Application of Demerit Chart and Fuzzy Demerit Chart for Monitoring Paper Production
Bibliographic record
Abstract
Statistical Process Control (SPC) is an important method in quality control to monitor and improve production processes. Control charts are one of the SPC tools that are often used to quickly detect the causes of process variation so that improvements can be made before more nonconforming products are produced. The u chart is commonly used to monitor the number of defects in a production unit. However, this control chart has limitations in handling variations in defect severity, so demerit and fuzzy demerit control charts were developed to assign weights to defects based on their severity. Demerit and fuzzy demerit control charts have been applied in various production cases, but the study of the application of demerit and fuzzy demerit control charts in the industrial field, especially the paper industry, has never been done. The purpose of this study is to apply demerit and fuzzy demerit control charts to monitor and evaluate the quality of the paper production process at PT. Bosowa Media Grafika (Tribun Timur). The data used in this study are secondary data obtained from research conducted by Ilham (2012). The results obtained that the demerit chart and the fuzzy demerit chart show that the paper production process at PT Bosowa Media Grafika (Tribun Timur) is still in a stable condition (incontrol) in each observation. This shows that demerit and fuzzy demerit control charts have the same performance in monitoring the paper production process.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".