Information Acquisition for Capacity Planning via Pricing and Advance Selling: When to Stop and Act?
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates a capacity planning strategy that collects commitments to purchase before the capacity decision and uses the acquired advance sales information to decide on the capacity. In particular, we study a profit-maximization model in which a manufacturer collects advance sales information periodically prior to the regular sales season for a capacity decision. Customer demand is stochastic and price sensitive. Once the capacity is set, the manufacturer produces and satisfies customer demand (to the extent possible) from the installed capacity during the regular sales period. We study scenarios in which the advance sales and regular sales season prices are set exogenously and optimally. For both scenarios, we establish the optimality of a control band or a threshold policy that determines when to stop acquiring advance sales information and how much capacity to build. We show that advance selling can improve the manufacturer's profit significantly. We generate insights into how operating conditions (such as the capacity building cost) and market characteristics (such as demand variability) affect the value of information acquired through advance selling. From this analysis, we identify the conditions under which advance selling for capacity planning is most valuable. Finally, we study the joint benefits of acquiring information for capacity planning through advance selling and revenue management of installed capacity through dynamic pricing.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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