FRAM-PSO: A semi-quantitative framework integrating multi-dimensional sustainability criteria
Bibliographic record
Abstract
• 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".