Global Supply Chains with Tariff and Market Uncertainty: Optimization and AI Approaches
Bibliographic record
Abstract
Global supply chains, as critical enablers of global economic integration, facilitate cross-border trade, expand market access, and support worldwide production and consumption. At the same time, they are increasingly exposed to risks such as trade policy shifts, tariff uncertainty, geopolitical conflict, natural disasters, supply disruptions, market volatility, and technological change. In response to evolving trade policies, sustainability pressures, tariff uncertainty, and resilience requirements, firms are restructuring their global production and sourcing strategies. This study presents a review of the literature on global supply chain management under tariff and market uncertainty. We analyze operations research approaches to network design, production planning, inventory management, and sourcing, with particular attention to how uncertainties are modeled and integrated into decision-making frameworks. The review further evaluates the role of emerging technologies, including artificial intelligence and machine learning, blockchain, and digital twins, in enhancing resilience and sustainability. In addition, we introduce an ongoing project on agricultural global supply chains under tariff uncertainty as an illustrative case, demonstrating the practical relevance of robust optimization methods. The findings highlight prevailing methodological trends, identify gaps in addressing multidimensional uncertainties, and outline directions for future research aimed at developing more adaptive and robust global supply chain systems.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".