Optimising industrial efficiency: integrating K-Means clustering and data Science for sustainable manufacturing and waste Reduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the practical application of K-means clustering analysis within industrial settings to optimise machine performance and operational efficiency. By collecting data every minute from 34 machines within a multinational company, we constructed an extensive time-series database. This database facilitated the classification of machine operations into five distinct speed classes, allowing us to meticulously analyse the time machines spent in each class to evaluate their operational efficiency. Through this analytical approach, we identified optimal performance levels, thereby enhancing managerial decision-making. The results highlight the significant benefits of employing advanced data analytics to refine industrial operations, contributing valuable insights to both the fields of industrial engineering and management. The use of sophisticated data analytics not only fosters academic advancement but also acts as a potent tool for industry application, underscoring its essential role in evolving manufacturing practices towards greater sustainability and waste reduction.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it