Cooperation Modes between Competing Manufacturers in EV Supply Chain with Innovation-Driven Common Supplier
Bibliographic record
Abstract
The competition and cooperation between automobile manufacturers and battery enterprises are an important topic concerned by electric vehicle supply chain management. This paper investigates the cooperation modes between competing manufacturers in the EV (electric vehicle) supply chain, under which the common supplier launches the innovation of the key component of EV to meet the demand of two manufacturers. Three cooperation modes between manufacturers, full cooperation, partial cooperation, and noncooperation, are established to depict the pricing decisions by the Stackelberg game. We find out that, when competition degree is small, it is more profitable to choose partial cooperation, while it is more advantageous to choose full cooperation when competition degree is high, and the manufacturer’s basic market demand is relatively small. Therefore, it is always preferred for the common supplier to expect noncooperation between manufacturers. Under the background that basic market demand ratio changes with competition degree between markets, it could be better for the whole supply chain when without cooperation or partial cooperation depended on the supplier power while it could be better for customers when full cooperation or partial cooperation depended on the competition degree between manufacturers.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".