A waste relationship model and center point tracking metric for lean manufacturing systems
Why this work is in the frame
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Bibliographic record
Abstract
Lean manufacturing is about eliminating waste, which requires the creation of waste metrics that are tracked in order to create the conditions for its elimination. In this article, metrics used to monitor the seven traditional non-value adding wastes types of overproduction, defects, transportation, waiting, inventory, motion, and processing are explored and a “center point metric pair” is proposed that can give systematic insight into system waste performance and trade-offs. For example, lower work-in-process levels (inventory waste) may require more replenishment (transportation waste) in order to maintain production. A waste relationship model is proposed that can be used to derive the relationship between different wastes in a Pareto-optimal waste-dependent lean system. The trade-off relationships are statistically verified using simulation experiments across different system configurations, complexities, and planning scenarios.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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