Apple Inc's Massive Demand for Chips and Semiconductors under COVID-19 and its Response Strategies
Bibliographic record
Abstract
COVID-19 has been in the spotlight since the end of 2019. The impact of the spread of the epidemic is not only limited to the inconvenience of people's lives, but also leads to the inability of most companies and related industries to operate and produce normally. Not only can most human resources not be restored to normal in a short period of time, but also the chip supply chain's untimely supply leads to the emergence of chip supply shortages, which also reflects the fragility of the supply chain network. This study focuses on the impact of the global chip supply chain shortage on Apple in recent years and how to address it. The study was conducted by understanding Apple's current business strategy and how it has been adjusted according to the chip market. At the same time, the company's current data analysis of the main products, you can clearly see the current chip shortage problem for Apple's impact. The main problems of the chip industry are in the chip production supply chain, the complexity of chip production, and the lack of raw materials, so the semiconductor can not be normalized production, but the market demand for chips is increasing day by day. To reduce the chip shortage brought about by the loss of profits, Apple needs to further develop its own supply chain to achieve chip supply sustainability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".