Risk Mapping: Ranking and Analysis of Selected, Key Risk in Supply Chains
Bibliographic record
Abstract
This study aimed to analyze the impact of key causes of external and internal risk on supply chains. The basic and most probable causes of the risk are listed, based on literature research and interviews with representatives of the metal industry. The analysis was carried out by semiquantitative assessment using risk maps. The relationship between the probability of an event occurrence and its impact on supply chains was tested. The study postulates that key risk factors can be controlled through risk monitoring. Attention was drawn to the beneficial aspects of using risk maps that enable a comprehensive assessment of the situation. Both external and internal risks can cause turmoil and disruption of the supply chain. The findings suggest that external uncertainty and crises have the most direct impact on supply chain risk and are the most dangerous. The work presents the possibility of practical application of risk maps for risk assessment and monitoring. The presented approach to risk assessment complements the methodology of risk assessment and monitoring. Risk maps were used as a basic tool in assessing the impact of individual risks on supply chains. It has been found that supply chains are subject to high risk, which can be monitored through risk matrix procedures. The conducted analysis showed that critical risk areas in supply chains are external crises, environmental uncertainty, supply chain relationships, and manufacturing and the most dangerous risks in supply chains are related to external conditions beyond the control of the participants in the supply chain. The article fills a gap in research on risk monitoring in supply chains by focusing on selected, generalized measures related to industrial supply.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| 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".