Effect of Nonstandard Conditions on Functionality of Methods Used by the Six Sigma Methodology

Abstract

The methodology of six-sigma is a kind of quality management tool that utilizes statistical analysis to minimize process variability and defects. The main goal is to investigate the behavior of ANO-VA and regression-based prediction when working with nonstandard data in the DMAIC cycle of Six Sigma. Data from various statistical conditions were collected and analyzed using ANOVA and regression to determine their behavior in these nonstandard conditions. The findings of this research indicate that nonstandard conditions have a significant impact on the effectiveness of the methods employed in the six-sigma Methodology.