Process Capability and Six Sigma Methodology Including Fuzzy and Lean Approaches

Abstract

Process capability analysis (PCA) and Six Sigma methodology occupy important places in quality and process improvement initiatives. As a fundamental technique in any production, quality and process improvement efforts, PCA is used to improve processes, products or services to achieve higher levels of customer satisfaction. In order to measure process capability numerically, process capability indices (PCIs) have been developed. Six Sigma is widely recognized as a systematic methodology that employs statistical and nonstatistical tools and techniques for continuous quality and process improvement and for managing operational excellence because it challenges to maximize an organization’s return on investment (ROI) through the elimination of nonconforming units or mistakes in the processes (Antony et al., 2005). The application of Six Sigma methodology provides reduction in variance and augmentation in the process capability, which is defined as the proportion of actual process spread to the allowable process spread that is measured by six process standard deviation units. Similar to Six Sigma methodology, in a process capability study, the number of standard deviations between the process mean and the nearest specification limits is given in sigma units. The sigma quality level of a process can be used to express its capability that means how well it performs with respect to specifications. After Zadeh (1965) introduced the Fuzzy Logic (FL) to the scientific world, this new phenomenon rapidly became an essential systematic used in nearly every field of science. Due to its capability of data processing using partial set membership functions, an enormous literature about FL is developed with full of its applications. In addition, the ability of donating intermediate values between the expressions mathematically turns FL into a strong device for impersonating the ambiguous and uncertain linguistic knowledge (Ross, 2004). But although studies about FL are extremely wide, its application to quality control and especially to PCA is relatively narrow. The aim of this chapter is to carry out a literature review of PCA, fuzzy PCA, PCIs, to make comparisons between PCIs, to introduce ppm and Taguchi Loss Function, to discuss the effects of estimation on PCIs as well as to provide general discussion about sample size determination for estimating PCIs. Another objective of this chapter is to provide the investigation of the relationship between Process Capability and Six Sigma along with the examination of Six Sigma methodology, and a relatively new approach called Lean Six Sigma methodology, and to identify the key factors that influence the success of Six Sigma project implementation for improving overall management process.