SSC June 2003 Halifax1 The Modern Practice of Statistics in Business and Industry Douglas C. Montgomery Professor of Engineering & Statistics Arizona State University

SSC June 2003 Halifax2 Background  Today’s statistician lives and works in different/changing times Widespread availability/use of statistical software by nonstatisticians The “democratization” of statistics (six- sigma) – everybody’s doing it Expanding scope of problems in which statistics plays a role  These changes cannot be ignored  How to play a leadership role?

SSC June 2003 Halifax3 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”

SSC June 2003 Halifax4  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 way The New Environment

SSC June 2003 Halifax5 Some Contrasts ThenNow Narrow (operational) focusBroad, strategic focus ConsultantTeam leader, facilitator Design experiments, analyze data Help define problems, tools to be employed Teach statistics to small groupsDevelop/implement broadly based systems (six sigma) Technical clientsWork with managers Narrow application of professional skills Broader application of an expanded skill set is expected Limited accountabilityGreat accountability Low visibility (under radar), few opportunities High visibility, potentially many opportunities

SSC June 2003 Halifax6 Business/Industry 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

SSC June 2003 Halifax7 Business/Industry 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

SSC June 2003 Halifax8  Taxonomy of methods: data collection data analysis/manipulation data storage data warehousing data mining data drilling – leading to data blasting, and finally data torturing Traditional statistics courses

SSC June 2003 Halifax9  We don’t recommend one- factor-at-a-time experiments, why do we use lots of univariate control charts?  This has implications for academic programs, what we teach students  Emphasis on small sample sizes, hypothesis testing, P- values, etc The multivariate nature of process data

SSC June 2003 Halifax10 Business/Industry Drivers  Extend use of statistical methods into engineering design and development 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

SSC June 2003 Halifax11  Traditionally the industrial statistician has been an internal consultant Often viewed primarily as a “manufacturing” person  This perspective is changing as statistical methods penetrate other key areas, including Information systems Supply chain management Transactional business processes  The statistician's role is changing as well  Six-sigma activities have played a part in this

SSC June 2003 Halifax12  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”

SSC June 2003 Halifax13 Some “Must” Background/Courses for Modern Industrial Statisticians  Preparation for professional practice  Design of Industrial Experiments Emphasis on factorials, two-level designs, fractional factorials, blocking Random effects, nesting, split plots  Response Surface Methodology Traditional RSM, philosophy, methods, designs Mixture Experiments Robust design, process robustness studies

SSC June 2003 Halifax14 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

SSC June 2003 Halifax15  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 Some “Must” Background/Courses for Modern Industrial Statisticians

SSC June 2003 Halifax16  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

SSC June 2003 Halifax17  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

SSC June 2003 Halifax18  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

SSC June 2003 Halifax19  Recruit engineers/scientists/ORMS majors for graduate programs in statistics But graduate programs had better be meaningful! Significant program redesign will be required  Alternative – develop joint graduate (degree/certificate) programs with engineering departments, business schools

SSC June 2003 Halifax20 The ASU Graduate Certificate Program in Statistics  Students take five approved courses  Certificate 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

SSC June 2003 Halifax21 Industrial Statistics & Six-Sigma  Design of Experiments  Regression Analysis  Statistical Quality Control Shewhart control charts Measurement systems analysis Process capability analysis EWMAs, CUSUMs, other univariate techniques Multivariate process monitoring EPC/SPC integration

SSC June 2003 Halifax22 Industrial Statistics & Six-Sigma  Six-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 industry  Six-Sigma Project 150 hour duration Typical industrial BB project Must use DMAIC approach, statistical tools Supervised by faculty & industrial sponsor

SSC June 2003 Halifax23 Project Examples  Develop 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

SSC June 2003 Halifax24 Increasing the Power of Statistics A force F acting through a distance s performs work: W = Fs s F

SSC June 2003 Halifax25 F s Power is a measure of how fast work is done: Increasing the Power of Statistics

SSC June 2003 Halifax26 Increasing the Power of Statistics 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?

SSC June 2003 Halifax27 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