Six Sigma

framework · management · organizing-schema

Reduce defects to 3.4 per million through statistical process control and DMAIC.

Six Sigma is a data-driven process-improvement methodology developed at Motorola by Bill Smith in 1986 and made famous by Jack Welch's intensive deployment at General Electric beginning in 1995. The name refers to a process operating at six standard deviations from the nearest specification limit, corresponding to 3.4 defects per million opportunities in long-run performance. The methodology combines statistical process control with project-based deployment via the DMAIC cycle (Define, Measure, Analyze, Improve, Control) for existing-process improvement and DMADV (Define, Measure, Analyze, Design, Verify) for new-process design, executed by trained practitioners certified at progressive belt levels (Yellow, Green, Black, Master Black). Lean Six Sigma combines the toolkit with Lean Manufacturing's waste-elimination focus.

Originators

Bill Smith (Motorola, originator); Jack Welch and GE (popularization); Mikel Harry (early Motorola development) high

Year / Decade

1986 (Motorola origination); 1995 (GE adoption under Welch) high

Primary sources

Pyzdek, T. & Keller, P. (2003, multiple editions). The Six Sigma Handbook, Harry, M. & Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy, Motorola University historical materials high

Core components

DMAIC (Define, Measure, Analyze, Improve, Control)
DMADV / DFSS (Design for Six Sigma)
3.4 defects per million target
Statistical process control
Belt certification (Yellow, Green, Black, Master Black, Champion)
Project portfolio management
Lean Six Sigma combination

Primary use case

Manufacturing and service-process quality improvement; structured operational-excellence programs at large enterprises; supplier quality development; foundation of corporate process-improvement training.

Common criticisms

Statistical rigor can stifle the creativity needed for breakthrough innovation (the Christensen-Welch debate)
certification industry incentivizes credentialing over capability development
less effective in highly variable or non-repeating processes (R&D, creative work)
empirical evidence on financial returns to Six Sigma is mixed and selection-biased toward voluntary adopters
tension with agile and lean-startup approaches
risk of metric optimization that misses customer outcomes
3M's post-Six-Sigma innovation decline cited as cautionary example.

Lineage

Parent of: DMAIC, DMADV
Siblings: Toyota Production System, Total Quality Management, Theory of Constraints