A Demand Estimation Procedure for Retail Assortment Optimization with Results from Implementations
Why this work is in the frame
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Bibliographic record
Abstract
We consider the problem of choosing, from a set of N potential stock-keeping units (SKUs) in a retail category, K SKUs to be carried at each store to maximize revenue or profit. Assortments can vary by store, subject to a maximum number of different assortments. We view a SKU as a set of attribute levels and also model possible substitutions when a customer's first choice is not in the assortment. We apply maximum likelihood estimation to sales history of the SKUs currently carried by the retailer to estimate the demand for attribute levels and substitution probabilities, and from this, the demand for any potential SKU, including those not currently carried by the retailer. We specify several alternative heuristics for choosing SKUs to be carried in an assortment. We apply this approach to optimize assortments for three real examples: snack cakes, tires, and automotive appearance chemicals. A portion of our recommendations for tires and appearance chemicals were implemented and produced sales increases of 5.8% and 3.6%, respectively, which are significant improvements relative to typical retailer annual comparable store revenue increases. We also forecast sales shares of 1, 11, and 25 new SKUs for the snack cake, tire, and automotive appearance chemical applications, respectively, with mean absolute percentage errors (MAPEs) of 16.2%, 19.1%, and 28.7%, which compares favorably to the 30.7% MAPE for chain sales of two new SKUs reported by Fader and Hardie (1996). This paper was accepted by Yossi Aviv, operations management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it