Information Acquisition for Capacity Planning via Pricing and Advance Selling: When to Stop and Act?
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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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".