SCRGD: Supply Chain Resilience in the Face of Global Disruptions
Bibliographic record
Abstract
Supply chain resilience (SCR) has emerged as a critical focus in response to the increasing frequency of global disruptions, including pandemics, geopolitical tensions, and climate change. This study explores the integration of resilience strategies with sustainability performance metrics to address these disruptions and enhance long-term supply chain performance. A conceptual framework is proposed, emphasizing resilience strategies such as flexibility, redundancy, and digital transformation, aligned with sustainability indicators like carbon footprint reduction, resource efficiency, and social responsibility. Case studies from disrupted supply chains, such as during the COVID-19 pandemic, are used to analyze the impact of resilience on sustainability. The findings reveal syner- gies between resilience strategies and sustainability metrics, demonstrating that collaborative and adaptive supply chain practices not only mitigate risks but also contribute to sustainable development goals. However, trade-offs, such as increased environmental costs due to redundancy, are noted, underscoring the need for energy-efficient practices. The study offers actionable insights for practitioners, advocating for digital transformation, multisourcing, and stakeholder collaboration as pathways to enhance resilience and achieve sustainability. Theoretical contributions include bridging the gap between resilience and sustainability while enriching systems theory and the triple bottom line framework. Future research directions are proposed to address gaps in sustainability measurement, focusing on advanced analytics and the social dimensions of supply chain management.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".