Enhancing Aviation Safety Analysis through FRAM-FDM: A Data-Driven Approach to Performance Variability

Understanding how performance variability emerges within complex socio-technical aviation operations is critical for strengthening proactive safety management. While the Functional Resonance Analysis Method (FRAM) provides a structured framework for modelling such variability, its traditional use relies largely on qualitative judgment. This paper introduces a quantitative extension, FRAM-FDM, which integrates Flight Data Monitoring techniques to measure functional variability directly from operational flight data. By replacing subjective characterizations in FRAM's variability and aggregation steps with empirically derived metrics and regression-based modelling of functional couplings, the approach enhances analytical precision. The method is demonstrated through a case study focused on the landing-run phase of a jet aircraft fleet, using data from 110 landings. Results show that the FRAM-FDM framework augments traditional FRAM analysis by providing objective, data-driven insights, thereby supporting more robust pilot training, procedure refinement, and proactive safety strategies.

Keywords: Aviation, FRAM, Flight Data Monitoring.