Sustainable and resilient cold chains: Enhancing adaptability, consistency, and digital transformation for success in a turbulent market
Bibliographic record
Abstract
Abstract Sustainable and resilient cold chains (CC) are the backbone of several industries, ensuring the seamless transport and storage of temperature‐sensitive products ranging from fresh produce and pharmaceuticals to life‐saving vaccines. Particularly in times of disruptions, such as natural disasters or global health crises, the sustainability and resilience of CCs become even more crucial, highlighting the urgent need for research, innovation, and strategic planning in this domain. This study conducted a systematic review of the sustainable and resilient CC research literature. A total of 143 high‐quality articles were shortlisted from the Web‐of‐Science (WoS) database for in‐depth content analysis. This review clarifies the existing results and highlights the trending themes and persistent gaps in research pertaining to CC in a business context and at a strategic level. It covers notably disruptions and related impacts, the relationship between sustainability and resilience in CC, decision tools for sustainable and resilient CC, CC sustainability and resilience performance areas and measurement metrics, data‐driven digital transformation for sustainable and resilient CC, and strategies for developing sustainable and resilient CC strategies. The results reveal that research on sustainable and resilient CC is growing; however, the field remains dominated by research methods that need to establish causality formally. Furthermore, the research area is fragmented into several subareas, and more conceptual and theoretical work is needed to advance the theoretical foundation of the domain. Finally, a proposed research agenda suggests 17 future avenues for improving the contribution to sustainable and resilient CC research.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".