A structured, data-driven program covering the DMAIC framework, statistical process control, process capability, and root cause analysis — with AI-assisted case studies for faster, deeper learning.
Is This Course For You?
This program is designed for professionals who want to use data-driven problem-solving to improve quality, reduce variation, and make better process decisions.
What You Will Achieve
By the end of this program, participants will be able to structure and execute improvement projects using the DMAIC framework with statistical rigor.
Apply the full DMAIC framework to define, measure, analyze, improve, and control any process improvement project
Collect, analyze, and interpret process data to make evidence-based decisions rather than relying on assumptions
Construct and interpret control charts to distinguish between common cause and special cause variation
Calculate and interpret process capability indices (Cp, Cpk) to assess how well a process meets specification
Apply root cause analysis tools — Pareto charts, fishbone diagrams, scatter diagrams — to identify and verify root causes
Conduct a Failure Mode and Effects Analysis (FMEA) to identify and prioritize process risks before they occur
Course Curriculum
8 structured modules delivered over 3 days — combining DMAIC theory, statistical tools, and practical case studies.
Course Details
Comprehensive participant workbook with notes, formulas, exercises, and reference material
Real-world case studies applying DMAIC and statistical tools across manufacturing and service industries
Selected exercises use AI tools to speed calculations, support chart preparation, and deepen interpretation
This program covers core topics included in the ASQ Certified Six Sigma Green Belt (CSSGB) Body of Knowledge and supports preparation for the CSSGB examination. The ASQ examination and certification are arranged separately through ASQ and are not included in the course fee. Visit asq.org for current examination details and eligibility requirements.
Selected case studies in this program incorporate AI-assisted analytics to speed calculations, support chart preparation, and improve interpretation of results. This means participants focus on method selection, root cause thinking, and business interpretation — not manual number-crunching. Participants remain responsible for validating outputs and applying professional judgment throughout. This is Six Sigma rigor, enhanced by AI.