A Monte Carlo evolution of the Functional Resonance Analysis Method (FRAM) to assess performance variability in complex systems

• We focus on the importance of Safety-II in safety management of complex systems.
• We present a wide literature review about the FRAM applications in complex systems.
• We evolve the FRAM qualitative structure into a semi-quantitative one.
• We develop a semi-quantitative FRAM based on Monte Carlo simulation.
• A walk through section shows the method’s outcome for an ATM system process.

Modern trends of socio-technical systems analysis suggest the development of an integrated view on technological, human and organizational system components. The Air Traffic Management (ATM) system can be taken as an example of one of the most critical socio-technical system, deserving particular attention in managing operational risks and safety. In the ATM system environment, the traditional techniques of risk and safety assessment may become ineffective as they miss in identifying the interactions and couplings between the various functional aspects of the system itself: going over the technical analysis, it is necessary to consider the influences between human factors and organizational structure both in everyday work and in abnormal situations. One of the newly introduced methods for understanding these relations is the Functional Resonance Analysis Method (FRAM) which aims to define the couplings among functions in a dynamic way. This paper evolves the traditional FRAM, proposing an innovative semi-quantitative framework based on Monte Carlo simulation. Highlighting critical functions and critical links between functions, this contribution aims to facilitate the safety analysis, taking account of the system response to different operating conditions and different risk state. The paper presents a walk-through section with a general application to an ATM process.