Is the global supply chain hard to reverse? Understanding manufacturing strategies of Chinese, Japanese, and South Korean Firms
Bibliographic record
Abstract
Abstract As the Covid pandemic underscores global supply chain risks, there is a debate on whether to bring US manufacturing back from overseas. This paper provides insights into the heated debate on the global supply chain by examining the competitive manufacturing environments of China, Japan, and South Korea. More specifically, we conduct a cross‐national survey and empirically investigate the manufacturing strategies employed by manufacturing managers in the top Asian players: China, Japan, and South Korea. We examine four dimensions of the manufacturing strategies: quality, inventory, flexibility, and top management involvement. Our findings indicate that Japanese manufacturers are more committed to the cumulative approach to quality management and see enhanced flexibility as a strategic priority. While Chinese managers are also committed to achieving quality, they are more delivery‐driven and thus are more likely to occasionally accept slightly off‐quality components from suppliers to “save” an order. However, in all three countries, managers with a high focus on quality also focus on just‐in‐time management and in turn, on flexibility. There is significantly less agreement among Chinese managers, compared to their Japanese and Korean counterparts, that the top management should be involved in operational planning, goal setting, and the provision of rewards.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".