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Enregistrement W297882562

Reflections of an International Forecaster

2004· article· en· W297882562 sur OpenAlexaboutno aff
Sean Reese

Notice bibliographique

RevueThe Journal of Business Forecasting Methods & Systems · 2004
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueGlobal Trade and Competitiveness
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBusiness cycleGovernment (linguistics)Metric systemEconomicsMacroeconomicsLinguistics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The issues that an international forecaster has to deal with are different from those of a domestic forecaster because of differences in culture, market characteristics, lead time, seasons and business arrangement ... the forecaster has to keep an eye on all the events, which differ from country to country, but have an impact on forecasts ... unusual spikes, caused very often by unexpected business expansions, are common in many foreign markets. A gram, a gram, it's a little more than a raisin... About the same as a paper clip, now isn't that amazin'? Remember that ditty from the Schoolhouse Rock® series on Saturday morning cartoons? If you do, you are probably in your thirties. It was during the Ford/Carter era that our government made a brief, abortive attempt to convert the United States citizenry to the metric system. Though my friends think I'm daffy, I think it was a good idea and wish that the initiative had succeeded. Metric is so much simpler. Working at a U.S. based company, and being in charge of forecasting for our international business, contending with metric conversion issues is all part of a day's work for me. It is sometimes difficult getting my business associates to think in terms of metric units, on par with me asking for things in a foreign language. My reports require special conversion tables that our IT department has to maintain. Often I feel like a bilingual mediator between two opposing cultures. SEASONAL INVERSION This is just one challenge among many for the forecaster of international goods. Consider the seasonal inversion between the northern and southern hemispheres. Our summer is their winter, and vice versa. For a juice company such as mine, where summertime drink refreshment is a big selling point, it makes a difference whether you are doing a forecast for Canada or Peru. With seasonal buying patterns reversed, you have got to apply the seasonality component of the forecast properly. Speaking of Peru, I was recently talking with my contact down there and she told me that middle class households buy smaller-sized bottles of juice. Why? Because the socioeconomic strata of that region is such that most middle income households can afford a maid, who does most of the grocery shopping for that family. The maid shops just about every day, so there's no need to buy a large bottle that will last a whole week. CULTURAL DIFFERENCES There are many cultural differences. For example, Latin American cultures prefer bright, vibrant colors. Ocean Spray developed a line of unique products for these markets, called Cran-Caribe(TM), that is full of reds, oranges, and yellows. Conversely, consumers in Asian markets tend to be suspicious of bright-red colored drinks, thinking them perhaps unnatural. Sweetness is another factor, in which U.S. consumers tend to prefer sweeter food products than people in European countries. These factors affect the formulation, marketing, sales, and, consequently, the forecasting of our beverages for each country. LEAD TIME Our forecasts are based on the date at which products ship from our warehouse to the customer. For domestic sales, there is a lead-time of several days from us to them. For international shipments, the period can be a month or more when you factor a combination of truck, rail, and cargo ship to the other side of the world. When I talk with my international sales contacts, they tend to think in terms of the date they need the product, without considering the transit time. …

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)0,003
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: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,501
Score d'incertitude au seuil0,458

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,002
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 Opus0,110
Tête enseignante GPT0,354
Écart entre enseignants0,244 · 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'étudeSimulation ou modélisation
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

Citations0
Publié2004
Routes d'admission1
Résumé présentoui

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