Order quantities for style goods with two order opportunities and Bayesian updating of demand. Part I: no capacity constraints
Notice bibliographique
Résumé
This paper is the first of two 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 no capacity constraints at the order opportunities. The second paper (Miltenburg, J. and Pong, H.C., Order quantities for style goods with two order opportunities and Bayesian updating of demand. Part 2: capacity constraints. Int. J. Prod. Res., 2007 (in press)) extends the results in this paper to the situation where there are capacity constraints. A number of inventory models having different information and computation requirements can be used to determine good order quantities. We find that complex models are appropriate for the most important A items. Simple models are best for other A items and for B and C items. The motivation for studying this problem is the experience of a real company. PTK has one medium-size factory and a chain of retail stores in Canada and the United States. The factory produces about one-third of the company's products. The other two-thirds are produced by suppliers, most of whom are located in China. About half of PTK's products are style-goods. There are two selling seasons for style-goods products: winter and summer. The style-goods products produced in China are ordered twice: first, about six months before the beginning of the products’ selling season, and second, very near the beginning of the selling season. The cost of products ordered at the first order opportunity is low because production cost and transportation cost are low. The cost of products ordered at the second order opportunity is high because production is expedited and transportation is speeded up. Demand for style-goods products is difficult to forecast. Forecast accuracy is poor at the first order opportunity. During the six months between the first and second order opportunities new information on competitors’ products, fashion trends, weather, the economy, promotional activity, and so on becomes available. This information is used to revise the demand forecast and adjust the order quantities.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».