QUANTITY RESTRICTIONS AND THE RESELLER'S RESPONSE TO A TEMPORARY PRICE REDUCTION OR AN ANNOUNCED PRICE INCREASE
Bibliographic record
Abstract
Manufacturers and suppliers offer temporary reductions (or permanent increases) in the price charged to the resellers for a variety of reasons. The trade promotion may be offered to the reseller at a single point in time or over a finite time-span. In addition, in reselling situations, the end demand tends to be sensitive to selling price. It is commonly found that under such trade promotion (or in stockpiling to an announced price increase), not all the quantity purchased by the reseller at discount may be passed on to the final consumer at a reduced selling price. In fact, previous studies have shown that it is optimal for the reseller to carry forward some of the quantity purchased at discount and sell it later at the regular price. It has been suggested in the literature that by placing a restriction on the reseller's purchase quantity, the supplier can restrict the reseller's forward buy quantity. In this paper, we evaluate this approach. In the rest of the paper, we present alternate schemes which are easy to administer and which insure that the supplier avoids the spike in demand that occurs in the unconstrained problem.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| 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".