Statistical Engineering: Statistical Methods for Process Improvement and Decision Making

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The general goals of this research include improving efficiency, reducing costs and better decision making and allocation of resources in business, industrial and medical applications. More specifically, I propose to pursue the following three related research themes: estimating current process performance from streaming data, augmenting the design and analysis of experiments using baseline data and improving the assessment of measurement systems.*******There are many situations where we obtain data at regular time intervals and wish to estimate the current process performance. For example, we may run weekly customer satisfaction surveys. Here we expect satisfaction to change slowly over time as we address customer concerns, develop new products, react to actions by our competitors, etc. This research aims to develop robust methods for assessing the current process condition that takes into account sampling variation, possible changes over time and the effect of covariates. New methods that combine data in a sensible way can greatly improve decision making. There are many related application areas, such as monitoring hospital or medical lab performance.*******Experiments, where we deliberately change a process to assess the effect of the change, are widely used in virtually all scientific inquiry. In this research I focus specifically on the use of experiments in improvement projects for existing processes. In this context, we usually have a lot of available observational “baseline” data. The goals of this project are to show how to best use this available process data to improve the planning of experiments and also to quantify the gains in efficiency resulting from incorporating the baseline data in the analysis. Since experiments are typically expensive and disruptive to run, such improvements would save time and money.*******Measurement systems are essential in virtually all areas of scientific inquiry. Without precise and well calibrated measurement systems process understanding and/or improvement are difficult to achieve. This research focuses on improving the assessment of binary measurement systems that are essential as inspection systems in industry and as diagnostic tests in medicine. Efficient and effective measurement system assessments and comparisons would enhance our ability to make informed decisions about which measurement systems to use and how to improve existing measurement systems. **