Statistical Thinking:
Past, Present and Future 2008 Fall Technical Conference
ASQ Statistics Division Invited Session
Doug Hlavacek, Ecolab
STAT Past Chair
October 10, 2008
Agenda
• Introduction
– Doug Hlavacek, Ecolab
• Statistical Thinking - Past
– Roger Hoerl, GE CRD
• Statistical Thinking - Present
– Gordon Clark, Ohio State University
• Statistical Thinking - Transition
– Robert Mitchell, 3M LSSQ
• Statistical Thinking - One Future
– Roger Hoerl, GE CRD
• Panel Discussion
Statistical Thinking
Statistical Thinking is a philosophy of learning and
action based on the following fundamental
principles:
¾ All work occurs in a system of interconnected
processes,
¾ Variation exists in all processes, and
¾ Understanding and reducing variation are keys to
success.
Glossary and Tables for Statistical Quality Control Quality Press, 1996
Statistical Thinking
• Emphasizes critical thinking
• Different from statistical tools...
Not number crunching
Questions
The Statistics Division first published the official definition of Statistical Thinking
in the 1996 edition of the Glossary & Tables for Statistical Quality Control.
Statistical Thinking is a philosophy of learning and action based on the
following fundamental principles:
• All work occurs in a system of interconnected processes,
• Variation exists in all processes, and
• Understanding and reducing variation are keys to success.
Three past chairs of the Statistics Division (Roger Hoerl, Gordon Clark, and Bob
Mitchell) will share their perspectives about the past, current, and future of
Statistical Thinking.
What question(s) about Statistical Thinking do you have for the panelists?
Statistical Thinking:
The Past
Roger Hoerl, GE Global Research
W. Edwards Deming
• To the best of my knowledge, never used the
term “Statistical Thinking”
• Taught that statistical concepts apply to
management, or anything else
– e.g., the “red bead” exercise
– Focused on understanding, not the formulas
• Gradually made the case that we were missing
something beyond statistical methods per se
– But didn’t articulate it well, in my opinion
• The statistical community continued to focus on
the math
Ron Snee
• More than anyone else, broadly popularized and
disseminated the concepts of statistical thinking
• Defined statistical thinking in 1986 as “thought
processes”, not formulas
• Clarified the distinction (synergy) between
statistical thinking and methods
• Later published introductory business statistics
text based on statistical thinking (2002)
– Statistical Thinking: Improving Business Performance
• The statistical community continued to focus on
the math
Heero Hacquebord
• Afrikaner student of Deming’s
• Taught public “Statistical Thinking” courses
beginning in 1987
– Was more articulate than Deming, in my opinion
• Emphasized managerial implications of the
concepts
– e.g., the hazards of “managing by the last data point”
• The statistical community continued to focus on
the math
Quote From Tom Pohlen, 3M
• Attended Hacquebord’s course in 1988
• “I went into the course thinking that I already knew
everything I needed to know about SPC. I came out of
the course with a whole new perspective on statistics,
looking upon SPC and other statistical applications more
as a way of thinking about processes so we can learn
how to improve them. I also found that I could never
again be satisfied with looking at numbers without
graphical analysis.”
• Pohlen clearly had a “Damascus road” experience
• The Statistical community continued to focus on the
math
Statistics Division Statistical Thinking
Tactical Planning Team
• Chartered at a Statistics Division long-term planning
meeting in 1994
– Developed “5 year plan”
• Published formal definition in 1996 Glossary and Tables
for Statistical Quality Control - a seminal event!
• Wrote a Special Publication on Statistical Thinking for
division members in 1996
• Wrote booklet: “Improving Performance Through
Statistical Thinking” (Quality Press, 2000)
• Organized several conference sessions to “get out the
word”
• The statistical community continued to focus on the math
Impact of These Efforts
• Statistical thinking became part of the
vocabulary of statistically-oriented quality
professionals
– Among this group, there is a realization of the
uniqueness of statistical thinking versus
statistical methods
• While not always recognized, statistical
thinking principles became a cornerstone
of major improvement initiatives, such as
TQM and Six Sigma
GMC1
Statistical Thinking:
Process Improvement Strategy
Gordon Clark
The Ohio State University
October 10, 2008
Illustration of Process
Improvement Strategy
• Understand the
Process
– Ricoh’s Numazu plant
produced raw
materials for paper
copier toner
• Resin
• Consistent quality &
volume
Collect data on key input,
process and output measures
Yield =
Actual Output
Theoretical Output
Why is Yield above 100%?
