Bibliographic record
Abstract
This report provides an overview of the world trade of kitchen furniture, with statistical data (production, consumption, imports, exports, in volume and value) for 60 countries selected according to their contribution to the international trade of kitchen furniture. The report identifies the opportunities that arise in the global kitchen furniture market and it is a helpful tool for companies exporting kitchen furniture as it contains a rich collection of key country data, allowing comparisons among different areas. Production and consumption of kitchen furniture are given at world level and by country, both in value and units. International trade statistics (imports and exports) of kitchen furniture by country of origin/destination are included, as well as trade balance data covering the years 2014-2019. Forecasts up to 2023 are provided for the world market (in real term) and the international trade (US$ value). Statistics and outlook data are also available in a country format. They include: historical series (2014-2019) of kitchen furniture trade by country of origin and destination; production, exports, imports and consumption data in value for the years 2014-2019 and data in volume for 2019, economic indicators (population, households, household consumption expenditure), exchange rates local currency per US$ and local currency per EUR; population, GDP, kitchen furniture market real growth (forecast 2020-2023); a comparison with imports in volume of selected built-in appliances (hoods, refrigerators, dishwashers) for the last available year, generally 2019; estimated average unit value of kitchen furniture production, exports, imports and consumption at factory price, excluding appliances, for 2019. The third part of the report provides company profiles for 30 among the main kitchen furniture manufacturers worldwide: Al Meera (United Arab Emirates), American Woodmark (USA), Ballingslöv International (Sweden), Black Red White (Poland), Bulthaup (Germany), Cabinetworks Group (USA), Cleanup (Japan), Golden Home (China), Häcker (Germany), Haier Kitchen (China), Hanssem (South Korea), Howdens Joinery (UK), IKEA (Sweden), Itatiaia (Brazil), Lixil (Japan), Marya (Russia), Masterbrand Cabinets (USA), Nobia (Sweden), Nobilia (Germany), Nolte Küchen (Germany), Oppein (China), Panasonic (Japan), Scavolini (Italy), Schmidt Group (France), Schüller (Germany), Signature Kitchens (Malaysia), Takara Standard (Japan), Todeschini (Brazil), Valcucine (Italy), Zbom (China). Countries included in the report are: Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Croatia, Cyprus, Czech Republic, Denmark, Egypt, Estonia, Finland, France, Germany, Greece, Hong Kong (China), Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Kuwait, Latvia, Lebanon, Lithuania, Malaysia, Malta, Mexico, Netherlands, New Zealand, Norway, Philippines, Poland, Portugal, Romania, Russia, Saudi Arabia, Serbia, Singapore, Slovakia, Slovenia, South Africa, South Korea, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, Ukraine, United Arab Emirates, United Kingdom, United States, Vietnam.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.028 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".