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Enregistrement W2993267916

One-Number Forecasting: Heinz's Experience and Learning

2008· article· en· W2993267916 sur OpenAlexaboutno aff
Sara Park

Notice bibliographique

Revue˜The œjournal of business forecasting · 2008
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueGlobal Trade and Competitiveness
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCompetitor analysisMarketingProduct (mathematics)Agricultural scienceProduct categoryAdvertisingBusinessAgricultural economicsEconomicsCommerceMathematics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The longer the forecasting period gets extended into the future, the more uncertainties, volatility, and biases get introduced ... forecasting at the lowest level accounts much more effectively the growth or decline in a segment .... with a one-number forecast, it is easy to reconcile demand and supply. Henry J. Heinz founded his company in 1869 when he was only 25 years old. His company's first product was horseradish in clear glass bottles; his competitors were filling their opaque bottles with fillers such as sticks and leaves. Our world-famous tomato ketchup was first introduced in 1875, quickly becoming a condiment of choice. Heinz's first venture overseas was in England in 1886, 17 years after the company's inception. Many British people believe that it is an English company known for its bean and soup products. In 1958, Heinz made its first acquisition and never looked back. Today, it is the most globalized U.S.-based food company with many recognizable brands sold in more than 50 countries as either number one or number two in their category. Some of those categories include: Ketchup, condiments, sauces, frozen entree and snacks, baby food, soup, pasta, and beans. Heinz North America (HNA) is based in the United States and Canada; the U.S. portion comprises two large business units: Consumer Products (CP) and Foodservice (FS). Consumer Products are further broken down into dry and frozen brands. Dry brands include Heinz Ketchup, condiments such as pickles, vinegar, and relish, WyIer's Bouillon, and sauces such as 57 Sauce, Home-style Gravy, and Classico Pasta Sauces. Frozen brands include Smart Ones Frozen Meals and Desserts, Boston Market Meals and Sides, Ore-Ida Potatoes, Bagel Bites, and TGIF Appetizers. Each CP brand has a certain structure in its functions: For business development and growth it includes Brand Management (Marketing), Market Research, Research & Development, Category Development/ Field Sales, and Consumer Marketing/CoMarketing. For supply chain management and operations, it is Demand & Supply Planning, Transportation & Warehousing, Procurement, and Manufacturing; and for budgeting and financial planning and support, Finance. FORECASTING: THE BEGINNING The department of Forecasting & Demand Planning has greatly evolved since its inception in January 2002 with the hiring of a manager from KimberlyClark to head up the forecasting efforts and report to VP of Marketing. Until then, the responsibility of volume forecasting resided with Marketing/Brand Management, which posed both benefits and challenges. Some of the benefits were that the owners of each brand (i.e., brand managers) led initiatives to grow and drive their businesses while keeping tuned to their consumers' behaviors and preferences, as well as their competitive activities in the marketplace. However, challenges included the presence of multiple motives behind calling the volume. Brand Management teams tended to be optimistic with their marketing programs and launch of new items in order to obtain more supportive funding, while Sales veered rather conservatively because of their targets. As such, various departments used different forecasts throughout the organization even though the uploaded volume (base-line forecast) was the same. For instance, Finance might have added more optimism to the forecast, while Production Planning may have applied a bit of conservatism to maintain low inventory. Therefore, everyone, especially Brand Management and Sales, ended up spending too much time on debating true demand. Their estimates differed from each other because of the use of different assumptions. When the shipment did not materialize as forecasted, everyone had their own explanation of why it missed the volume call. Despite all that, the Forecasting Department, a third party, maintained its obj ectivity in developing forecasts by using consistent formats and methodologies. …

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,176
Score d'incertitude au seuil0,757

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0000,000
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 Opus0,066
Tête enseignante GPT0,227
Écart entre enseignants0,160 · 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'étudeObservationnel
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
Publié2008
Routes d'admission1
Résumé présentoui

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