Statisticians and Statistical Organizations How to Be Successful in Today

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Statisticians and Statistical Organizations How
to Be Successful in Todays 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
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Abstract

• The statistics profession is at a critical
  point in its history and has been for some time.
  The May 2008 Technometrics article, Future of
  Industrial Statistics, summarized many of the
  major issues. Two key drivers are global
  competition and the rapid growth of information
  technology. The old model for the use of
  statistical thinking and methods in business and
  industry, which has been around for at least 50
  years, does not work in todays business
  environment. This presentation begins with a
  brief summary of the current state of the
  profession and then moves quickly to a focus on
  what statistical organizations and statisticians
  as individuals need to do to effectively deal
  with the new environment. The focus is on
  strategies and approaches that have been found to
  work. Several case studies will be presented to
  illustrate the new model and the needed changes.

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Agenda

• Todays 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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Todays 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 havent fundamentally modernized the model
  for applied statistics since the 1950s
• 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 todays environment will help us
  understand the future of statisticians and
  statistical organizations

The Choice is Yours Survival Isnt Mandatory
W. E. Deming
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Expanding World of StatisticsThe 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
Collaborator/Leader

• Consult on other peoples projects
• Perform routine analyses if needed
• Teach statistical tools
• Work with technical people
• Narrow expertise and accountability
• Benign neglect

• Lead or collaborate on our own projects
• Focus on significant, complex problems
• Design training systems
• Work with managers and technical people
• Broad expertise and accountability
• 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 dont manage in the short term, there
  wont 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 RD,
  Manufacturing and other functions including
• 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, RD,
  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

• 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
Results ()
Control

• Goals
• Problems
• Gaps

Improve
Analyze

• Leadership
• Teamwork
• Stakeholder Building
• Project Management

Measure
Define
II-18
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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 RR
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
Process
Process Map
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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
• Project Tracking and Reporting

• Methods and Tools
• 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

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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 Linkasq.org/discussionBoards/forum.ispa?foru
  mID77

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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 Ron_at_SneeAssociates.Com