Statistical Methods for Process Understanding and Improvement

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My research develops innovative statistical methods for process improvement and understanding. More specifically, I propose to continue my work in the following three areas among others: Statistical Engineering, Measurement System Assessment, and Process Monitoring and Understanding. The results of my research which tackle real world problems have the potential to greatly influence industrial practice. Specific goals include improving the definition and widening the application of Statistical Engineering and developing a field failure prediction methodology applicable at Research in Motion, based on data from a variety of sources, including manufacturing, final test, warrant and customer usage. In addition, I plan to develop new and better plans and analyses for the assessment and comparison of measurement systems. Here a specific focus will be on relaxing questionable assumptions and tackling non-standard measurement characteristics, such as, counts, bounded data and multivariate data. A third research theme focuses on developing models and methods for monitoring processes when the output characteristics are not directly comparable and to better understand their performance. One specific project involves developing a method for monitoring the surgical learning curve. I also propose to re-examine the fundamental concepts and terminology of Statistical Process Control (SPC). The current environment has little coherence and many contradictions that result in confusion. Developing a consistent and concise set of definitions will improve SPC understanding and hopefully successful usage.