Analyze Process Stability
Special Cause?
• Mechanical problem was special cause
– Fixed
Evaluate Process Capability
• Investigated customer needs for batch output quantity
– 4300 kg ± 5 kg
Analyze Common-Cause Variation
Study Cause & Effect Relationships
• Extraction of 2nd phase volume
– Resin remained in tank after dividing phase
– Line B had less material than line A
– Changed dividing procedure
• Data showed no detectable difference between
batch sizes
• Implement change
– Variation in output quantity reduced but still
too large
Solvent Feed Ratio Potential Cause
• No relationship
should exist
• Ratio measurement
affected by time
solvent sat in tank
• Change implemented
• Output relationship with feed ratio disappeared
• Variation still too high
Weighing Process Potential Cause
• Found problems affecting weighing
process accuracy
– In-process (manual)
– Final (automatic)
• Problems corrected
• Change implemented
– Output variation met tolerance
Output Control Charts
ST or Hoerl-Snee Process
Improvement Strategy
•
References
–
–
–
–
Hoerl, R. W. and R. D. Snee (1995). Redesigning
the Introductory Statistics Course. Madison,
Wisconsin, University of Wisconsin, Center for
Quality and Productivity Improvement.
Britz, G. C., D. W. Emerling, et al. (2000). Improving
Performance Through Statistical Thinking.
Milwaukee, WI, ASQ Quality Press.
Hoerl, R. and R. D. Snee (2002). Statistical Thinking
- Improving Business Performance. Pacific Grove,
CA, Duxburry
Blog: http://www4.asq.org/blogs/statistics
Process Improvement
Strategy
Comparison with DMAIC Strategy
• Improvement occurs in iterative sequential
steps
– Enhanced PDCA approach to improvement
• Emphasis on removing special-cause
variation first
– Analysis of special cause variation differs
from common-cause variation
Current Scope of SQC
Douglas Montgomery (2005). Introduction to
Statistical Quality Control, Fifth Edition
– “Quality is inversely proportional to variability”
– “Quality improvement is the reduction of variability in
processes and products”
– “quality improvement … three major areas..statistical
process control, design of experiments, …
acceptance sampling.”
Observation
• Lacks an overall process improvement strategy
Statistical Process Control
Montgomery (2005)
–
“Statistical Process Control (SPC) is a powerful
collection of problem-solving tools useful in
achieving process stability and improving capability
through the reduction of variability”
Observations
• Lacks an overall process improvement strategy
• In practice, focus is on control charts
• More emphasis needed on reducing common-cause
variation
Statistical Process Improvement
• Upgrade to SQC and SPC
• Use Statistical Thinking
• Use Hoerl-Snee Process Improvement
Strategy
Statistical Thinking:
The Transition to Entitlement Quality
Robert Mitchell - 3M LSSQ
October 10, 2008
Quality Journey
• A typical example
Six Sigma
– Lean
– Innovation
– Human Sigma
-- Entitlement Quality
DMAIC
•
•
•
•
•
•
Project-by-Project Improvement
Eliminate defects (nonconformance)
Business Critical Y: Cost, Cash, Growth
Project length:
6-9 months
Tools focused
Metrics: Primary, Secondary, Counterbalance
Learnings:
– Internally-focused. Where is the customer?
