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    • 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
    • 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 today’s 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.
      • 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
      Agenda
    • 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
    • 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
    • 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
    • Expanding Role of Statisticians
      • Consult on other people’s 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”
      Consultant Collaborator/Leader Computer Scientists Provide an Example of Such a Role
    • 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
    • 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
    • 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
    • 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
    • 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:
        • 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.
      • 21 st Century
      • Society needs statistics to be primarily an engineering discipline, with a secondary focus on statistical science.
    • 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,
    • 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.
    • Two Examples of Statistical Engineering
      • Product Quality Management at DuPont
      • Process and Organizational Improvement Using Lean Six Sigma
    • 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
    • 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
    • Sense of Urgency Goals Problems Gaps Define Improve Control Results ($$) Measure Analyze
      • Leadership
      • Teamwork
      • Stakeholder Building
      • Project Management
      DMAIC Process Improvement Framework II- Data Lean Six Sigma Tools
    • Six Sigma Uses a Small Set of Tools Capability Analysis Control Plans and SPC Design of Experiments Multi-Vari Studies Failure Modes & Effects Analysis Gage R&R Cause and Effect Matrix Maps Project Charter Control Improve Analyze Measure Define Tool
    • Six Sigma Tools are Sequenced and Linked Process Process Map Customers Improvement Need FMEA Control Plan C&E Matrix MSA Process Capability Multi-Vari DoE SPC
    • The Tools Are Part of An Improvement System
      • Deployment
      • Improvement
      • Breakthrough
      • Systematic, Focused Approach
      • Right People:
        • Selected &Trained
      • Results:
        • Process & Financial ($$)
      • 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
    • 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”.
    • Summary
      • Whether we like it or not, our environment today is radically different than even 10 - 15 years ago
      • To prosper in the 21 st 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
      • 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 5 th 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
      References
    • 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
    • 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
    • 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.
    • For Further Information, Please Contact: Ronald D. Snee, PhD Snee Associates (610) 213-5595 [email_address]