Transcript Slide 1

Statisticians and Statistical Organizations
How to Be Successful in Today’s World?
Ronald D. Snee
Snee Associates
With Significant Contributions from
Roger W. Hoerl, General Electric
2009 Quality and Productivity Research Conference
IBM T. J. Watson Research Center
Yorktown Heights, NY
June 3-5, 2009
Agenda
 Today’s Realities
 We Need to Change our Thinking
 What Should Statisticians be Doing?
 Helping Our Organizations Succeed
 Focus on Statistical Engineering
 “Embedding” Statistical Tools in Work Processes
 Summary
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Today’s Realities
 Profession appears to be at a crucial point in its history
 Recent Technometrics article and blog highlight major
issues we must deal with going forward
 “Future of Industrial Statistics: A Panel Discussion”
 ASQ Stat Division Newsletter article by Vijay Nair
 Disconnect between academic research and practice
 We haven’t fundamentally modernized the “model” for
applied statistics since the 1950’s
 Pure science versus statistics as an engineering
discipline?
 Leadership is lacking and desperately needed
 No evidence that we have critical mass to change
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How Should We Respond?
 Jump in “fox holes” and wait for the crisis to blow over
 Argue against globalization
 Understand the fundamental changes in our environment,
 Embrace them
 Adapt to them
 Take advantage of them
 Understanding today’s environment will help us understand
the future of statisticians and statistical organizations
The Choice is Yours
“Survival Isn’t Mandatory”
W. E. Deming
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Expanding World of Statistics
The Profession Has Responded
 Launching of Sputnik by the Soviet Union:
 Created the need for design of experiments and other
statistical methods in research and development
 Food, Drug and Cosmetics Act created the need for
statisticians in the pharmaceutical industry
 Clean Air Act and the Environmental Protection Agency
created the need for environmetrics and the use of
statistics in solving environmental problems
 Global Competition and Information Technology
creates need for improvement
Needs of Employers and Society Define the
Roles and Uses of Statistics
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Expanding Role of Statisticians
Consultant
 Consult on other people’s
projects
 Perform routine analyses if
needed
 Teach statistical tools
 Work with technical people
Collaborator/Leader
 Lead or collaborate on our
own projects
 Focus on significant,
complex problems
 Design training systems
 Work with managers and
technical people
 Narrow expertise and
 Broad expertise and
accountability
accountability
 “Benign neglect”
 “In the firing line”
Computer Scientists Provide an Example of Such a Role
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What Should Our Focus Be?
 “Anyone can manage for the short term or the long
term; real success comes from managing both short
term and long term at the same time…
If you don’t manage in the short term, there won’t be
a long term” (Jack Welch).
 “The complex problems of this world will not be
solved at the same level of thinking we were at when
we created them.” (Albert Einstein)
 We need to
 Think differently.
 Be bold but not reckless
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Helping your Organization Deal with the
Global Financial Crisis – Short Term
 Cost reduction and short term cash flow
 Quick wins essential for sustaining change (John Kotter)
 Prudent risk taking
 Process understanding is needed
 Reducing variation reduces risk
 Effective prioritization – working on the right things
 Improvement project selection
 Customer and employee surveys
 Follow the money
Statisticians Can Play a Major Role
in Each of These Areas
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Reinvigoration of Improvement
Bottom Line Improvement Never Goes Out of Style
 Some may respond, “been there, done that.”
 “We have already done Lean Six Sigma, and now
moved on to bigger and better things”
 Improvement is particularly needed now
 Lean Six Sigma also helps us make sure that we are
working on the right things
 The result will be
 Immediate, bottom line results
 Help with business prioritization
 Risk management approaches that balance need for
income generation with need to limit risk
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What Else Should Statisticians be Doing?
A Longer Term View
 Greater emphasis on “statistical engineering” relative
to “statistical science”
 “Embedding” statistical methods and principles into
key business process
 Making the use of statistical thinking and methods part
of how we work
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What Does Society Need from Statisticians?
Decades of the 1950s, 60s and 70s
 Statistical science needed to be developed to deal with
the problems encountered in R&D, Manufacturing and
other functions including:
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Efficient and effective experimentation
Empirical modeling
Process control
Process optimization
 Need for statistical engineering was there, but limitations
of available methods created a stronger need to develop
statistical science.
21st Century
 Society needs statistics to be primarily an engineering
discipline, with a secondary focus on statistical science.
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Statistical Engineering
Engineering focuses on how to best utilize known
scientific and mathematical principles for the benefit
of mankind.
 Pure science works to advance our understanding of
natural laws and phenomena.
Example
 Chemist may attempt to advance understanding of the
fundamental science of chemistry
 Create a new marketable substance
 Chemical engineer would more likely attempt to better
utilize the current understanding to greater human
advantage.
 Determine how to scale up the process to produce this
substance commercially,
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Engineers Develop Engineering Theory
 Engineers do research to develop new theory
 Engineers’ theoretical developments:
 Tend to be oriented towards the question of how to
best utilize known science to benefit society
 Rather than on how to advance known science.
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Two Examples of Statistical Engineering
 Product Quality Management at DuPont
 Process and Organizational Improvement Using
Lean Six Sigma
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PQM – Statistically Based Product Quality
Management System
 Product Quality Management (PQM)
 Framework for managing the quality of a product or service.
 Operational system the enables Marketing, R&D, Production
and support personnel to work together to meet increasingly
stringent customer requirements
 “Within two years product quality had improved to the point
of commanding a marketplace advantage and more than $30
million had been gained in operating cost improvements.
