Extracting knowledge of workers by identifying performance variability using functional resonance analysis method
提出一种基于功能共振分析的方法，通过比较理想工作与实际工作识别工人绩效变异性，系统提取工人应对干扰的适应性知识，案例验证于半导体蚀刻工艺。
In the safety management of complex socio-technical systems, an approach based on Safety-II has gained attention, focusing on how systems maintain everyday operations in the face of various disturbances and unexpected situations. Performance variability, which refers to the workers’ adjustment activity in response to these external factors, is essential for analyzing system safety and the workers’ knowledge about adaptive behaviors. This study proposes a practical method to identify workers’ performance variability by comparing Work-as-Imagined (WAI) with Work-as-Done (WAD) and systematically extracting workers’ knowledge for managing specific disturbances. This method builds upon the Functional Resonance Analysis Method (FRAM) and associated semi-quantitative simulation techniques developed within the Safety-II paradigm. The approach involves constructing separate FRAM models for WAI and WAD, followed by comprehensive simulations based on a Qualitative Comparative Analysis (QCA) process where functions within the WAD model are iteratively modified. This process identifies the critical functions essential for the operator’s adaptive knowledge, providing a systematic framework to capture and analyze workers’ resilient performances. The practical application of the proposed approach was demonstrated through a case study of the etching work in a compound semiconductor manufacturing process. The results showed that combining the specific functions in the WAD model contributes to operational resilience, reflecting workers’ practical knowledge emerging from interactions between multiple functions. The proposed approach contributes to revising work procedures, improving worker education, and enhancing system safety management.