Bibliographic record
Abstract
In order to achieve a fully integrated manufacturing supply chain and to maximize its effectiveness and efficiency, the manufacturing supply chain needs to be assessed for its performance. My thesis has two main objectives: 1. To develop a new methodology for the performance measurement of manufacturing supply chain. 2. To evaluate manufacturing supply chain performance and carry out a comparative analysis of existing supply chains. \n \nTo accomplish the first objective a simple, generic and comprehensive tool for measuring the performance of supply chains was developed. The tool was validated by several interviews from various industries. \n \nIn order to achieve the second objective the proposed tool was used as a basis for a questionnaire, and a survey of the manufacturing supply chains across various countries and industries was conducted. The results show that even though performance measurement in the whole supply chain is considered as critical by many respondents, some supply chains have not implemented any performance measurement system. A four-factor index for the assessment of the supply chain performance was developed and used. The results suggest that the supply chains which use performance measurement systems are perceived as better performing than those which do not use any performance measurement systems. Also, the weighted performance scores for the national supply chains were higher than the scores for the international ones. Finally, supply chains with strategic alliance showed better performance than those which do not have strategic alliance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".