Pengendalian Kualitas Part Trim Rear Quarter Right APV Arena dengan Menggunakan Metode Six Sigma di PT. Suzuki Indomobil Motor
Bibliographic record
Abstract
Quality is an important aspect in enhancing the competitiveness of the product. Quality's role is to give satisfaction to the customer and be able to compete with similar products. During of the production process there is a chance that the products are not produced in established standards. Products that not suitable with the specifications is a defect that would cause harm to the company. Trim Rear Quarter Right is a component of the APV Arena car which is each production of Trim Rear Quarter Right there are at least one or more defective products. This of course would lead to losses for the company. The method used in this case study is the six sigma method because this method has been proven effective. Defect in Trim Rear Quarter Right caused by five factors that is negligence of the operator in set up the machine, the age of the machine , the quality of the materials, the application of the method is not maximized and noisy environments. The set up process of injection molding machine is the most influential in causing the defect product with RPN (Risk Priority Number) value is 256.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".