Coordinating Contracts for Decentralized Supply Chains with Retailer Promotional Effort
Bibliographic record
Abstract
In this paper, a risk-neutral manufacturer sells a single product to a risk-neutral retailer. The retailer chooses inventories ex ante and promotional effort ex post. If the wholesale price exceeds marginal production cost, the retailer orders fewer than the joint profit-maximizing inventories. If the manufacturer attempts to coordinate inventories by buying back unsold units, then the retailer's promotional incentives are dulled. Under very general assumptions on the form of the effort function, we show that buy-backs adversely affect supply chain profits, and higher buy-back prices imply lower profits. Also, while a buy-back alone cannot coordinate the channel, coupling buy-backs with promotional cost-sharing agreements (if effort cost is observable), offering unilateral markdown allowances ex post (if demand is observable but not verifiable), or placing additional constraints on the buy-back (if demand is observable and verifiable) does result in coordination. This problem is not limited to returns policies but is shown to hold for a much larger set of contracts. The results are quite robust (e.g., when the retailer chooses effort before observing demand), but coordinating contracts become more problematic if, for example, the retailer also stocks substitutes for the manufacturer's product. Other model extensions are also discussed.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".