The role of statistics in Lean Six Sigma. A presentation to the American Statistical Association (Colorado/Wyoming) April 2011.

1. Statistics and Lean Six Sigma:
A Perfect Combination
ASA Colorado/Wyoming Spring Meeting
April 15, 2011
Presented By
Scott Leek
Senior Member, ASQ
Managing Director & MBB
Sigma Consulting Resources

2. Outline
 Background
– Recent History of Quality & Statistics
– What is Six Sigma?
– What is Lean?
– Merging Of Lean And Six Sigma
 Statistics Role In Lean Six Sigma
 The Statistician’s Role In Lean Six Sigma

3. Recent History of Quality & Statistics
1915

4. Recent History of Quality & Statistics
Birth of
Quality Control
& Quality
Assurance
1915 1940
• Bell Labs nurtures
QC/QA methods
• Shewhart develops
the Control Chart
• Dodge develops
Acceptance Sampling
• Deming, Grant, et al
also working at the
Labs

5. Recent History of Quality & Statistics
Birth of
Quality Control
& Quality
Assurance The War Years
1915 1940 1945
• Bell Labs nurtures • Statistical Quality
QC/QA methods Control embraced by
the War Production
• Shewhart develops Board
the Control Chart
• SQC wildly
• Dodge develops
successful improving
Acceptance Sampling
war production
quality and
• Deming, Grant, et al
also working at the productivity
Labs

6. Recent History of Quality & Statistics
Birth of
Quality Control
& Quality The Maturation
Assurance The War Years of Quality
1915 1940 1945 1980
• Bell Labs nurtures • Statistical Quality • In 1946 American
QC/QA methods Control embraced by Society for Quality
the War Production Control (ASQC) is
• Shewhart develops Board founded, now (ASQ)
the Control Chart
• Statistical methods
• SQC wildly continue to be
• Dodge develops
successful improving developed (Box,
Acceptance Sampling
war production Hunter, Shainin, et al)
quality and
• Deming, Grant, et al • Hit or miss process
also working at the productivity
improvement
Labs

7. Recent History of Quality & Statistics
Birth of The
Quality Control Renaissance of
& Quality The Maturation Process
Assurance The War Years of Quality Improvement
1915 1940 1945 1980 Present
• Bell Labs nurtures • Statistical Quality • In 1946 American • In 1980 Deming and
QC/QA methods Control embraced by Society for Quality Crosby start to get
the War Production Control (ASQC) is C-Suite attention
• Shewhart develops Board founded, now (ASQ)
the Control Chart • Formal Total Quality
• Statistical methods Management (TQM)
• SQC wildly continue to be
• Dodge develops Programs become
successful improving developed (Box, wide-spread
Acceptance Sampling
war production Hunter, Shainin, et al)
quality and
• Deming, Grant, et al • Hit or miss process • Motorola develops the
also working at the productivity
improvement first Six Sigma
Labs program in 1986

8. What Is Six Sigma?
 Originated at Motorola in the mid 1980’s
 Multiple Dimensions
- Enterprise process improvement initiative
- Metric and performance measure
- A set of improvement methods and tools
- A management philosophy of satisfying customer needs profitably
 Common characteristics of Six Sigma Initiatives
- Emphasis on strong management leadership and support
- Focus on achieving measurable results with financial returns
- Infrastructure of Champions, Master Black Belts, Black Belts, etc.
- Commitment to making decisions based on data, not opinion

9. What Is Lean?
 Based on the Toyota Production System
 Focus on eliminating the Seven Wastes (“muda”)
 Define and analyze the Value Stream to:
- Eliminate/minimize non-value added steps
- Plan rapid improvement events ( Kaizen Events)
- Create Flow (eliminate “mura”)
 A set of solutions

10. The Merging Of Lean And Six Sigma
 Throughout the 1990’s Lean and Six Sigma existed
almost completely independently
 As organizations used both methodologies, Lean and
Six Sigma become increasingly integrated
 Integration varies from organization-to-organization
but involves a consistent body of knowledge

11. Statistics Role In Lean Six Sigma
Quality improves
by removing
special causes
Defect Levels
Time
Adopted from Deming, W. Edwards, Out of the Crisis (MIT Center for Advanced Engineering Study, 1982, p. 323).

