Price and Order Postponement in a Decentralized Newsvendor Model with Multiplicative and Price-Dependent Demand
Bibliographic record
Abstract
We analyze the effect of price and order postponement in a decentralized newsvendor model with multiplicative and price-dependent demand, wherein the manufacturer sets the wholesale price, and possibly offers a buyback rate, and the retailer determines the order quantity and retail price. Such postponement strategies can be used by the retailer by delaying his operational decisions (order quantity and retail price) until after demand uncertainty is observed. We show how the equilibrium values of the contract parameters and profits are affected by (i) vertical competition, (ii) type of contract (wholesale price-only or buyback), (iii) demand distribution, (iv) form of the expected demand function, and (v) the timing of the retailer's operational decisions. Although in most cases postponement is quite beneficial for the channel members, we show that for some model parameters, due to vertical competition, the expected value of perfect information about demand for price postponement and order postponement may be negative for the channel and even, surprisingly, for both members. We also show that when a buyback option is offered, neither order postponement nor price postponement has an effect on the equilibrium wholesale price, profit allocation ratio between the manufacturer and the retailer, and channel efficiency, and that the equilibrium wholesale price, expected retail price, profit allocation ratio between the manufacturer and the retailer, and channel efficiency in the model with buyback options under either order or price postponement further coincide with their counterparts in the corresponding deterministic model.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".