Optimal markdown policies for perishable products with fixed shelf life
Bibliographic record
Abstract
The market for perishable products is subject to short selling seasons and volatile demand. In response, retailers often use markdowns to maximize revenue and minimize waste. Among the markdown options, the best policy is not always clear-cut, as there is a trade-off between the complexity of the policy and the revenue generated. Despite its importance, the value of different markdown policies remains understudied in the literature. To address this gap, we present a joint inventory-pricing model for perishable items with fixed shelf lives. Our study is the first to examine the effectiveness of different markdown policies, including single-stage, multiple-stage, and dynamic markdown policies, theoretically and numerically. Empirical evidence from two case studies based on real-life data is used to show the performance of the proposed models. We prove that the value of markdown policies asymptotically vanishes as the shelf life, market demand, or customers’ maximum willingness-to-pay (WTP) increases. Conversely, the benefits of markdown policies increase when per unit expiration, shortage, and production costs rise. Further, our results suggest that while single-stage markdown policies can significantly benefit the system, in some cases, the benefit of multiple-stage markdown policies over the single-stage policies is insignificant and may not justify their complexities.
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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 | 0.002 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".