Assortment Planning in Omnichannel Retailing Under Product Returns and Showcase Capacity
Bibliographic record
Abstract
ABSTRACT We investigate the assortment planning decisions of a retailer that operates an online sales channel and a brick‐and‐mortar store. We explicitly investigate the impact of product returns, which is a norm in modern retailing and a factor for lost profit. Assortment decisions affect product returns as showcased products reveal information to online shoppers who visit the physical store before making their purchase. We model customers' purchase and keep‐or‐return decisions through a multinomial logit choice model and derive the retailer's expected profit function. Using analytical and numerical results, we show that (i) if the cost of handling returns is not too high, allowing returns can lead to substantial increase in profit, (ii) an increase in returns does not necessarily mean a decrease in profit, (iii) retailers are generally better off if the hidden attribute levels are slightly undervalued rather than correctly or overvalued, (iv) even if there is shelf‐space capacity available for free, it may be optimal not to utilize it fully, and (v) under a generous refund policy, retailers should reveal a limited number of undervalued levels; whereas, for the overvalued levels, their action depends on relative sizes of the online and offline market segments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".