Developing Decision Making Grid for Maintenance Policy Making Based on Estimated Range of Overall Equipment Effectiveness
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
In today world of competition, one of critical success factors influencing survival, profitability, and competitive advantage of manufacturing organizations is to select appropriate maintenance policy. While decision making grid (DMG) provides a relatively comprehensive perspective to managers for policy making, its criteria does not include overall equipment effectiveness (OEE), perhaps since OEE is mostly used in one of the policies, i.e. total productive maintenance (TPM). In this article, the traditional DMG has been modified, in which the range of OEE has been estimated and replaced by one of the grid's criteria. A case study has been conducted in one of the steel manufacturing companies of Iran and data has been obtained and analyzed from 30 equipments of the company. The major finding of this investigation is that although OEE is an indicator of TPM, its different values might suggest different policies in addition to TPM.
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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 | 0.000 |
| 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 | 0.000 |
| 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