Order quantities for style goods with two order opportunities and Bayesian updating of demand. Part II: capacity constraints
Bibliographic record
Abstract
Abstract This paper is the second of two papers that study the problem of ordering a family of style-goods products where demand is uncertain and there are two order opportunities. The first opportunity has a long lead time and low unit cost. The second opportunity has a short lead time and high unit cost. During the time between the two order opportunities new information on demand becomes available. The information is used in a Bayesian estimation process to revise demand forecasts. There are capacity constraints at both order opportunities. The first paper (Miltenburg, J. and Pong, H.C., Order quantities for style goods with two order opportunities and Bayesian updating of demand. Part I: No capacity constraints. Int. J. Prod. Res., 2007, 7, 1643--1663) studied the problem without capacity constraints. The problem examined in this paper is based on problems at a real company. Several inventory models having different information and computation requirements are developed to determine good order quantities. The models are organized into a framework that real companies can use to select the best problem statement and solution procedure for their particular situation. Keywords: Style goods inventory problemFashion goods inventory problemBayesian estimation Acknowledgements This research was supported by Grant OGP5474 from the Natural Sciences and Engineering Research Council of Canada. Notes †The tilda symbol (∼) in means X is a random variable.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".