Cooperation in assembly systems with supply risks
Bibliographic record
Abstract
Interdependence among supply chain members increases their vulnerability to supply risks. This study explores the optimal decisions of supply chain members and their interactions within an unreliable assembly system facing supply risks. The assembler purchases complementary components from suppliers to assemble them into a final product, which requires exactly one unit of each component. First, we examine how the stochasticity and severity of supply risks influence the proactive and reactive decisions of assembly system members. We then analyze the interaction between upstream cooperation and supply risks. Our results show that the assembler’s response varies depending on the severity of the observed supply risk, including maintaining the status quo, increasing the selling price, or increasing orders from the emergency source. Furthermore, potential supply risks can break down the one-to-one correspondence between complementary components. Our findings suggest that, when potential supply risks are stochastically lower, suppliers lose their incentive to cooperate. The realized value of cooperation depends heavily on the observed supply risk and may even become negative if inappropriate wholesale prices are negotiated, making it a double-edged sword in the context of unreliable assembly systems. Therefore, upstream suppliers must carefully evaluate cooperation and price negotiations before committing to such agreements.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".