12. Statistics Role In Lean Six Sigma
Quality improves
by removing
special causes
Defect Levels
Continued
improvement is
expected
Time
Adopted from Deming, W. Edwards, Out of the Crisis (MIT Center for Advanced Engineering Study, 1982, p. 323).

13. Statistics Role In Lean Six Sigma
Quality improves
by removing
But the process
special causes
has stabilized
Defect Levels
Continued
improvement is
expected
Time
Adopted from Deming, W. Edwards, Out of the Crisis (MIT Center for Advanced Engineering Study, 1982, p. 323).

14. Statistics Role In Lean Six Sigma
Quality improves
by removing
But the process
special causes
has stabilized
Defect Levels
Statistical
Continued methods close
improvement is this gap
expected
Time
Adopted from Deming, W. Edwards, Out of the Crisis (MIT Center for Advanced Engineering Study, 1982, p. 323).

15. Statistics Role In Lean Six Sigma
If your objective is to harvest the fruit
Where do you start?

16. Statistics Role In Lean Six Sigma
Special Causes fixed
Ground Fruit with intuition and
common sense

17. Statistics Role In Lean Six Sigma
Special and Common
Low Hanging Fruit Causes fixed with basic
analytical tools
Special Causes fixed
Ground Fruit with intuition and
common sense

18. Statistics Role In Lean Six Sigma
Common Causes
reduced with advanced
Sweet Fruit
tools and Design for Six
Sigma (DFSS)
Special and Common
Low Hanging Fruit Causes fixed with basic
analytical tools
Special Causes fixed
Ground Fruit with intuition and
common sense

19. The Statistician’s Role In Lean Six Sigma
 Statisticians can and do serve in a variety of roles
but the most common is as a Master Black Belt
(MBB)
 The American Society for Quality (ASQ) defines the
role of a Master Black Belt in the ASQ Certified
Master Black Belt Body of Knowledge that includes:
I. Enterprise-wide Planning and Deployment
II. Cross-functional Competencies
III. Project Management
IV. Training Design and Delivery
V. Mentoring Responsibilities
VI. Advanced Measurement Methods and Tools

20. Summary
 Background
– Recent History of Quality & Statistics
– What is Six Sigma?
– What is Lean?
– Merging Of Lean And Six Sigma
 Statistics Role In Lean Six Sigma
 The Statistician’s Role In Lean Six Sigma

21. What Is Lean?
Seven Wastes
 Based on the Toyota Production System
• Defects
• Over-production
 Focus on eliminating the Seven Wastes (“muda”)
• Transportation
• Waiting
• Inventory
• Motion
• Over-processing
X

22. What Is Lean?
Lean Solutions
 Based on the Toyota Production System
• Poka Yoke (mistake proofing)
• Set-up time reduction (SMED)
 Focus on eliminating the Seven Wastes (“muda”)
• Kanban
 Define Cell Design
•
and analyze the Value Stream to:
• Level Production
- Eliminate/minimize non-value added steps
• Total Productive Maintenance
- Plan • Reducing Batch Size
rapid improvement events ( Kaizen Events)
- Create Pull Scheduling “mura”)
• Flow (eliminate
• Line Balancing
 A set • Uniform Loading
of solutions
• 5S (Sort, Set in order, Shine, Standardize, Sustain)
X

23. Statistics Role In Lean Six Sigma
Basic Analytical Tools
•
Flow Charts
•
Cause & Effect Diagrams
•
Time Series Plots & Control Charts
•
Capability Analysis
•
Graphical Plots
Special and Common
Low HangingBoxplots
- Fruit Causes fixed with basic
- Dotplots analytical tools
- Scatterplots
- Histograms Special Causes fixed
Ground Fruit X
with intuition and
common sense

24. Statistics Role In Lean Six Sigma
Advanced Tools
Y = f(X1, X2, X3, …, Xn)
Common Causes
reduced with advanced
• Measurement Systems Analysis
Sweet Fruit
tools and Design for Six
• Time Series Analysis Sigma (DFSS)
• Regression Analysis (multiple, logistic, non-linear)
• Hypothesis Testing Special and Common
Low Hanging Fruit
• General Linear Models (GLM) Causes fixed with basic
• Design of Experiments (DOE) analytical tools
• Response Surface Methodology (RSM)
• Monte Carlo Simulation Special Causes fixed
Ground• Fruit
Linear Programming with intuition and
• Advanced Statistical Process Control common sense
X