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AUGUST 2016 | VOLUME 1, ISSUE 1

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CPID Spring NewsletterSPRING 2016

Special Points of Interest

Dear CPID Members,

I am starting my year as chair of the CPID with great enthusiasm as we align to the ASQ strategic direction and 2020 goals.

I would like to take this opportunity to let you know some of our recent developments and future activities for 2016. First, I would like to announce our leadership structure. Anne Driscoll from Virginia Tech is the chair-elect; Jennifer Van Mullekom from DuPont is the secretary; Ashley Childress from Eastman is the treasurer. The division leadership also welcomes two new members: Sarah Burke from ASU and Jennifer Kensler from Shell. Sarah is the communications chair/website manager and Jennifer will take the responsibilities of membership chair.

The Fall Technical Conference (FTC) and the World Conference on Quality and Improvement are two important events that provide value to our members. We will continue supporting these events to ensure they are a successful learning and networking experience for our members.

The FTC was held in Houston, TX, in October 2015. The conference was well attended and a great success. Please join us for the 2016 Fall Technical Conference in Minneapolis, MN, October 6 – 7. This year marks the 60th anniversary of the conference. The theme of the conference is Statistics and Quality: Twin Pillars of Excellence.

The WCQI conference will be held in Milwaukee, WI, May 16 – 18. Please make sure to stop by our exhibit booth. We enjoy hearing from our members. We also will continue our CPID group on LinkedIn, led by Brooke Marshall, who is the past CPID chair. We encourage you to access it and participate in the discussions. This is a great opportunity to exchange ideas and comment on the topics posted as we continue working to develop our body of knowledge.

Thank you to all the member leaders in the CPID for all the hard work over the past year. We look forward to continuing to serve our members. If you have comments or suggestions, or would like to get involved in the division, please feel free to contact me at [email protected].

I am excited to be part of the CPID leadership team and look forward to a very productive year.

Flor Castillo CPID Chair, 2016

Message From the Chair………..1

Youden Address Perfect Makes Practice.……..…..2

2016 World Conference on Quality and Improvement….....10

Fall Technical Conference 2016……...….…..12

In Remembrance of Connie Borror……...….......…..13

Nominations Sought for 2016 William G. Hunter Award….....14

CPID Leadership, 2016………..15

Message From the ChairIN THIS ISSUE

mailto:[email protected]

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CPID Spring Newsletter

Perfect Makes Practice

CHEMICAL AND PROCESS INDUSTRIES DIVISION2

Youden Memorial AddressFall Technical ConferenceOctober 8, 2015

William A. BrennemanResearch FellowStatistics DepartmentThe Procter & Gamble Company

Introduction It is really very humbling and exciting to be chosen as this year’s Youden Memorial Address speaker. I have attended many FTCs in the past and have always enjoyed the Youden Memorial Address. I really hope this talk resonates with at least a few of you. And thanks to the Youden Address committee, made up of all four FTC sponsoring organizations—ASQ Statistics Division, who leads the process, ASQ CPID, ASA-Q&P, and ASA-SPES. Thank you for all your hard work during the selection process.

Today, I want to discuss a topic that has been near and dear to me throughout my career as an industrial/practicing statistician—how collaboration between industry and academia can be a win-win for both, and the critical role that the industrial, or practicing statistician, plays in this relationship is key. I hope to challenge both industrial and academic statisticians to think about how they can make collaboration happen—for the good of the profession (all of us together), for the good of the advancement of knowledge (main goal of academia), and for the good of the entity that employs the practicing statistician (company/government/self-employed).

I will first talk about the research cycle as it pertains to industry/academic collaboration. This is where I will make some sense out of the title of today’s talk, “Perfect Makes Practice.” Then I will discuss briefly the person that this session memorializes—Jack Youden, the person and the name. Then I will some time telling stories of research in industry from my personal experience. The focus will be on the story—how did the problem arise, how did we realize it was “new research,” was it done in-house or worked collaboratively externally, what were some of the challenges

working collaboratively, and what were the results, the win-win outcomes. I will then share just a few thoughts on the relationship and importance of both hard and soft skills in industry, followed by concluding comments.

