Evaluation of Semiconductor Risk Mitigation Strategies in the Electric Vehicle Supply Chain
Bibliographic record
Abstract
The study examines the effects of semiconductor scarcity on the electric vehicle (EV) supply chain caused by an increase in electronics demand after the 2020 automobile industry downturn due to the COVID-19 pandemic. The rising demand for semiconductor chips in the automotive industry, especially in EVs, necessitates strategic measures for original equipment manufacturers and suppliers to strengthen supply chain resilience. This study uses a consequence-based decision-making framework that uses a hybrid Decision-Making Trial and Evaluation Laboratory (DEMATEL) method with Interpretive Structure Modeling (ISM). By leveraging this innovative approach, the research unveils complex causal relationships among supply chain strategies, providing quantifiable insights for prioritizing resilience in the face of multifaceted risks such as trade wars, regulatory changes, and raw material shortages. In addition, the study enhances our comprehension of supply chain resilience within the electric vehicle sector by illuminating aspects that have not been thoroughly examined by the Multi-Criteria Decision Analysis (MCDA) technique employed in this research. The analysis includes the adoption of multisourcing, fostering ecosystem partnerships, and improving supply chain visibility. Through these novel insights, this analysis aims to empower stakeholders and small- to medium-sized enterprises to navigate future automotive market dynamics, with a focus on evolving manufacturer–supplier relationships in the midst of technological advancements.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".