Utilizing the FMEA RPN Framework in Quantifying Supply Chain Risks of High Severity and Low Probability Events: Pandemics and Geopolitical Conflicts - An In-depth Analysis
Bibliographic record
Abstract
Supply chains are facing disruptions in succession, and recovery remains a challenge. Disruptions challenge supply chain managers to find solutions for a faster recovery. However, building supply chain resiliency may lead to foregoing some globalization cost-benefits. While professionals and academicians research this conundrum, it's evident that reactive approaches do not support sustenance and present a unique challenge with each disruption. Therefore, it becomes significant to predict risk probabilities and severity and act to mitigate the risks strategically. It also calls for timely decision-making. This paper identifies the need for a proactive approach to predicting risks and detecting trigger points for well-timed decision-making. The paper recommends the existing frameworks of Failure Mode and Effects Analysis (FMEA) Risk Priority Number (RPN), Uppsala model, and Multicriteria Decision Making (MDM) for quantifying and reducing the risk and improving resiliency. The FMEA model helps assess and prioritize risks, while the Uppsala model guides commitment based on changes in risk. MDM acknowledges that other criteria may also be important in strategic decision-making beyond just risk.
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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 | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".