Global Disruption of Semiconductor Supply Chains During COVID-19: An Evaluation of Leading Causal Factors
Bibliographic record
Abstract
Abstract The coronavirus pandemic has caused unprecedented supply chain disruptions globally, resulting in a heightened need for supply chain resilience. Particularly in the case of semiconductor chips, a commodity already in high demand, the existing challenges in supply chains have been aggravated by the pandemic. This global shortage is resulting in manufacturing disruptions across multiple sectors from automobiles to electronics. The global automobile industry alone is said to suffer a $210 billion loss in revenue from chip shortages. This highlights the cruciality of scientifically analyzing and building solutions that addresses the issue of resiliency of global semiconductor supply chains. While several news articles and white papers have reported this issue, there has been a lack of scientific literature on this topic. The objective of this paper is to identify the factors causing semiconductor shortage, analyze, and quantify their impact on the supply chain. This paper identifies 20 factors under 4 major categories from pre- and post-pandemic era, in the period ranging from 2018 to 2021, that have contributed to this disruption. The categories are: geopolitical tensions, natural disasters, logistics challenges and COVID-19 pandemic. The factors are ranked using the Analytical Hierarchy Process (AHP) methodology. The scientific value of this study lies in its contribution of quantifying and ranking the impact of the individual factors leading to the recent disruption in semiconductor supply chains. The results of this study will provide supply chain managers with the analytical information necessary for enabling resilient semiconductor supply chains as they navigate through these current challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".