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Developing a confidence interval-based fuzzy testing method using the small bias estimator of the six sigma quality
Kuen-Suan Chen1,2,3, Kuei-Kuei Lai4, Chun-Min Yu5
1Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung, 411030, Taiwan, R.O.C.
Scientific Reports
|March 28, 2025
Summary
This study introduces a new Six Sigma quality index estimator with reduced bias and variance. This method improves accuracy in industrial assessments, especially with small sample sizes, benefiting smart manufacturing.
Area of Science:
- Industrial Engineering
- Quality Management
- Statistical Process Control
Background:
- The Six Sigma quality index is vital for assessing process yield and capability.
- Small sample sizes often lead to large confidence intervals, causing sampling errors and inconsistent evaluations.
- Timeliness and cost constraints challenge accurate industrial decision-making.
Purpose of the Study:
- To propose a Six Sigma quality index estimator with minimized bias and variance.
- To introduce a confidence-interval-based fuzzy test for enhanced assessment accuracy.
- To address limitations of small sample sizes in industrial quality evaluation.
Main Methods:
- Development of a novel Six Sigma quality index estimator.
- Application of confidence intervals for improved estimation.
- Integration of a smaller bias estimator to reduce misjudgments.
- Introduction of a confidence-interval-based fuzzy test.
Main Results:
- The proposed estimator demonstrates reduced bias and variance compared to traditional methods.
- The confidence-interval-based fuzzy test effectively mitigates misjudgments from sampling errors.
- Improved accuracy in quality assessments, particularly with limited data.
Conclusions:
- The novel Six Sigma quality index estimator enhances assessment accuracy and reliability.
- The confidence-interval-based fuzzy test offers a robust solution for industrial decision-making.
- This approach supports smart manufacturing by improving process quality and product value.
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