Quality investment strategies in a complementary supply chain with an unreliable supplier
Bibliographic record
Abstract
This study investigates investment strategy in a supply chain that comprises one manufacturer and two complementary suppliers - a reliable supplier and an unreliable supplier. The unreliable supplier's quality improvement capacity is uncertain. Where the manufacturer determines to invest in which suppliers' quality improvement activities and suppliers decide the quality improvement levels of their components, respectively. We demonstrate three potential strategies to highlight the manufacturer's and suppliers' optimal choices: investing in an unreliable supplier, investing in a reliable supplier, and investing in both. Investing in two suppliers results in higher quality improvement levels and profits for the manufacturer, and the optimal level of product quality improvement is not monotonically related to the efficiency rate. The unreliable supplier can benefit the most from the investment strategy, while the manufacturer profits the least. The uncertainty of an unreliable supplier is more likely to affect a reliable supplier than himself. There are two effects: mutual hold-up and spillover effects result in counter-intuitive findings. Lastly, we relax our assumptions to examine their impacts on the manufacturer's strategy choice.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".