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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 doug.montgomery@asu.edu. 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

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  1. Education of Future (Industrial) Statistical Consultants Douglas C. Montgomery Professor of Engineering & Statistics Arizona State University doug.montgomery@asu.edu JSM August 2002 NYC

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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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