Enhancing lean supply chain through traffic light quality management system
Bibliographic record
Abstract
Lean is a continuous journey to grow and excel the company.Any company want to develop and cope with the world pace must adopt lean.However, in most of the organizations the management culture or people's mentality is not so good to embrace change.They have predestined mind set where no change is normally allowed.Lean is a cooperative way of working that involves all departments and all personnel to work together in a team for the betterment of the entire company.Without providing fixed solution of any problem it suggests the best way that people willingly accept to do.Lean normally deals with highest quality, shorter lead time and lowest cost.In Bangladesh, most of the garment manufacturing companies are experiencing a massive quality problem.We describe a case where traffic light, a tool of lean quality system was adopted to a garment manufacturing company in Bangladesh.We also provide the charts to contrast the before and after scenario in detail, in order to illustrate the company benefits.After the traffic light system being implemented, the quality status was improved, production capacity was increased; significant days were saved that enhanced the lead time and thus strengthen the supply chain.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".