Audit and compliance in supply chains with damage cost sharing under supplier’s responsibility standards
Bibliographic record
Abstract
Governmental and industry standards enforce responsibility in supply chains. However, supplier violations often impose costs on buyers, leading to misaligned incentives. Buyer audits act as proactive measures to ensure supplier compliance, while reactive measures hold non-compliant suppliers accountable for damages.We examine the interaction between buyer audits and supplier safety compliance when reputable buyers and a supplier share damages from non-compliance. Buyers and the supplier set their audit and safety levels, respectively, with the supplier bearing a fraction of the damage cost for under-compliance.Our findings indicate that stricter audits enhance supplier safety when damage costs are low but not when costs are high. Likewise, joint audits can compromise supplier safety at high damage costs but enhance it at lower costs. However, shared audits can result in better supplier safety than joint audits. Finally, with high damage costs, suppliers profit more from joint audits than independent audits, while buyers achieve maximum profits in audit scheme that has audit level sufficiently higher than the others.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".