Statistical Methods for Quality and Productivity Improvement

The automatic process controlexperimental designrobust designstatistical process control (SPC)Taguchi method first section of this chapter introduces statistical process control SPC and robust design RD, two important statistical methodologies for quality and productivity improvement. Section 10.1 describes in-depth SPC theory and tools for monitoring independent and autocorrelated data with a single quality characteristic. The relationship between SPC methods and automatic process control methods is discussed and differences in their philosophies, techniques, efficiencies, and design are contrasted. SPC methods for monitoring multivariate quality characteristics are also briefly reviewed. Section 10.2 considers univariate RD, with emphasis on experimental design, performance measures and modeling of the latter. Combined and product arrays are featured and performance measures examined, include signal-to-noise ratios SNR, PerMIAs, process response, process variance and desirability functions. Of central importance is the decomposition of the expected value of squared-error loss into variance and off-target components which sometimes allows the dimensionality of the optimization problem to be reduced. Section 10.3 deals with multivariate RD and demonstrates that the objective function for the multiple characteristic case is typically formed by additive or multiplicative combination of the univariate objective functions. Some alternative objective functions are examined as well as strategies for solving the optimization problem. Section 10.4 defines dynamic RD and summarizes related publications in the statistics literature, including some very recent entries. Section 10.5 lists RD case studies originating from applications in manufacturing, reliability and tolerance design.