Geopolitical disruptions in global supply chains: a state-of-the-art literature review
Bibliographic record
Abstract
This paper systematically reviews the literature on the impact of geopolitical disruptions on supply chains to identify primary discourses, emergent themes and key gaps to set a future research agenda. The guiding research question is ‘how do geopolitical disruptions affect the configuration, flow, and management of global supply chains?’. The study applies a systematic literature review of 50 papers from the Association of Business Schools’ (ABS) ranked academic journals in the fields of operations, production, and supply chain management published between 1995 and 2022. Through an in-depth literature analysis, this paper demarcates geopolitical disruptions and the resulting impact on supply chains as a new subfield of research. The results indicate that the impact of geopolitical disruptions on supply chains can be mitigated through: (1) supply chain re-design including regionalisation, back-shoring, and moving away from just-in-time delivery models as well as (2) the implementation of emerging technologies, such as blockchain, 3D printing and artificial intelligence, to improve supply chain transparency and the development of modularised manufacturing. This paper is one of the first to define the current state of research and thinking on the impact of geopolitical disruptions on supply chains, laying a firm foundation for future research by setting a detailed research agenda based on identified gaps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".