Supplier encroachment and pricing scheme choice in a supply chain with two-sided uncertainties
Bibliographic record
Abstract
This paper examines supplier encroachment in a scenario where the supplier procures an input in spot and processes it into a final product, while the retailer chooses between contingent and committed pricing schemes for the contract price. We find that when the correlation between the input spot price and final product demand is relatively small, it is optimal for the retailer to adopt the contingent pricing scheme regardless of the supplier's encroachment decision. This is because this scheme enables the supplier to share the input price risk, resulting in a lower contract price. Furthermore, the likelihood of the retailer adopting the contingent pricing scheme increases as demand variability decreases or spot price volatility increases. However, supplier encroachment reduces the retailer's inclination to use the contingent pricing scheme. We further demonstrate that the supplier should encroach when the correlation is sufficiently small, because encroachment alleviates double marginalisation in the retail channel and brings the additional responsiveness in the direct channel. By contrast, a large correlation compels the supplier to encroach even with a sufficiently small market size. In this case, encroachment improves the responsiveness of both retail and direct selling quantities and enables the quantities to match the demand better.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".