Lean Six Sigma combines Lean's waste-elimination focus with Six Sigma's statistical defect-reduction methods to create a powerful continuous improvement methodology. At its core, Six Sigma targets 3.4 defects per million opportunities (99.99966% accuracy), while Lean eliminates non-value-adding activities through systematic process analysis. The DMAIC framework (Define, Measure, Analyze, Improve, Control) guides improvement of existing processes, while DMADV (Define, Measure, Analyze, Design, Verify) creates new processes optimized from inception. Understanding the distinction between common cause variation (inherent to the process) and special cause variation (assignable to specific factors) enables practitioners to apply the right tools—from statistical process control to root cause analysis—creating sustainable improvements that balance speed, quality, and cost.
What This Cheat Sheet Covers
This topic spans 18 focused tables and 97 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: DMAIC Phases for Process Improvement
DMAIC is the foundational five-phase roadmap for improving existing processes in Six Sigma. Each phase builds on the previous one, moving from problem identification through sustained control, with specific deliverables and decision gates that prevent teams from jumping to solutions before understanding root causes.
| Phase | Example | Description |
|---|---|---|
Project CharterProblem StatementSIPOC Diagram | • Establishes project scope, business case, and boundaries • identifies the problem, goal, timeline, and stakeholders before any analysis begins | |
Data Collection PlanBaseline Cpk = 0.85MSA with Gage R&R < 10% | • Quantifies current process performance using validated measurement systems • establishes baseline metrics that define how bad the problem actually is | |
Fishbone Diagram5 Whys AnalysisHypothesis Test p < 0.05 | • Identifies root causes of defects through statistical analysis and process mapping • separates vital few causes from trivial many using data-driven evidence |