Risk mitigation in the implementation of AMTs: A guiding framework for future
Bibliographic record
Abstract
The fast industrial development increases different types of risks for the industries.Many risk factors are inherent in the implementation of advanced manufacturing technologies (AMTs).Industries are developing methodologies for risk prevention and protection.The present research focuses to identify various risks that could influence the implementation of AMTs, and develop a framework to mitigate them.For this framework, interpretive structural modeling(ISM) has been used to depict the relationship and priority among the various risks.This research provides a path for managers and indicates the dominant risks on the basis of higher driving power.Also, this research classifies the relationship among various risks in AMTs implementation according to their driving power and dependence.The risks have been categorized into four categories as autonomous risks, linkage risks, dependent risks and independent risks.The proposed hierarchal model would help the management to effectively handle and develop strategies against the risks and hence new and latest technologies can be adopted with ease and effectiveness.
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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 | 0.024 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".