Bibliographic record
Abstract
Both traditional retailers and e-tailers have been implementing omnichannel strategies such as buy online, pick up at store (BOPS). We build a stylized model to investigate the impact of the BOPS initiative on store operations from an inventory perspective. We consider two segments of customers, namely store-only customers who only make purchases offline and omni-customers who strategically choose between offline and online channels. We show that BOPS may either benefit or hurt the retailer depending on two fundamental system primitives: the store visiting cost and the online waiting cost. If the online waiting cost is relatively low and the store visiting cost is even lower, BOPS can induce omni-customers to migrate from online buying to BOPS, leading to demand pooling at the brick-and-mortar (B&M) store. Such demand pooling provides two benefits for the retailer: it reduces the overstocking cost, and after inventory reoptimization, it results in a higher fill rate at the B&M store, which benefits existing customers and potentially attracts more customers to the store. In contrast, if both store visiting and online waiting costs are relatively high with the latter even higher, introducing BOPS can result in demand depooling as a result of the migration of the omni-customers from offline purchasing to BOPS. This leads to a lower fill rate after inventory reoptimization, likely the result of a lower profit margin under BOPS, which turns away store-only customers and hurts the retailer. This paper was accepted by Charles Corbett, operations management.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".