A Statistical Career in Engineering & Industry

A Statistical Career in Engineering & Industry Some (maybe) useful links:- Tim Davis PhD, CStat, CEng, FIMechE June 17, 2013 http://www.timdavis.co.uk/career http://www.timdavis.co.uk/technicalfellowarticle • http://scholar.google.co.uk/citations?user=54ao7XkAAAAJ&hl=en • http://www.timdavis.co.uk/lectures%26conferencepresentations • http://www.linkedin.com/in/tdavis5

Preamble There are three main areas of human endeavour through which, by interfering with the natural order of things, we attempt to make life better for mankind – these are • Agriculture • Experiments take a long time due to the motion of the planet around the sun • Number of treatments and sample sizes usually not a problem • Medicine • Ethical considerations constrain experiments, and replication can be slow • Regulatory authorities like certain numbers on p-values • Engineering • Experiments are sequential, and usually quick • Emphasis is on selection rather than estimation Each field is contextually different. Statistical careers are probably better known in Agriculture and Medicine than Engineering

Engineering and the iterative learning process “Engineering is the profession in which a knowledge of the mathematical and natural sciences, gained by study, experience, and practice, is applied with judgment to develop ways to utilize, economically, the materials and forces of nature for the benefit of mankind.” Deduction Induction Box, GEP. "Science and statistics". JASA, Vol. 71, No 356, 1976, pages 791-799.

Engineering and the iterative learning process “Engineering is the profession in which a knowledge of the mathematical and natural sciences, gained by study, experience, and practice, is applied with judgment to develop ways to utilize, economically, the materials and forces of nature for the benefit of mankind.” Deduction Induction Davis, TP (2006). “Science, engineering, and statistics”. Applied Stochastic Models in Business and Industry, 22, Issue 5-6, 401-430. The contribution of statistical science to engineering is to a) encourage creativityand b) to ensure convergence.

An engineer must be able to… • Recognise need • Define problems • Conceive alternatives • Predict consequences • Design experiments and draw inferences • Test and evaluate • Delineate solutions • Understand production and distribution • Be intellectually honest This list is from a classic engineering text by E Mischke (Mathematical model building – an introduction to engineering). These criteria also describe the attributes of an engineering statistician!

Tim Davis - Career • 1981 – BSc Statistics, Aberystwyth • 1981 – Dunlop Ltd. • 1982 – Fellow, Royal Statistical Society (RSS) • 1983 – Started PhD at University of Birmingham • 1985 – Sumitomo Rubber Industries, Japan

Hazard functions of tire failures } Estimate by Estimate by 4 Called competing risks [The likelihood function for the case of tied lifetimes with different failure types needed straightening out, so I wrote my PhD thesis on this.]

Tim Davis - Career • 1986 – Ford Motor Company • 1988 – Captain’s Player Ford Warley CC • 1989 – Best Fielder Ford Warley CC • 1991 – PhD (Competing Risks Survival Analysis) • 1991 – Council member RSS (4 year term; VP ‘93-’95) • 1992 – Book (Engineering, Quality & Experimental Design) with Dan Grove • 1995 – Quality Manager, Ford Werke AG, Köln

Tim Davis - Career • 1992 – Greenfield Industrial Medal, RSS • 1993 – Lecturer at UCL on Statistics MSc course • 1994 – Chartered Statistician (C.Stat.) • 1995 – Quality Manager, Ford Werke AG, Köln, Germany https://lectopia.ncl.ac.uk/lectopia/lectopia.lasso?ut=10855&id=3841

Transmitted variation Jim Morrison’s Glass Bead example (1) (2) Amount of glass, Morrison, SJ (1957). “The study of variability in engineering design”. Applied Statistics, Vol6, No. 2, 133-138. Diameter, Length,

Electric motor in a window winder = magnet thickness = internal diameter of motor housing = rotor core diameter = magnet angle = magnet density = motor torque = wire diameter = wire length = wire conductivity = armature length = magnet length Variability in Torque causes variability in window closing times R Parry-Jones (with TP Davis, G Green) - 1999. Engineering for corporate success in the new millennium. Royal Academy of Engineering. ISBN 1 871634 83 0.

Engine Modeling T Holliday, AJ Lawrance, & TP Davis. "Engine mapping experiments: a two-stage regression approach". Technometrics, Vol. 40 #2, pages 120-126. Engine modeling involves predicting how an engine performs (in terms of torque, yT, or emissions, yE) as a result of changing load (xL), RPM (xR), spark advance (xS) air & fuel mixture (xA), amount of exhaust gas recycled (xE), etc. It is an important activity in Engine Mapping. There are two possible ways to view the (empirical) model: Either as a “one shot” response function, written as yT= f(xL, xR, xS, xA, xE) {# of parameters = # of coefficients} Or as a “two-stage” response function, written as 1st stage: yT = fs(xS; b1, b2, b3,…); fs(.) are know as “spark sweeps” 2nd stage: {# of parameters < # of coefficients} Hence, the 2-stage approach reduces model complexity (design parsimony) – hence less prediction error.

