Delegation of Stocking Decisions Under Asymmetric Demand Information
Why this work is in the frame
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Bibliographic record
Abstract
Problem definition: We consider the incentive design problem of a retailer that delegates stocking decisions to its store managers who are privately informed about local demand. Academic/practical relevance: Shortages are highly costly in retail, but are less of a concern for store managers, as their exact amounts are usually not recorded. In order to align incentives and attain desired service levels, retailers need to design mechanisms in the absence of information on shortage quantities. Methodology: The headquarters knows that the underlying demand process at a store is one of J possible Wiener processes, whereas the store manager knows the specific process. The store manager creates a single order before each period. The headquarters uses an incentive scheme that is based on the end-of-period leftover inventory and on a stock-out occasion at a prespecified inspection time before the end of a period. The problem for the headquarters is to determine the inspection time and the significance of a stock-out relative to leftover inventory in evaluating the performance of the store manager. We formulate the problem as a constrained nonlinear optimization problem in the single period setting and a dynamic program in the multiperiod setting. Results: We show that the proposed “early inspection” scheme leads to perfect alignment when J equals two under mild conditions. In more general cases, we show that the scheme performs strictly better than inspecting stock-outs at the end and achieves near-perfect alignment. Our numerical experiments, using both synthetic and real data, reveal that this scheme can lead to considerable cost reductions. Managerial implications: Stock-out-related measures are typically not included in store managers’ performance scorecards in retail. We propose a novel, easy, and practical performance measurement scheme that does not depend on the actual amount of shortages. This new scheme incentivizes the store managers to use their private information in the retailer’s best interest and clearly outperforms centralized ordering systems that are common practice.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it