– Lack of systems thinking... Sub-optimization
– Must focus on building process capability
Lean
•
•
•
•
•
Eliminate waste (8 forms of muda)
Eliminate non value-added activities
Improve flow, Reduce cycle-time
Tools focused... Not a philosophy, like TPS
Metrics: Yield, Time, Productivity, Inventory
Learnings:
– Cannot reliably improve flow unless process is stable
– Lack of knowledge of variation
– Cultural norms and behaviors (LMS) must be created
Commercialization
NPI Framework
Idea
Concept
Feasibility
Dev’t
Scale Up
Launch
PostLaunch
• Collect and translate fuzzy VOC
• Understand variation in markets and customer
segments
• DFSS tools to design and deliver value-added
products and services
Learnings:
– Commercialization is a business strategy
– Innovation is messy, not linear
– ST concepts drive robust product design
Process Approach
Process
Inputs
Suppliers
Outputs
Customers
A series of activities that converts inputs into outputs
The business should see the improvement ($),
the customer should “feel” the improvement
ST in All Improvement Initiatives
All work
is a process
Processes
are variable
Change
Process
Analyze
process
variation
Develop
process
knowledge
Reduce
Variation
Improved
Quality
Control
Process
Roger Hoerl, Ron Snee, Statistical Thinking Improving Business Performance, pg 13
(ISBN 0-534-38158-8)
Satisfied:
• Employees
• Customers
• Shareholders
• Community
Human Sigma
John H. Fleming
Gallup Consulting
• An holistic approach to optimizing the vital signs
of a company’s human systems
• Focus on reducing variability in performance and
improve organizational effectiveness
– The human aspects that drive profitability and growth
• In a service economy, value is created when an
employee meets and interacts with the customer
• Variation = Danger
3M Entitlement Quality
• Improvement methodologies are often treated
as “floats in a parade” (Jim Buckman, Juran
Center, U of MN Carlson School).
• But the improvement principles and tools are
bedrock... building blocks to continual
improvement.
• EQ integrates Statistical Thinking into a system
of continuous improvement approaches of
Quality-Lean-Six Sigma-Innovation methods to
optimize customer value.
3M Entitlement Quality
Back to basics...
– Focus on key business processes, value streams, and
customer CTQs
– Characterize process behavior (average and variation,
structure)
• “Plot the dots... and look at the plots” (Lynne Hare)
– Assess process state and capability
– Apply a critical thought process
– Address the root causes using the appropriate tool
regardless of the improvement toolkit.
3M Leadership Attributes
Building the Culture
•
•
•
•
•
•
Thinks from the Outside In
Drives Innovation and Growth
Develops, Teaches and Engages Others
Make Courageous Decisions
Leads with Energy, Passion and Urgency
Lives 3M Values
Statistical Thinking:
The Future
Roger Hoerl, GE Global Research
Statistical Thinking – What Next?
• There will always be “the next big thing” in
the business world
– Total Quality Management
– Reengineering
– Six Sigma
– Lean
– Innovation
– ???
Statistical Thinking – What Next?
• However, some things never go out of style:
–
–
–
–
–
Chocolate
High heels
Diamond rings
Pizza and beer
Spending holidays with the family
• Business improvement, including the use of
statistical thinking, is one of those things
– The concepts are timeless, and they work!
One Specific Thought
• The first principle of the Statistical Thinking
definition:
– “All work occurs in a system of interconnected
processes”
• This critical principle has not yet been applied
broadly to continuous improvement initiatives
– We have tended to focus on one improvement
process: Six Sigma, Lean, Reengineering, etc.
– We need initiatives that emphasize the system of
interconnected improvement processes
A System of Interconnected Improvement
Processes*
Process
Performance
Data
Customers
Process
Improvements
The Process
Feedback
Feedback
Reports &
Information to
Management
Process
Control
Periodic
Analysis and
Reviews
Feedback
Improvement
Projects
Continuous
Improvement
System
When Needed
*From Snee and Hoerl
Leading Six Sigma (2003)
Product &
Process
Redesign
System of Improvement Processes
• Must be managed and optimized as a system
– Not sub-optimized at the process level
– No competition among “favorite methods”
• Covers “Juran Trilogy”:
– Design, improvement, control
– Long-term, medium-term, short-term
improvement
• Avoids the “fad of the month” trap
• “Back to the future” – an idea whose time has
come (again)