Production Management Model to Reduce the Percentage of Defectives by Applying Lean Manufacturing Tools in a Textile Sector: A case study
Bibliographic record
Abstract
This research project focused on addressing the high percentage of defectives in a Small and Medium-Sized Enterprise (SME) in Peru's textile sector.Production defects had impacted the overall profitability.To mitigate these issues, three key Lean Manufacturing tools were implemented: 5S, SMED (Single-Minute Exchange of Dies), and Standardized Work.The main goal of this project was to decrease the rate of defective products, improving production efficiency while upholding high-quality standards.The outcomes achieved following the implementation of the three tools were extremely positive.The 5S methodology showed significant improvements, as evidenced by the final evaluation score of 72,06%, surpassing the initial score of 32,35%.The SMED tool achieved a notable reduction in production process time, decreasing it to 148,26 minutes.Lastly, the application of Standardized Work increased efficiency from 96,69% to 97,69% and contributed to reducing the percentage of defective products from 2,65% to 1,64%.Additionally, an economic flow analysis with improvements was conducted, requiring an investment of $8 705,97.The economic results included a Net Present Value (NPV) of $11 924,87, an Internal Rate of Return (IRR) of 48,36%, and a Benefit-Cost Ratio (B/C) of 2,37.It was also concluded that the invested amount was recovered in 3,74 months.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".