Journal
QUALITY ENGINEERING
Volume -, Issue -, Pages -Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/08982112.2023.2202727
Keywords
yield analysis; testing capability; Monte Carlo simulation; outliers; robustness
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This article presents a robust yield test to investigate the performance of an industrial production process in the presence of outliers. A new robust estimator of S-pk is introduced to test the production yield for any normal distribution in the presence of various numbers of outliers. Moreover, a Monte Carlo simulation method is proposed for testing the production yield based on the yield index S-pk by normal data and how well it can be used for some non-normal data is discussed.
Testing the performance of a production process is a very serious and important topic in statistical quality control. This article presents a robust yield test to investigate the performance of an industrial production process in the presence of outliers. For this purpose, a new robust estimator of S-pk is introduced to test the production yield for any normal distribution in the presence of various numbers of outliers. Moreover, a Monte Carlo simulation method to estimate the decision-making components is proposed for testing the production yield based on the yield index S-pk by normal data. Meanwhile, this article discusses how well the proposed Monte Carlo method can be used for some non-normal data. Numerical computations of the simulation and real data analyses are provided to explain the proposed method.
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