The Hidden Cost of Supply Chain Disruptions: Case Study of the UK’s Automotive Sector
Bibliographic record
Abstract
As the world economy has become increasingly integrated the spectre of transnational supply chains has become a central feature of globalisation. The smooth and unfettered working of transnational supply chains has facilitated efficiency increasing changes to business operations (such as just in time inventory management). The automotive sector worldwide has been at the forefront of internationally integrated supply systems. The European Union (EU) has, in part, been structured to reduce friction in Europe-wide supply chains through the single market. Transnational supply chains are at the heart of United Kingdom (UK) – EU trade, and the UK’s departure from the EU’s single market (Brexit) will increase friction in international trade. This case study of the UK’s automotive sector uses a social network approach to analyse supply chain linkages between the UK, EU and other trading partners, and how these could be impacted as a result of Brexit. We use data from Trade in Value Added (TiVA) and World Input-Output Database (WIOD) to map supply chains, estimate total value-added in exports and examine how Brexit is likely to impact the competitiveness of UK exports. Results confirm that the UK’s automotive sector is closely integrated with the EU. To offset the loss of UK’s export competitiveness after Brexit, trade facilitation measures complemented with a duty drawback scheme could be an option in the short run. Policy measures are, however, unlikely to replace the benefits of duty-free and frictionless access enjoyed under single market trading arrangements. This suggests that the UK automotive sector, which is primarily comprised of globally active firms, may have to reconfigure supply chain arrangements and in the long run alter how decisions pertaining to locations are made. Keywords: Brexit, global value chains, input-output linkages, WIOD.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".