Analysis and Defect Improvement Using FTA, FMEA, and MLR Through DMAIC Phase: Case Study in Mixing Process Tire Manufacturing Industry
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 line with the increasingly fierce industry competition, all companies strive to make continuous improvements to increase added value and reduce waste which will impact the company's ability to maintain its existence in the future. One of the problems found in the tire manufacturing industry is the quality problem of the occurrence of defects in the mixing process which is dominated by the viscosity out standard on the compound steel breaker. In this study, analysis and improvement of the defect problem were carried out using Fault Tree Analysis (FTA), Failure Mode and Effect Analysis (FMEA), and Multiple Linear Regression (MLR) to test the correlation between the root causes found to the main problem. Based on the results of the analysis found thirteen root causes where the factor of variation in material viscosity and the suitability of determining the design process has the largest Risk Priority Number (RPN) value and has a strong correlation to defects that occur based on hypothesis testing. Furthermore, improvements are made using the DMAIC method on all factors that affect the occurrence of defects. As the result, the improvement can be effective in reducing the defect to 34.5% and achieve the expected target.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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