Functional resonance analysis method and monte carlo simulation: evaluating performance variability in a socio-technical direct reduction iron plant

Direct Reduction Iron (DRI) units are complex socio-technical systems where traditional failure-centric risk models often fail to capture emergent behaviors. This study aims to develop an integrated resilience-based framework to quantify performance variability and identify critical systemic vulnerabilities in DRI operations by combining the Functional Resonance Analysis Method (FRAM), the Fuzzy Analytic Hierarchy Process (FAHP), and Monte Carlo Simulation (MCS). The proposed framework integrates FRAM-based functional modeling with FAHP-based expert judgment weighting and MCS-based uncertainty analysis. The simulation was implemented in R, with 10,000 iterations, to quantify the propagation of variability across critical functional interactions under different operational scenarios. The results identified increased workload conditions, production shutdown and system restart, and operational delay or coordination failure as major contributors to systemic instability. Among the identified interactions, human resource evaluation and the influence of training on inspection processes were found to be critical pathways affecting system performance variability. The findings demonstrate that improving safety in DRI units requires proactive management of functional variability rather than focusing solely on preventing isolated component failures. This framework provides decision-makers with a quantitative approach to enhance operational resilience and support safety management in complex industrial systems.