FRAM-PSO: A semi-quantitative framework integrating multi-dimensional sustainability criteria

• Introduces a novel framework integrating Functional Resonance Analysis Method (FRAM) with Particle Swarm Optimization (PSO) to quantitatively assess systemic risk and optimize the selection of risk mitigation strategies. • Provides a quantitative, systemic, and mitigation-oriented approach for evaluating risks specifically associated with smart wearable technologies in manufacturing settings. • Explicitly incorporates sustainability criteria (environmental, economic, social) into the optimized risk mitigation process, aligning with Industry 5.0 principles for a more holistic system improvement. • Demonstrates the practical application and effectiveness of the FRAM-PSO methodology through case studies, achieving significant reductions (e.g., more than 22% of reduction in three cases) in overall system risk. The increasing complexity of modern industrial systems, particularly those integrating smart wearables, makes it harder for traditional risk analysis methods to keep up. Systemic approaches such as the Functional Resonance Analysis Method (FRAM) help to understand how systems behave; however, there is an opportunity to develop more reliable quantification methods and integrate sustainability criteria, which current methods often do not emphasize. To address these gaps, this paper introduces a novel semi-quantitative framework that integrates FRAM with the Particle Swarm Optimization (PSO). This hybrid approach provides a structured methodology to systematically identify system functions, quantify performance variability, and model risk propagation. A key contribution is the explicit integration of multi-dimensional sustainability criteria (environmental, economic, and social) into the risk management process. This allows for the selection of optimized mitigation strategies. Three case studies involving smart wearables in assembly and disassembly systems were used to demonstrate the effectiveness of the proposed methodology. The results showcase the model’s ability to identify high-risk pathways and prioritize mitigation efforts. This confirms its potential as a decision-support tool. This study contributes a novel methodological structure for embedding sustainability and optimization into systemic risk management.