ANALISIS PENYEBAB PRODUK NOT GOOD PACKING COUMPOUND DENGAN METODE SIX SIGMA DMAIC DI PT. A&A
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
Penelitian dilakukan untuk mengetahui faktor penyebab terjadinya produk not good pada Packing Compound. Penelitian dilakukan di departemen Quality Control PT. A&A. Analisis dilakukan menggunakan metode Six Sigma DMAIC. Diagram pareto memperlihatkan bahwa terdapat empat jenis not good yang perlu perbaikan segera yaitu not good sobek, kotor, short mold dan visual. Analisis penyebab dilakukan menggunakan Failure Mode and Effect Analysis (FMEA). Hasil analisis membuktikan bahwa faktor manusia, material, mesin dan metode menjadi faktor penyebab terjadinya produk not good. Oleh karena itu, perusahaan perlu menambah jumlah tenaga kerja ahli, meaksanakan SOP dengan baik dan melakukan pengecekan dan perawatan mesin secara terjadwal. Kata kunci: Kualitas, Six Sigma, DMAIC
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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