Contract efficiency for a decentralized supply chain in the presence of quality improvement
Bibliographic record
Abstract
Abstract In this paper, we study a joint pricing and product quality decision problem in a decentralized supply chain consisting of one manufacturer and one retailer. Although the manufacturer decides the product quality with an associated cost, the retailer decides the retail price. We aim to study and compare different contract formats for this decentralized supply chain. There is a trade‐off in the choice of contracts: simpler format contract (with a few parameters) is less complicated, but the contract efficiency is low. We start with the simplest one‐parameter contract: a wholesale price contract that serves as the benchmark. We then study how contract efficiency can be improved by adding one more parameter. Specifically, we consider three two‐parameter contracts that are commonly used in reality: two‐part tariff contract, revenue‐sharing contract, and effort cost sharing contract. We find that the contract efficiency is improved under all the three contracts, but in different ways: the improvement in contract efficiency under each of them dominates the other two when manufacturer's quality improvement effectiveness is relatively low, moderate, and high, respectively. Furthermore, through numerical examples, we find that under some cases, a choice from these three two‐parameter contracts can achieve a close‐to‐perfect efficiency (>85%). Finally, we investigate whether a combination of the three two‐parameter contracts can achieve coordination. Interestingly, we find that only the combination of effort cost sharing contract and revenue‐sharing contract can achieve coordination, whereas combinations of either of them and two‐part tariff contract cannot.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".