The statistically based Product Quality Management system
developed for “Dacron” was expanded to other products
with further contributions in earnings.”
Richard E. Heckert
Chairman and CEO, DuPont Company
ASA Annual Meeting 1986
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PQM System – Statistical Techniques Used
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Sampling Schemes
Product Release Procedures
CUSUM Process Control
Shewhart Control
ANOVA and Variance Components
Inter-Laboratory Studies
Design of Experiments
Response Surface Methodology
Graphical Tools
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DMAIC Process Improvement Framework
Sense
of
Urgency
Lean Six Sigma
Tools
Results ($$)
Data
Control
Improve
Analyze
Leadership
Teamwork
Stakeholder Building
Project Management
Measure
Define
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Six Sigma Uses a Small Set of Tools
Tool
Define
Measure Analyze Improve
Control
Project Charter
Maps
Cause and Effect
Matrix
Capability
Analysis
Gage R&R
Failure Modes &
Effects Analysis
Multi-Vari
Studies
Design of
Experiments
Control Plans
and SPC
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Six Sigma Tools are Sequenced and Linked
Customers
Process
SPC
Control Plan
Process
Map
MSA
C&E Matrix
Process
Capability
Multi-Vari
DoE
FMEA
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The Tools Are Part of An Improvement System
Deployment
• Improvement
• Breakthrough
• Systematic,
Focused Approach
• Right People:
 Selected &Trained
• Results:
 Process &
Financial ($$)
• Communication
• Recognition and
Reward
• Improvement
Initiative Reviews
Projects
• Right Projects:
 Linked to Business
Goals
• Project Portfolio
Management
• Projects:
 Execution
 Reviews
 Closure
• Sustain the Gains:
 New Projects
Methods and Tools
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Process Thinking
Process Variation
Facts, Figures, Data
Define, Measure, Analyze,
Improve, Control
• 8 Key Tools:
 Sequenced and Linked
• Statistical Tools
• Statistical Software
• Critical Few Variables
• Project Tracking and
Reporting
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Embedding Statistical Thinking in Core
Business Processes – Some Examples
 Product Quality Management at DuPont
 Design and analysis of clinical trials conducted by
pharmaceutical and biotech organizations
 Driven by FDA
 Track safety and injury data – Mandated by OSHA
 Managers often study tabular reports and respond to
random variation
 Plotting safety data over time on a control chart, or even
a run chart, can save a lot of time and effort by providing
a more insightful view of the process performance.
 If the appropriate statistical tools are part of the
information system, we would say that tools have been
“embedded”.
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Summary
 Whether we like it or not, our environment today is
radically different than even 10 - 15 years ago
 To prosper in the 21st century, statisticians need to play
broader leadership role
 More pro-active and clearly value-adding.
 Focus should be on:
 Bottom-line improvement – It never goes out of style
 Significant, complex problems
 Statistical Engineering
 Embedding statistical approaches in work processes
A High-Yield Strategy
Change Before You Are Forced to Change
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References
Hoerl, R. W. and R. D. Snee (2002) Statistical Thinking – Improving
Business Performance, Duxbury Press, Pacific Grove, CA.
Kotter, J. P. (1996) Leading Change, Harvard Business School Press,
Boston, MA.
Marquardt, D. W. (1991) ed., PQM: Product Quality Management
(Wilmington, DE: E.I. DuPont de Nemours & Co. Inc., Quality
Management and Technology Center). A shorter version appears in
Juran's Quality Handbook 5th Edition
Snee, R. D. and R. W. Hoerl (2003) Leading Six Sigma – A Step by Step
Guide Based on the Experience With General Electric and Other Six
Sigma Companies, FT Prentice Hall, New York, NY,
Snee, R. D. and R. W. Hoerl (2005) Six Sigma Beyond the Factory Floor –
Deployment Strategies for Financial Services, Health Care, and the
Rest of the Real Economy, Financial Times Prentice Hall, NY, NY.
Technometrics (2008) “Future of Industrial Statistics – A Panel Discussion.
Technometrics Blog Link
asq.org/discussionBoards/forum.ispa?forumID=77
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Cost Reduction and Short Term Cash Flow
 Bottom line improvement is needed today more than
ever before in, at least in recent history
 Productivity = System output / resources used.
 You can increase productivity by reducing resources or
by increasing system output.
 We believe that the statistics profession could be well
positioned to identify ways to improve the system
 Reinvigoration of Lean Six Sigma can provide the
needed improvements
 Big Opportunity – Project selection
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Prudent Risk Taking –
Process Understanding is Needed
 Prudent risk taking can be done when we understand
our processes;
 Critical process drivers
 Capability of the processes to meet customer
requirements.
 Greater use of data and statistical tools can lead to
better process understanding.
 Statisticians have much to offer regarding quantifying
risk and making decisions in the face of this
uncertainty
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Effective Prioritization –
Working on the Right Things
 Effective prioritization is always important, but particularly
critical in this economy.
 Many companies have gone through massive layoffs.
 There are simply fewer resources available, both in terms of
people and money.
 Yet work has to be done if results are to improve.
 Careful prioritization of critical needs is required to identify
what must be done and what can be dropped or done later
 Statisticians can help the organization:
 Focus on a few key strategies,
 Use data to identify and prioritize improvement opportunities
 Use employee and customer surveys to identify opportunities,
 Follow the money - large income and expenditures are often
opportunities for improvement.
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For Further Information, Please Contact:
Ronald D. Snee, PhD
Snee Associates
(610) 213-5595
[email protected]
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