How To Use Advanced Analytics To Revitalize Six Sigma

By Allison Buenemann

Lean and Six Sigma continuous improvement methodologies have been a foundation in process manufacturing industries for decades. In more recent times, industry 4.0 and data science innovations—such as machine learning, big data, and cloud computing—have improved analytic capabilities beyond historical limits, enabling the evolution of Lean and Six Sigma efforts. However, the foundational Six Sigma methodology of define, measure, analyze, improve, control (DMAIC) has remained the same. 

DMAIC is typically used by certified Six Sigma Black Belts to solve well-defined process optimization problems. The challenge is DMAIC analytics can result in organizational and data silos based on where the Black Belts are deployed. A more modern approach is enabled with Seeq which democratizes data and analytics across organizations, empowering an army of DMAIC practitioners to use advanced statistical concepts to solve manufacturing problems—with no Black Belt required.