Notice bibliographique
Résumé
Interview with Sean Reese Q: Can you tell us something about Ocean Spray? A: Ocean Spray Cranberries is a growerowned farming cooperative with $1.5 billion in annual sales, headquartered in Lakeville, MA. The ownership is comprised of roughly 700 cranberry and 200 grapefruit growers. The company manufactures a variety of juices and fruit-based products in four primary facilities located in: Bordentown, NJ; Kenosha, WI; Henderson, NV; and Sulphur Springs, TX. Q: How large is the Ocean Spray forecasting group? A: There are seven people who work in the forecasting group. The official name of the group is Demand Planning, and it represents Ocean Spray's commitment to and investment in the forecasting field/practice. Q: What are the forecasting organization's titles and positions? A: Ocean Spray's group is composed of a Manager of Demand Planning, five Demand Planners, and one System Administrator. Q: How do they relate to the planning function? A: Demand Planning directly supports Operations. The emphasis of our work is demand-oriented, by product and by geographic area. Its primary concern is to support short-term production planning and short- to mid-term management decision-making. Q: Whom does the group report to? A: The Demand Planning Group reports to the Director of Logistics and Planning, who reports to the Vice President of Operations. Q: What type of forecasts do you make? A: We forecast at both the SKU (UPC) level and the Category/Size level. For example, 64-oz. CranApple(R) cranberry apple juice drink would be a SKU, and would be a subset of the larger 64-oz. Cranberry Drinks category/size grouping. We forecast on a disaggregated level, using cases as our base unit. As an example of the case unit, eight 64-oz. bottles of Cranberry Juice Cocktail represent one case. These case units are linked to retail accounts that correlate with Ocean Spray's sales organization. Each account is in turn linked to one of our four distribution centers around the country. We forecast the current month and the following six months, with the greatest attention on the next three months. Q: What software do you use? A: We primarily utilize Manugistics in our forecasting work. Q: Do you rely on any other MIS-type systems or support? A: On the front end, we get data feeds from SAP (an Enterprise Resource Planning system) and a proprietary intermediate system, which we call BIS. On the back end, we use Oracle, Microsoft Access, and Microsoft Excel for analysis and presentation. Q: How much data and which models do you use in forecasting? A: The forecasting models that we use in our Manugistics system are primarily time series models. We keep three years of history in that system, on which to base the statistical forecasts. We, of course, have the latitude to override the time series with event-based inputs such as promotional plans, advertising and the effects of product reformulations. We use time series forecasts as a starting point from which we query the field sales personnel as well as our Marketing team for additional market intelligence. As such, our modeling process is a part of an intensive collaborative process with the Sales and Marketing team, a process that I suppose you could call a judgmental approach. Causal and Regression-based models are deployed sparingly, mainly in support of upper-- management's strategic planning needs. Q: Why are Causal models used sparingly? A: At the disaggregated level, for which we are responsible, it is difficult to find qualified explanatory variables. Q: Do you use scanning data? If so, what types of data do you use and how do you use it? A: Yes, we do. For our domestic accounts we use IRI, and for Canada we use Neilsen data. This scanning data is used heavily by our Marketing department as they try to ascertain changes in our base volume and consumer reaction to promotions and advertising. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,006 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».