Application of Lean Manufacturing for Green Production Textile Manufacturing: synthesis of literatures
Bibliographic record
Abstract
The climate change agenda wants stakeholders to participate in systemic transformation.Companies need to make products that don't harm the environment and to contribute employee's wellbeing.The process and waste management are considered to manage the manufacturing performance.This research is to make textile manufacturing better for the environment.As the first step, the Five Whys are used to figure out what the problems are.Then, we use Function Analysis Technical System method to understand how different functions are connected.After that, we use mathematic tools and the Five S to study the results.Finally, we pick research paper from 2002 to 2022 to learn more about the topic.Lean manufacturing in textile is a way of making products more efficiently and reducing waste.It can help businesses improve operational performance as delivery and quality.Competitiveness is the main results.In roughly year, the on-time delivery rate increased from 10% to 65%.In addition, research on the effects and impacts of Lean manufacturing reveals that it enhances functional execution.Environmental performance increases with Lean tools for green production.The work environment will increase from 40% to 80%.In Asia, these methods are used a lot in the textile industry.The textile manufacturing unit generates two types of industrial waste: unusable waste and recoverable waste.Faced the challenges of climate change, the first strategy aims to adapt or adjust production processes to boost competitiveness.The second strategy aims at mitigation through sustainable waste management. .
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".