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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.

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Presentation on theme: "Statisticians and Statistical Organizations How to Be Successful in Todays World? Ronald D. Snee Snee Associates With Significant Contributions from Roger."— Presentation transcript:

1 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

2 IV-2 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.

3 IV-3 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 Agenda

4 IV-4 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

5 IV-5 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

6 IV-6 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

7 IV-7 Expanding Role of Statisticians 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 Consultant Collaborator/Leader Computer Scientists Provide an Example of Such a Role

8 IV-8 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

9 IV-9 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

10 IV-10 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

11 IV-11 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

12 IV-12 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.

13 IV-13 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,

14 IV-14 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.

15 IV-15 Two Examples of Statistical Engineering Product Quality Management at DuPont Process and Organizational Improvement Using Lean Six Sigma

16 IV-16 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

17 IV-17 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

18 IV-18 Sense of Urgency Define Improve Control Results ($$) Measure Analyze Data Leadership Teamwork Stakeholder Building Project Management Lean Six Sigma Tools DMAIC Process Improvement Framework II-18

19 IV-1919 ToolDefineMeasureAnalyzeImproveControl 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 Six Sigma Uses a Small Set of Tools

20 IV-20 Customers Six Sigma Tools are Sequenced and Linked Process Map Improvement Need FMEA Control Plan C&E Matrix MSA Process Capability Multi-VariDoE SPC 20

21 IV-21 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 The Tools Are Part of An Improvement System

22 IV-22 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.

23 IV-23 Summary Whether we like it or not, our environment today is radically different than even 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

24 IV-24 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 References

25 IV-25 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

26 IV-26 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

27 IV-27 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.

28 IV-28 For Further Information, Please Contact: Ronald D. Snee, PhD Snee Associates (610)

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