Engine mapping with Spark Sweeps Residual Plot # of parameters = # of coefficients Residual Plot 1st stage: 2nd stage: ) # of parameters < # of coefficients

Tim Davis - Career • 1995 – Member, American Society of Quality (now a Senior member) • 1999 – Quality Director, Ford Motor Company, Detroit, USA • 2000 – Firestone Tire crisis • 2001 – 10th Henry Ford Technical Fellow (for Quality Engineering) • 2002 – Created the Office of the Technical Fellow to support the CTO “Established in 1994, the Henry Ford Technical Fellow distinction is the most prestigious technical expert position in the Ford Motor Company. It is intended to recognize exceptional engineers or scientists in research, product development, and manufacturing. The position was created for top technical experts with an international reputation in their particular field of automotive expertise. Fellows provide technical expertise and leadership in the application of relevant engineering and scientific principles to manufacturing and product development teams. They also play a major consultative role in the development of corporate technical strategy.”

The 2000/01 Firestone tire crisis • In 2000, it was reported in the US media that people had been killed (~300 in total) in roll-over accidents involving tread separations . • All the accidents involved certain Firestone tires • Most of the accidents involved Ford Explorers • In September 2000, Firestone recalled some (~5m) of the suspect tires • In May 2001, Ford recalled another ~20m tires that, it was determined (based on my work), might also fail. • Several trips to Washington DC during the crisis, and legal depositions & taking the witness stand for 1½ days in the high profile court case followed. • This crisis was my “Challenger accident”. A heady mix of science, ethics, legal wrangles, and politics. The science & ethics won.

The hazard function (again) Cumulative hazard analysis  Increasing Failure Rate. Note differences between factory of origin for the same tire type. Subject Tires (colour relates to factory) Cumulative hazard x10-6 Other tires (colour relates to brand) Tire age (years)

Developing a lab test to mimic the field Factorial design – to develop a lab test to replicate the failure mode, and the relative failure frequency Standard Load Pressure Ambient Temp. 1785lbs 32psi 30psi 26psi 1500lbs 100oF 70oF 22psi = no tread separation = tread separation 18psi 1300lbs

The 2000/01 Firestone tire crisis The recall decision was made to replace 20 million tires ($3Bn) before the authorities asked us to do it. “… the set of cumulative hazard function curves for the recalled tires… demonstrate that if they are not removed from service, the focus tires from these plants – … will experience a similar increase in tread separation failures over the next few years.…” Engineering Analysis Report and Initial Decision regarding EA0023: Firestone Wilderness AT Tires U.S. Department of Transportation National Highway Safety Administration Safety Assurance Office of Defect Investigation October 2001 NHTSA report available at www.nhtsa.gov/nhtsa/announce/press/Firestone/

Tim Davis - Career • 2004 – Fellow I.Mech.E, and Chartered Engineer (C.Eng.) • 2005 – IMechE Donald Julius Groen Prize in reliability, for Failure Mode Avoidance. • 2006 – Honorary Professor, University of Warwick • 2007 – Quality Director and Board Member – Jaguar Land Rover • 2008 – Business sold to Tata Motors

Failure Mode Avoidance • Treats failure modes as due to 2 causes (due to Don Clausing) • Lack of robustness • Mistakes • Doesn’t need the statistical mathematics of defining reliability as a probability; instead it uses an information based approach, combined with the FMEA. • The job of the engineer is to select the design that will fail the least, not to estimate the failure rate of the selected design • “If a guy tells me the probability of failure is 1 in 105, I know he’s full of crap” – Richard Feynman http://history.nasa.gov/rogersrep/v2appf.htm

Asynchronous material and information flow in product/technology development Easy to detect, hard to fix Latitude to take counter-measures time Release Part A Release Part B Release Part C Release Part D ① release drawings ② build ③ detect, fix

Synchronous material and information flow in product/technology development Easy to detect, hard to fix Latitude to take counter-measures Hard to detect, easy to fix time Release Part A Release Part B Release Part C Release Part D ① detect, fix ② release drawings ③ build

Tim Davis - Career • 2010 – Council & Executive Committee member RSS, 2nd term • 2010 – Established timdavis consulting ltd. (www.timdavisconsulting.com) • 2012 – CTO at We Predict Ltd. (www.wepredict.co.uk) • 2012 – Elected member of International Statistical Institute • 2013– Fellow of the American Statistical Association (ASA), and P.Stat.

My Statistical Philosophy • Statistics should be seen as a branch of science, and statisticians should be the custodians of the scientific method (induction vs. deduction); • Therefore, statistical science notstatistical mathematics; • Focus on hypothesis generation, not just hypothesis testing; • Recognize that often, the problem is one of selection, not estimation (the job of the engineer is to select the design that will fail the least, not predict the failure rate of the chosen design); • Graphical methods vs. numerical/tabular methods; • Exploit observations with strange residuals; • Parsimony over complexity; • Context – timescales, loss function, sequential vs. “one-shot”.

… and finally • More statisticians need to get into positions of senior management if we as a profession are to make an impact on business and industry.

A Statistical Career in Engineering & Industry