Six Sigma Measurement Systems
Candidates seeking Six Sigma Black Belt certification, quality professionals, engineers, production managers, frontline supervisors, and all individuals charged with responsibility for improving quality and processes at the organizational or departmental level, including process owners and champions
Proficiency at the Green Belt level with Six Sigma measurement systems as scoped in the ASQ – Six Sigma Green Belt Body of Knowledge (BOK)
Six Sigma measurement systems are vital to improving an organization’s processes. Measurement systems encompass the conditions, devices, and the human element of measurement, which together must produce correct measurements and comply with appropriate standards. Measurement error, or measurement variability, is a problem whose components must be thoroughly understood and kept in check to maintain the effectiveness of any measurement system. Measurement variability contributes to the overall variability in the process and it is important to understand its sources and minimize it. Black Belts can calculate correlation, bias, linearity, stability, reproducibility, and repeatability to analyze and further improve measurement systems.
This course examines how to analyze a measurement system to help it produce correct measurements and minimize its proportion of variability in the overall variability. It introduces key elements of metrology and international systems of measurement, explores the many sources of measurement error, and surveys a broad range of items that can be measured in various functional areas of the enterprise. The course also presents some of the considerations influencing the use of measurement systems in service industries. This course is aligned with the ASQ Certified Six Sigma Black Belt certification exam and is designed to assist learners as part of their exam preparation. It builds on foundational knowledge that is taught in SkillSoft’s ASQ-aligned Green Belt curriculum.
Measurement System Analysis
Measurement Systems in the Enterprise