating adaptive skills in the manufacturing industry.