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Enregistrement W220651198

Forecasting at Ocean Spray Cranberries

2001· article· en· W220651198 sur OpenAlexaboutno aff
Jack Malehorn

Notice bibliographique

RevueThe Journal of Business Forecasting Methods & Systems · 2001
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueHorticultural and Viticultural Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDemand forecastingWork (physics)BusinessProduct (mathematics)MarketingOperations managementOperations researchAgricultural scienceAgricultural economicsEngineeringEconomicsEnvironmental scienceMathematics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,594
Score d'incertitude au seuil0,703

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0060,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,164
Tête enseignante GPT0,336
Écart entre enseignants0,173 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2001
Routes d'admission1
Résumé présentoui

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