1. JSM August 2002 NYC1 Education of Future (Industrial) Statistical Consultants Douglas C. Montgomery Professor of Engineering & Statistics Arizona State University doug.montgomery@asu.edu

2. JSM August 2002 NYC2 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

3. JSM August 2002 NYC3 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

4. JSM August 2002 NYC4 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

5. JSM August 2002 NYC5 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

6. JSM August 2002 NYC6 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”

7. JSM August 2002 NYC7 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)

8. JSM August 2002 NYC8 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

9. JSM August 2002 NYC9  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

10. JSM August 2002 NYC10  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?