Supply Chain Resilience Roadmaps for Major Disruptions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Unexpected events or major supply chain disruptions have demonstrated the vulnerability in which supply chains operate. While supply chains are usually prepared for operational disruptions, unexpected or black swan events are widely disregarded, as there is no reliable way to forecast them. However, this kind of event could rapidly and seriously deteriorate supply chain performance, and ignoring that possibility could lead to devastating consequences. Methods: In this paper, definitions of major disruptions and the methods to cope with them are studied. Additionally, a methodology to develop supply chain resilience roadmaps is conceptualised by analysing existing literature to help plan for unexpected events. Results: The methodology is introduced to create roadmaps comprises several stages, including supply chain exploration, scenario planning, system analysis, definition of strategies, and signal monitoring. Each roadmap contains the description of a plausible future in terms of supply chain disruptions and the strategies to implement to help mitigate negative impacts. Conclusions: The creation of roadmaps calls for an anticipatory mindset from all members along the supply chain. The roadmaps development establishes the foundations for a holistic supply chain disruption preparation and analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it