FRAM to assess performance variability in everyday work: functional resonance in the railway domain

This paper shows the possibility of enhancing the traditional FRAM structure by a semi-quantitative framework in order to increase FRAM’s applicability for the analysis of complex systems. This innovative framework consists of defining numeric scores for variability, quantifying in a particular scenario the effects of interactions among functions. Rather than static and deterministic values, it assigns probability distribution functions to the scores, combining them by the aid of Monte Carlo simulation. The distributions, based on Subject Matter Experts’ judgments and historic data, if available, allow obtaining an estimation of performance variability and its subsequent functional resonant effects. This semi-quantitative framework allows isolating the critical functions and the critical links among functions, considering non-linear and transient interdependencies. This paper explores the possibility of combining the Monte Carlo framework with an Abstraction/Agency framework recently introduced in literature, in order to make more evident and readable the model itself, maintaining a systemic functional perspective. Once addressed the criticalities and related them to different abstraction levels, it would be possible to plan for mitigating actions. The illustrative case study takes advantage of SMEs and several accident reports in the railway domain to illustrate the application of the proposed semi-quantitative multi-layer framework.