Quality improvement through export item rejection reduction using the implementation of statistical quality control (SQC) tools: a case study
Bibliographic record
Abstract
Garment sector is one of the industrial sectors in Ethiopia. In this sector the final products are always with defect(s) which reduces attraction from customers and economic benefit from the business. With this intention, quality enhancement of shirt products through defect(s) rejection reduction using SQC tools was a vital task of this research. The study applied Pareto Analysis and Cause-and-effect diagrams for detailed examination of top defects. From the Pareto-analysis six top defect types; cuff assembly seam slip out, sleeve hemming, button slip out, side seam puckering, button missed, and placket seam out have been identified. These defects contributed 81.68% of all defects happening in the case company. Then root-cause analyses for these top defect types have been done and solutions have been suggested to overcome causes to reduce rejected shirts. Finally, the suggested solutions have been practically implemented through the organized implementation team from different departments of the case company including the researcher. This has given remarkable results of almost 67.3%, 2222 shirts, of export rejected shirts have been saved. These saved shirts have been exported additionally to the international market in line with the defect free products of that month and increased the income of the case company by 444,400 ETB to 555,500 ETB per month.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".