Speaker Profile
About
Ryan Bays is a Lead Associate with Booz Allen Hamilton’s Corporate Quality Office, specializing in business analytics and business process improvement. He graduated magna cum laude with a Bachelor of Science degree in Political Science from Kennesaw State University and has a Graduate Certificate in Business Analytics from the Kelley School of Business at Indiana University. He is also a certified Project Management Professional and a Lean Six Sigma Black Belt. Mr. Bays brings more than 20 years experience solving complex business problems in multiple industries including government, public health, automotive, and banking. He has presented topics at Capability Counts conferences since 2016.
SPEAKER PRESENTATION
Conference Track: Methodology Integration
Measurement activities are the cornerstone to successful implementation of CMMI. Define-Measure-Analyze-Improve-Control (DMAIC) is the cornerstone of statistical analysis/Lean Six Sigma. How can we combine these two concepts to contribute to project success? Via the Measurement Analyst! Using the DMAIC framework, we will demonstrate how using DMAIC for daily measurement activities allows a Measurement Analyst to effectively provide just in time training, develop job aids, facilitate planned group activities, and conduct data analysis. The presentation will share detailed information about the how the DMAIC framework is used to support measurement activities at every level of CMMI maturity.
Conference Track: Performance Management
Taking a Define-Measure-Analyze-Improve-Control (DMAIC) approach to performing causal analysis enables a structured, problem solving framework in which to address issues and analyze improvements as organizations move toward CMMI High Maturity. Six Sigma’s DMAIC facilitates a systematic method for using data to evaluate performance improvements. This presentation will show how the DMAIC approach informs the causal analysis process when tackling both a “simple” and a more complex challenge. We will also be exploring advanced statistical techniques such as hypothesis tests, to measure the impact of process changes and subsequent performance gains.