Resilience Engineering for Sustainable Prevention in the Manufacturing Sector: A Comparative Study of Two Methods of Risk Analysis
Bibliographic record
Abstract
Manufacturing businesses today have become complex sociotechnical systems. They must manage a variety of risks, including those related to occupational health and safety and emergent ones. In this article, we examine the possibility of using resilience engineering to provide a framework for the management of OHS and emergent risks in manufacturing businesses. More specifically, we compare failure mode effect and criticality analysis (FMECA) to functional resonance analysis method (FRAM), the former being an established risk analysis method in the manufacturing sector and the latter being a relatively novel method used primarily in high-risk sectors such as air transportation, hospitals and railways. These two methods differ in several respects, but comparison reveals their relative advantages as well as their complementarity. Our principal finding confirms the advantages of the functional resonance analysis method in risk management for complex systems. Although failure mode effect and criticality analysis is more effective for understanding technical risks, functional resonance analysis method provides better understanding of the system as a whole and allows more efficient management of organizational risks as well as risks associated with the variability of human performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".