MARKETING AND INNOVATIONS IN CHEMICAL AGRI-INDUSTRY THROUGH THE SWOT AND PEST ANALYSIS
Bibliographic record
Abstract
This paper is dedicated to the reflection of innovations in the mirror of marketing. The best way to do so is to consider specific product, company or market. The production of mineral fertilizers is one of the most important branches in the chemical industry. The potash market is exceptional in characteristics, which make it an interesting object for marketing analysis with emphasis on innovations. The comparative marketing analysis of main potash producers has been performed in order to delineate the best potash supplier for EU market. SWOT and PEST analysis as well as Porter's five competitive forces model were used. It has very important economic advantages aroused from Canadian tax system, facilitating the company to work up a market by lowest prices for potash fertilizers. However, transportation costs to EU market are relatively large. Recently Uralkali started massive retrofit installation, completed the takeover process of Silvinit, which has set company to the second place in the international potash market. The main disadvantages of the company are relatively low quality of the product and high transportation cost to EU market. On the other hand, Belaruskali remains to be the best supplier for EU countries from the point of geographical location and product quality, since German K+S cannot cover all EU market demand. Despite highly unfavourable political environment, the main company's asset is high quality potash fertilizer. Recently, a radical innovation - the balling granulation - has been introduced by Belaruskali, which allowed obtaining the product with superior characteristics. Finally, the estimation of the economic effect for balling granulation has been performed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".