Returns policy, in-store service, and contract strategies in the presence of customer returns
Bibliographic record
Abstract
Returns policies and in-store retail service are widely used strategies for managing customer returns. In this paper, we consider a supply chain with a manufacturer and a retailer, in which the retailer should decide its returns policy strategy by choosing between a no-refund policy (NR) or a money-back guarantee policy (MBG), as well as deciding whether to provide in-store service. We identify the retailer’s optimal returns policy and in-store service strategies. We find that while the net salvage value of a returned product is a key factor influencing the retailer’s decision on its optimal returns policy, the retailer’s in-store service strategy is dependent on its chosen returns strategy. We show that offering an MBG policy can expand the market coverage of the supply chain, while providing in-store service does not yield the same effect. The retailer’s optimal returns policy and in-store service benefit the manufacturer. However, there are cases where providing in-store service is not optimal for the retailer, but the manufacturer can benefit from it. In such circumstances, the manufacturer can use a contract to incentivize the retailer to provide in-store service. We also discuss extensions of the model to examine the impact of a non-zero residual value of the unsatisfactory product under an NR policy on the retailer’s decisions, and where the retailer endogenously determines its service level through numerical exploration.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".