Improving safety in complex systems: A review of integration of functional resonance analysis method with semi-quantitative and quantitative approaches

Abstract

Functional resonance analysis method (FRAM) is extensively employed in analyzing and managing performance variabilities. Additionally, semi-quantitative and quantitative methods have been increasingly integrated with the FRAM to analyze complex socio-technical systems to improve safety levels. This review article presents a comprehensive and updated survey of current literature focused on semi-quantitative and quantitative methods employed for quantifying performance variabilities and exploring aggregation/propagation rules. A total of 1659 studies published between 2012 and March 2024 from various scientific databases were systematically examined using preferred reporting items for systematic review and meta-analysis, identifying 29 studies that met inclusion criteria. The identified studies were categorized into four groups based on the quantitative methods employed: Monte Carlo simulation, fuzzy logic, cognitive reliability and error analysis method, and miscellaneous approaches. While different methodologies had unique strengths, they commonly relied on expert judgment for data collection, whether for defining probability distributions in Monte Carlo simulations, membership functions, and fuzzy rule bases in fuzzy inference systems, or selecting common performance conditions, determining their interrelationships, and assigning scores. Addressing bias from expert judgment in assessing performance variabilities can be achieved by using suitable experts' opinions integration techniques, and leading safety indicators in the analysis.