Since joining Boeing in 1979 he has worked in many projects including: Applications of Residue Number Systems to Electronic and Photonic Computing; Markov Reliability Models for Analysis of Fault Tolerant Systems; Fault Tree Reliability Models for Multiphased Systems; Statistical Models for Cargo-Passenger Loads; Pilot workload models. Some areas of more recent interest include Probabilistic Networks, Statistical Tolerancing, and Data Mining. Statistics for Boeing in the 21st century; What are the statistics needed to help Boeing move successfully into the future? There are many tools in our old set of tricks: regression, design of experiments, quality control, reliability, and so on. There are some relatively recent ones, e.g., Pattern Theory, Markov Chain Monte Carlo, Functional Data Analysis, Spatial Statistics. There are even some that have been more commonly associated with other fields such as Data Mining and Neural Nets in computer science, and Signal Processing in electrical engineering. Using some ongoing and upcoming projects I will show how statistical methods, the old and the new, are being applied to solve Boeing problems.
This presentation was made earlier this year to a large group of Boeing engineers, statisticians, and technical and business managers. Though not exhaustive, it tried to showcase the variety of problems that require the expertise of a statistician. It also tried to show that statistics is not a static discipline: present industry problems require the application and development of new statistical methods and ideas.