Notice bibliographique
Résumé
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,019 | 0,012 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,011 | 0,035 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,006 |
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 source (Gemma direct ou Codex distillé), 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 ».