Programs for systematically reducing manufacturing variation and improving quality (often called six-sigma programs) are now firmly established in industry. However, the vast majority of six-sigma analysis tools were designed decades ago for use with limited amounts of data with relatively simple structure. This research will develop a knowledge discovery methodology for six-sigma variation reduction that is designed for the high-dimensional data structures found in modern manufacturing operations. In contrast to methods in which one constructs prior models for known variation sources and then looks for those specific premodeled patterns in the data, the goal of this research is to blindly discover the nature of the variation patterns and their sources, based solely on a sample of data, with no premodeling. Interactive graphical visualizations of each identified pattern will enable users to visualize the root causes of variation. To accomplish this, this research will develop a paradigm for representing variation patterns that encompasses linear and nonlinear phenomena in a variety of data structures. The research will also develop algorithms for blindly identifying the patterns with as much accuracy, robustness, and automation as possible.
If successful, this research will modernize variation reduction methods to keep pace with advances in measurement and information technology. Although the algorithms will necessarily be complex, the automation, coupled with methods that will be developed for visualizing the results, will create tools that are easy-to-use and widely applicable. These characteristics, which are traditional six-sigma hallmarks, will facilitate broad dissemination and adoption of the methodology and enable its use by multidisciplinary teams of collaborators (e.g., operators, engineers, statisticians, and managers with varying backgrounds). Dissemination will be further enhanced by integrating the results into six-sigma and data mining courses offered to undergraduate and Ph.D. students and to an ethnically and technically diverse spectrum of engineers and managers in professional masters' courses.
