Analyzing Global Supply Chains in the Times of Disruption: A Literature Review on Structural Dynamics and Challenges
Bibliographic record
Abstract
The global supply chains for different products, due to the increasing complexity, have nowadays exposed their vulnerability to countless disruptions, including natural disasters, pandemics, geopolitical tensions and trade wars. This review paper critically studies 17 scholarly publications focusing on global supply chains, especially around key products such as seafood, lithium, steel, energy, pharmaceuticals, agrifood, and others. The review identifies the major parties involved in the supply chains and significant business impacts, outlines obstacles and challenges, and explores the role of geopolitical tensions. The review finds geopolitical tensions have become the dominant challenge, along with technological and market forces which may restructure global logistics, trade dependencies, and risk strategies. It also finds some strategic initiatives taken by the practical world for these issues. It aims for a detailed exploration on recent geopolitical tensions as one of the most affecting supply chain disruptions under one umbrella rather than what is stated in the previous studies. Finally, some recommendations have been provided to cope with this disruption such as sourcing strategies, resiliency, visibility and digitization.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".