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Education of Future (Industrial) Statistical Consultants. Douglas C. Montgomery Professor of Engineering & Statistics Arizona State University [email protected] Challenges for Industrial Statisticians. Today’s industrial environment is often data-rich and highly automated

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Education of future industrial statistical consultants

Education of Future (Industrial) Statistical Consultants

Douglas C. Montgomery

Professor of Engineering & Statistics

Arizona State University

[email protected]

JSM August 2002 NYC


Challenges for industrial statisticians
Challenges for Industrial Statisticians

  • Today’s industrial environment is often data-rich and highly automated

  • Taxonomy of methods:

    • data collection

    • data storage

    • data analysis

    • data warehousing

    • data mining

    • data drilling – leading to

    • data blasting, and finally

    • data torturing

JSM August 2002 NYC


Challenges for industrial statisticians1
Challenges for Industrial Statisticians

The multivariate nature of process data

  • If you would not use a one-factor-at-a-time experiment, why do we continue to apply lots of univariate control charts?

  • This has implications for what we teach

  • Many techniques have promise, including multivariate generalizations of standard control charts, CART, MARS, latent structure methods – we don’t teach students enough about these techniques

JSM August 2002 NYC


Challenges for industrial statisticians2
Challenges for Industrial Statisticians

Extending use of statistical methods into engineering design and development

  • Methods for reliability improvement continue to be of increasing importance - driven by reduced design/development leadtimes, customer expectations

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

  • Robustness of products and processes are still important problems

JSM August 2002 NYC


Challenges for industrial statisticians3
Challenges for Industrial Statisticians

  • Traditionally the industrial statistician has been viewed as a “manufacturing” person

  • This perspective is changing as statistical methods penetrate into other key areas, including

    • Information systems

    • Supply chain management

    • Transactional business processes

  • Six-sigma activities have played a role in this

JSM August 2002 NYC


Education of industrial statisticians
Education of Industrial Statisticians

  • It’s important to be a “team member” and not just a “statistical consultant”

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

  • Quote from Craig Barrett (INTEL)

  • Statisticians often do not share in patent awards/recognition, other incentives – sometimes regarded as merely “data technicians”

JSM August 2002 NYC


Some must courses for modern industrial statisticians
Some “Must” Courses for Modern Industrial Statisticians

  • Design of Industrial Experiments

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

    • random effects, nesting, split plots

  • Response Surface and Mixture Experiments (should include some robust design, process robustness studies)

  • Reliability Engineering (should include RAM principles, test design, as well as survival data analysis)

JSM August 2002 NYC


Some must courses for modern industrial statisticians1
Some “Must” Courses for Modern Industrial Statisticians

  • Modern Statistical Quality Control

  • Analysis of Massive Data Sets

  • Categorical Data Analysis, GLM

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

  • Discrete Event Simulation

  • Principles of Operations Research

    • Basic optimization theory

    • Linear & nonlinear programming

    • Network models

JSM August 2002 NYC


  • I have just outlined about 27 semester hours of graduate work!!

    • Most MS programs require 30 hr beyond the BS (non-thesis option), 24hr with thesis

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

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

  • 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 eager to help set these up

    • Could also work at MS level

JSM August 2002 NYC


  • Recruit engineers/scientists for graduate programs in statistics

    • But graduate programs had better be meaningful!

    • Significant program redesign will be required

  • Alternative – develop joint graduate programs with engineering departments, business schools

  • Where do graduates go?

    • Lots of places, 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?

JSM August 2002 NYC


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