PRICING AND INVENTORY DECISIONS WITH UNCERTAIN SUPPLY AND STOCHASTIC DEMAND
Bibliographic record
Abstract
We consider a retailer, facing uncertain supply and price-sensitive stochastic demand, who has to make stocking and pricing decisions for a given selling period. We also consider the case when the demand is price-sensitive deterministic and provide a unified framework for the model with additive errors. For both scenarios, we look at the case when the price is set before receiving the supply, called simultaneous pricing and the case when the price is set after receiving it, which is called postponed pricing. We develop a procedure for finding the optimal policy for the retailer with general distributions for the supply and the demand. To study the effect of supply uncertainty on expected profit, we conduct sensitivity analysis and develop results for both pricing scenarios and give insights. The results have important implications for a retailer in the supply chain, where a portion of the inventory may be lost due to variety of factors including mishandling and failure to meet quality standards. The findings shed light on the nature and role of prices and their relationship to supply and demand.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".