Waste reduction of polypropylene bag manufacturing process using Six Sigma DMAIC approach: A case study
Bibliographic record
Abstract
In the current study, minimization of waste in terms of sack rejection at a polypropylene bag manufacturing process is achieved. The Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) approach is adopted which results in 50% waste reduction and a considerable cost saving. The sack rejection is brought to 1.20% from the previous average waste of 2.80% using DMAIC. It is found that this high rejection rate of 2.80% is due to the low fabric strength obtained at the weaving section, which in turn occurred due to the lower tape tenacity values obtained from the extrusion section. Hence, experimental design is conducted at the extrusion department and it is found that the two interacting factors are playing a significant contribution to the process variation and hence result in lower tape-tenacity (i.e., less than 6 g/denier). The two interacting factors included the “water bath temperature” and “line speed” of the extrusion process, with a p-value less than 5%. By further analysis, the optimal level of these significant factors is found. They are 300 m/min for “line speed” and 40⁰C for “water bath temperature. At these settings, the extrusion process produces optimal tape-tenacity results (i.e., at least 6 g/denier), which ultimately results in minimum waste in terms of sack rejection waste. The objective of the study includes finding the significant factors contributing to the process variation. Also controlling those factors to the optimal levels to achieve minimum wastage and considerable cost saving. The methodology and findings of the present study can be generalized to the polypropylene bag manufacturing plants and the process efficiency can be enhanced.
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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) | 0.001 | 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) | 0.001 | 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".