Webinar: Quality 4.0 Data Science – The Variance Components Problem скачать в хорошем качестве

Webinar: Quality 4.0 Data Science – The Variance Components Problem

Published on 9/09/2024 Presented on 8/21/2024 Abstract The main problem of Data Science is the extraction of meaningful insights from pattern discovery in massively large data sets for business decision-making. The decision-making problem for quality professionals, however, is one of process stability and control and of product or service performance optimization. Industry 4.0 massively horizontally and vertically integrated smart cyber-physical processes expand the stability, control, and optimization problem. This webinar examines the problem of massively horizontally and vertically integrated smart cyber-physical process stability and control and smart products or service performance optimization from a variance components viewpoint. The variance components insight provided will provide the foundation for attendees to consider the respective massively horizontally and vertically integrated stability, control, and optimization problems to be encountered in their respective processes, as they transition to globally distributed smart cyber-physical processes. Bio T. Steven Cotter is a Master Lecturer with the Engineering Management and Systems Engineering department at Old Dominion University. He earned a Ph.D. in Engineering Management and Systems Engineering from Old Dominion University, a Master of Science in Engineering Management with a concentration in quality/reliability engineering from the Mechanical and Industrial Engineering Department, University of Massachusetts at Amherst, a Master of Business Administration with a Finance concentration and a Bachelor of Science both from the University of South Carolina, and a diploma in Electronic Technology from Graff Area Vocational and Technical School (now Ozarks Technical Community College). He is Certified in Quality and Reliability by the American Society for Quality. He has over 40 years of experience in engineering management and in automated chemicals manufacturing, computer manufacturing, and defense electronics. He has supported projects for the startup of production facilities and a research and development center. He has managed Lean Six Sigma projects, automated quality information systems, and automated measurement systems. His research initiatives are in digital engineering informatics, cyber-physical-socio quality-reliability systems, and computational systems statistical engineering.