Statisticians and Statistical Organizations How

to Be Successful in Today’s World?

Ronald D. SneeSnee Associates

With Significant Contributions from Roger W. Hoerl, General Electric

2009 Quality and Productivity Research ConferenceIBM T. J. Watson Research Center

Yorktown Heights, NYJune 3-5, 2009

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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

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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

organizationsThe Choice is Yours

“Survival Isn’t 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

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

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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: 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

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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Sense Sense of of

UrgencyUrgency

Sense Sense of of

UrgencyUrgency

DefineDefine

ImproveImprove

ControlControl

Results ($$)Results ($$)

MeasureMeasure

AnalyzeAnalyze

DataData

LeadershipTeamworkStakeholder BuildingProject Management

Lean Six SigmaLean Six SigmaToolsTools

DMAIC Process Improvement Framework

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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

Six Sigma Uses a Small Set of Tools

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CustomersCustomers

Six Sigma Tools are Sequenced and Linked

Process

ProcessMap

Improvement Need

FMEA

Control PlanControl Plan

C&E Matrix

MSAProcess

CapabilityMulti-Vari DoE

SPC

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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

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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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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

References

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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@SneeAssociates.Com