Strategic Integration of Lean Manufacturing Tools for Quality Enhancement and Environmental Improvement in a Textile SME
Bibliographic record
Abstract
This research project addressed the high defect rate in a Peruvian textile SME through the implementation of three Lean Manufacturing tools-5S, SMED (Single-Minute Exchange of Dies), and Standardized Work-alongside an environmental assessment using the Leopold Matrix.The main objective was to reduce production defects and improve operational efficiency without negatively impacting the environment.The 5S implementation led to a significant improvement in workplace organization, increasing the evaluation score from 32.35% to 72.06%.SMED reduced setup time to 148.26 minutes, while Standardized Work improved efficiency from 96.69% to 97.69% and lowered the defect rate from 2.65% to 1.64%.Environmentally, the net impact value improved significantly, from an initial score of -258 to -14, reflecting a substantial reduction in environmental impact.These results demonstrate that operational performance and environmental sustainability can be jointly enhanced in resource-constrained manufacturing settings.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".