FRAM and STAMP new avenue for risk analysis of manufacturing in the context of industry 4.0
Bibliographic record
Abstract
Manufacturing in the context of industry 4.0 has become increas-ingly complex. As the complexity of these systems increases, so to will the potential for emerging hazards. Consequently, finding an appropriate ap-proach to identify, measure and assess new and emerging hazards is im-perative to prevent their occurrence or lessen their effects on the human, organizational, and technical scales. FRAM and STAMP applications are two known methods for analyzing risks in complex systems. This study con-ducts a critical review of the literature on FRAM and STAMP specifically with regards to their application in the manufacturing sector. For this purpose, scientific databases such as IEEE, Compendex and INSPEC, Science Di-rect, Google Scholar, and Espace ÉTS were consulted for relevant studies from 2004 to 2020, mostly in English. The search keywords included FRAM, STAMP, STPA, manufacturing, risk, industry, and industry 4.0. The results are presented in two tables including the year of publication, the study's aim and results, the type of analyzed risks, and applied methods. Despite the limited number of studies that have applied FRAM or STAMP in manufac-turing, the results show that they are suitable for understanding, explaining, and analyzing complex manufacturing systems. They can offer a different perspective on the analysis of the system. However, to the best of our knowledge, their application in manufacturing in the context of industry 4.0, particularly with regards to the use of wearable technologies in manufactur-ing, has not yet been studied. The results of this review conclude that the use of FRAM and STAMP in manufacturing could be promising for the anal-ysis of digital manufacturing risks, especially wearable technologies used in manufacturing, a point which needs further consideration in future studies.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".