Robust yield test for a normal production process

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.

Automated summary

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.