An Empirical Analysis of Intra‐Firm Product Substitutability in Fashion Retailing
Bibliographic record
Abstract
This study offers an empirical investigation of inventory and sales dynamics in a large‐scale retail network setting. We infer the impact of product shortages on sales in neighboring outlets using unique data from a large fast fashion retailing chain and an Instrumented Difference‐in‐Differences (DDIV) methodology. Our analysis reveals that sales for a particular item at a focal store increases when that same item experiences stock‐outs in neighboring stores. Our empirical findings suggest that there is substitutability across stores, and that this substitutability is the strongest in the period when the stock‐out is observed for the first time, and decreases as time passes following the stock‐out. In order to assess the implications of considering the impact of stock‐outs on inventory allocation, we develop an optimization model that is calibrated using parameters estimated via our earlier DDIV analysis. The simulation analysis confirms that revenues markedly improve on average by 6% under low demand variance and by 14% under high demand variance when neighboring stock‐out information is taken into account for sales forecasting while optimizing initial inventory allocations. Finally, we conduct sensitivity analysis to evaluate how these potential revenue improvements vary with turnover, product price, and inventory.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".