Optimal Restocking Fees and Information Provision in an Integrated Demand-Supply Model of Product Returns
Bibliographic record
Abstract
Product returns cost U.S. companies more than $100 billion annually. The cost and scale of returns management issues necessitate a deeper understanding of how to deal with product returns. We develop an analytical model that describes how consumer purchase and return decisions are affected by a seller's pricing and restocking fee policy. Taking into account the consumers' strategic behavior, we derive the seller's optimal policy as a function of consumer preferences, consumer uncertainty about product attributes, consumer hassle cost for returns, and the effectiveness of the seller's forward and reverse channel capability. We allow for two sources of consumer uncertainty and show how the seller may use its price and restocking fee as a means of targeting a segment of consumers who know their product consumption utilities. We find that even if it is possible to eliminate returns costlessly through the provision of information about the fit between consumer preferences and product characteristics, returns can nevertheless be part of an optimal product sales process. That is, we identify conditions under which it is (or is not) optimal to provide product fit information to consumers. We show that the marginal value of information to the seller is decreasing in the operational efficiency of the seller's forward and reverse logistics process as well as the level of product uncertainty. We identify the impact of multiple product options and sources of consumer uncertainty on the model's results. The analysis generates testable hypotheses about how consumer-level and seller-level parameters affect the return policies observed in the marketplace.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".