Supply Chain Resilience Metrics: Optimization under Multi-Risk Shocks
Bibliographic record
Abstract
Background: Modern supply chains are exposed to multi-risk shocks that interact across demand, supply, logistics, finance, and cyber domains. These interactions challenge resilience scorecards that rely on isolated indicators.Methods: This review synthesizes definitions of supply chain resilience, operational resilience metrics, and robust and risk-averse optimization frameworks. We develop a measurement-to-decision pipeline that links stress scenarios to policy choices.Results: We identify a compact and operationally meaningful metric set—time-to-survive (TTS), time-to-recover (TTR), service continuity, and cost-to-serve—and show how these metrics can be embedded in robust, minimax, and CVaR-based optimization formulations. We describe how structured multi-risk stress scenarios translate metrics into decisions on inventory buffers, backup capacity, sourcing diversification, and logistics routing.Conclusions: Conclusions: Resilience metrics become operationally useful when tied to explicit decision levers and validated through structured stress tests, not treated as stand-alone scorecards.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".