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Record W297882562

Reflections of an International Forecaster

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

Bibliographic record

VenueThe Journal of Business Forecasting Methods & Systems · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness cycleGovernment (linguistics)Metric systemEconomicsMacroeconomicsLinguistics
DOInot available

Abstract

fetched live from 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. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.110
GPT teacher head0.354
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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