Developing a purchasing strategy for high chromium grinding media. Case company: KGHM Polska Miedź S.A, Ore Enrichment Plants.
Bibliographic record
Abstract
This thesis was conducted according to the needs of KGHM Polska Miedź S.A. The company was interested in improving the purchasing strategy of high chromium grinding media, which are relatively new to Ore Enrichment Plants and require further research.\n\nThe objective of this thesis was to give development suggestions to Ore Enrichment Plants on their purchasing strategy. The researcher analysed two different aspects helping to answer the Research Problem. According to the received and studied data it was important to determine the current purchasing strategy of high chromium grinding media is the company’s experience of using the current purchasing strategy.\n\nThe thesis project was initiated in November 2012 and was concluded in August 2013. Theoretical literature has been studied in October and November 2012 in order to support cooperation with KGHM Polska Miedź S.A. Studies included purchasing process, product characteristics, contracting, supplier selection and evaluation, quality control and industrial customer behavior. Data was collected between December 2012 and May 2013. Data collection methods included e-mails and telephone consultation, and interviews at the company’s headquarters. The research tools aimed at elaborating on the current and previous experiences in using certain purchasing strategies and finding the best way to suggest valuable improvements. Improvement ideas were based on personal observations and judgement.\n\nIt was found that the current purchasing strategy for high chromium grinding media is already well developed and a lot of attention is given to supplier selection and quality issues. Some development ideas were also given. One example are changes in the purchasing process by adding tender procedures, supplier database maintenance and purchasing strategy unification in relation to KGHM International in Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".