A multi-disruption risk analysis system for sustainable supply chain resilience
Bibliographic record
Abstract
As global Supply Chains (SCs) face increasing complexities and risks, organizations must balance operational efficiency with preparedness for unforeseen disruptions. Recent events, including the devastating floods or wildfires and the impacts of the volatility of international relations , have underscored the vulnerability of SCs. The study explores the critical need for combining supply chain management , risk management, and sustainability for the systematic analysis of disruption risks from the unpredictability of natural disasters, man-made events, and rapid technological advancements. We develop a decision support system integrating the fuzzy C-means clustering and integrated multi-criteria decision-making approach for risk categorization and prioritization, respectively. The developed framework considers the significance of multiple risk factors (e.g., urgency and vulnerability) in the risk disruption analysis process through the hybrid Bayesian best-worst method-combined compromise solution approach. This enables managers to identify critical disruption risks (e.g., communication network disruptions, production facility-related risk, and increased demand for certain goods) while observing their adverse effects on the resiliency of sustainable SCs. Compared to the traditional risk priority number , the proposed system includes the importance of risk factors and provides a more stable and separable ranking, empowering managers to deal with potential resource limitations. This study also suggests risk mitigation strategies to alleviate the negative consequences of disruptions within organizational constraints and improve sustainable SC responsiveness to future disasters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".