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The Modern Practice of Industrial Statistics

Douglas C. MontgomeryProfessor of Engineering & Statistics

Arizona State University doug.montgomery@asu.edu

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OutlineIndustrial statistics, then and nowBusiness drivers Skills helpful for successWhat can academic programs and

institutions do?Increasing the power of statistics

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BackgroundToday’s statistician lives and works in

different/changing times• The “democratization” of statistics –

everybody’s doing it• Six sigma is playing a role in this• Widespread availability/use of statistical

software by nonstatisticians• Expanding scope of problems in which

statistics plays a role

These changes cannot be ignoredHow to play a leadership role?

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The New Environment

Lots of people use statistics; the techniques are no longer exclusively the province of statisticians

Applications in distribution systems, financial, and services are becoming at least as important as applications in manufacturing and R&D

“Statistical Thinking” in management decision making is becoming just as important as the actual use of statistical methods • Data-driven decision-making • “In God we trust, all others bring data”

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Statisticians are needed•Sometimes even wanted, respected (loved?)•But not just to analyze data, design experiments, etc•Non-statisticians often do that for themselves

The scope of professional practice is changing, expanding So – the options are: lead, follow, or get out of the wayHow do we do that?

The New Environment

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Some ContrastsThen Now

Narrow (operational) focus Broad, strategic focus

Consultant Team leader, facilitator

Design experiments, analyze data

Help define problems, tools to be employed

Teach statistics to small groups

Develop/implement broadly based systems (six sigma)

Technical clients Work with managers

Narrow application of professional skills

Broader application of an expanded skill set is expected

Limited accountability Great accountability

Low visibility (under radar), few opportunities

High visibility, potentially many opportunities

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Business Drivers

Flattening (“delayering”) of organizations • Less staff, fewer consultants & technical experts• More operational accountability

Shift from manufacturing to service economy• Impacts even traditional manufacturers• Supply chain management critical (domestic

content issues)

Drive to create value for stakeholders• More broad application of basic tools • Perhaps fewer applications of advanced tools

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Business Drivers

Data-rich, highly automated business and industrial environment

Semiconductor manufacturing process• Fabrication process typically has 200+ steps• Assembly and test required to complete

product• 1000s of wafers started each week• In-process, probe, parametric, functional

test data available

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Business Drivers Improve efficiency/effectiveness of

engineering design and developmentMove upstream

• Methods for reliability improvement continue to be of increasing importance - driven by customer expectations

• Reliability of software, process equipment (predictive maintenance) are major considerations

• Reducing development (cycle) time• Robustness of products and processes are still

important problems• DFSS a growing emphasis

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Traditionally the industrial statistician has been an internal consultant – manufacturing or R&D focus

This perspective is changing as statistical methods penetrate other key areas, including• Information systems• Supply chain management• Transactional business processes

Six-sigma activities have played a part in this

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The statistician's role is changing as well It’s important to be a “team member” (or

facilitator, leader) and not just a “consultant”

The mathematics orientation of many statistics programs does not make this easy

Quote from Craig Barrett (INTEL):“To be successful at INTEL, the statisticians

need to be better engineers”Statisticians still often

• Do not share in patent awards/recognition, other incentives

• Not viewed as full team members• Regarded as merely “data technicians”

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Some Key Background/Courses for Modern Industrial Statisticians

Preparation for professional practiceDesign of Industrial Experiments

• Emphasis on factorials, two-level designs, fractional factorials, blocking

• Random effects, nesting, split plotsResponse Surface Methodology

• Traditional RSM, philosophy, methods, designs

• Mixture Experiments• Robust design, process robustness studies

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Some “Must” Background/Courses for Modern Industrial Statisticians

Reliability Engineering • Survival data analysis, life testing• RAM principles• Design concepts

Modern Statistical Quality Control Analysis of Massive Data Sets

• Traditional multivariate methods• CART, MARS, other data mining tools

Categorical Data Analysis, GLM

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Forecasting, Time Series Analysis & Modeling (should overview a variety of methods, include system design aspects)

Discrete Event SimulationPrinciples of Operations Research

• Basic optimization theory• Linear & nonlinear programming• Network models

Some “Must” Background/Courses for Modern Industrial Statisticians

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I have just outlined about 27 semester hours of graduate work!!• Most MS programs require 30 hrs

beyond the BS (non-thesis option), 24hrs with thesis

• PhD programs require a minimum of 30 hrs of course work beyond the MS

• Academic programs would need to be significantly redesigned if a serious effort is going to be made to educate industrial statisticians

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Where do graduates go?• Lots of places: business and industry,

government, academia• But few of them will be theorists or

teach/conduct research in theory-oriented programs

• So why do many graduate programs operate as if all of them will?

• More flexibility is needed

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Most PhD programs require a minor (sometimes two, sometimes out-of-department)• Require that this be in engineering,

chemical/physical science, etc.• Most departments will be interested

in setting these up• Could also work at MS level• Certificate programs

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Recruit engineers/scientists/ORMS majors for graduate programs in statistics• But graduate programs had better be

meaningful!• Significant program redesign will be

requiredAlternative – develop joint

graduate (degree/certificate) programs with engineering departments, business schools

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The ASU Graduate Certificate Program in Statistics

Students take five approved coursesCertificate can be pursued as part of

a graduate degree or as a stand-alone program

Emphasis area in industrial statistics and six-sigma methods is available

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Industrial Statistics & Six-Sigma Design of ExperimentsRegression AnalysisStatistical Quality Control

• Shewhart control charts• Measurement systems analysis• Process capability analysis• EWMAs, CUSUMs, other univariate

techniques• Multivariate process monitoring• EPC/SPC integration

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Industrial Statistics & Six-SigmaSix-Sigma Methods

• How to use tools (case studies, illustrations)• DMAIC framework• Non-statistical skills • Design for six-sigma, lean concepts• Taught by six-sigma black belts from

industrySix-Sigma Project

• 150 hour duration• Typical industrial BB project• Must use DMAIC approach, statistical tools• Supervised by faculty & industrial sponsor

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Project ExamplesDevelop web-based decision system for

deployment of statistical tools Reduce average internal cycle time of

instrument calibration lab Develop prediction model for rate of

customer returns to quantify benefits of yield and test coverage improvements, and to identify parts within a technology that do not fit the model

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Increasing the Power of Statistics

A force F acting through a distance s performs work:

W = Fs

s

F

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F

s

Power is a measure of how fast work is done:

Increasing the Power of Statistics

Fs WP

t t

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Increasing the Power of Statistics Fs

Pt

More force = more power

More distance more power

Shorter time = more power

How well can we apply force to this opportunity?

How much leverage (distance) can we generate?

How quickly can we apply it?

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Statistics in Business and Industry Use of statistical methods (thinking?) is routine Statisticians can be leaders, change agents Logistics/service/financial applications are

growing rapidly This requires a different type of professional

with different skills There are significant challenges in preparing

these individuals for profession practice

Statisticians are valued and needed

FsP

t