The Research Cycle Academic research clearly drives the work that those in statistical practice do)—we need theoretical backing for the techniques that are used in industry and government. This is the current model for educating those who go into statistical practice, and so in this sense, academic research drives statistical practice. However, the “new research” cycle that is often seen within the academic community is that of taking a method/theory and making a small change or tweak. This is needed and good, and can have applications down the road. But we also need strong connections between industry and government and academia to drive new methods and theory, so that research is in line with what is needed today. Statistical practice can influence the direction of academic research. Now, if we replace “statistical practice” with “practice” and “academic research” with “perfect” (perfect in the sense that statistical theory is often indisputable evidence via a mathematical proof), then we get the English saying that “Practice Makes Perfect.” I contend though, in this chicken-and-egg type cycle, that the foundation and beginning of the research cycle starts with academia, thus a play on the English saying leads to my title of the talk, “Perfect Makes Practice.” See Figure 1 for a description of the research cycle. Let’s explore a bit more the “Practice Makes Perfect” part of the cycle.

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Many important statistical ideas and methods have their roots in industrial problems. Here are a few examples I took from David Steinberg’s paper, “Industrial Statistics: The Challenges and the Research.” Thank you to David for writing about this topic and for reaching out to Bill Myers and me from P&G, as well as many others, to review and provide input from an industrial perspective. Notice how each of these examples started with a problem from statistical practice and then led to academic research:

1. The t-test grew out of Gosset’s desire to analyze small samples of data at the Guinness Brewery.

2. The rank-sum test was developed by Frank Wilcoxon from his need for distribution-free methods to analyze data at American Cyanamid.

3. Statistical process control was Walter Shewhart’s response to needs arising from production at Bell Telephone Company.

4. The need for efficient munitions testing in World War II led Abraham Wald to invent the sequential probability ratio test.

5. The management philosophy of W. Edwards Deming, and the role of statistics in management and quality, arose from his close association with industry.

6. Ridge regression addressed problems of correlated predictors faced by Arthur Hoerl and Robert Kennard at Du Pont.

7. Exploratory data analysis was stimulated by problems John Tukey encountered at Bell Laboratories.

8. Response surface methodology resulted from George Box’s collaborations with chemists at Imperial Chemical Industries.

A Brief Look at Jack Youden: The Person and the Name For those who want to get a really good look at Jack Youden, I would recommend reading John Cornell’s 1992 Youden Address, “W.J. Youden – The Man and His Methodology,” or the wealth of information in the January 1972 issue of Quality Technology dedicated to Jack. Jack was born in 1900 and passed away in 1971, received a BS in chemical engineering in 1921 and a Ph.D. in physical chemistry in 1924. He started his career at the Boyce Thompson Research Institute for Plant Research in 1924 and had interactions with some well-known statisticians along the way, including R.A. Fisher, during his stint at the Galton Laboratory from 1937 to 1938. His very important paper, that some say started his transition from chemist to statistician was the 1937 paper entitled, “Use of Incomplete Block Replications in Estimating Tobacco Mosaic Virus,” in which the famous Youden square was introduced. He then worked at the National Bureau of Standards from 1948 until his retirement in 1965 as assistant chief of the statistical engineering laboratory in the applied mathematics division. As you can see, Jack took the nontraditional path of a statistician, going from chemist to statistician throughout his career. For him, Practice Made Perfect.

Before we transition to my personal stories of statistical practice leading to statistical research, let’s take a detour into some data analysis, shall I say data mining, that leads to a totally unusable, nonsensical conclusion that there is a conspiracy going on in the selection process for Youden Address speakers!

If we take a look back at the names of the past speakers, we see that since 1973 we have had 43 speeches, with 40 unique speakers. If we just look at the first names, then we see some unusual patterns start to develop—there are seven Williams, followed by four Johns, and then two Geralds and two Harrys. If you dig deeper though, you find that Harry Smith, John Cornell, and Gerry Hahn each gave two talks! So by graphing unique speaker first names (see Figure 2) we see that William comes first followed by John. Now comes the conspiracy part. Jack’s full name is William John (Jack) Youden. Clearly there is substantial bias shown by the selection committee to candidates with the first name either William or John!

Figure 1: The research cycle, showing the relationship between academic research (perfect) and statistical practice (practice)

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Stories of Research Stemming From Statistical Practice I would like to share four stories of the research cycle from my personal experience as a practicing statistician. I am sure that when you hear these stories, those of you who are practicing will find some common themes that resonate, and I would be happy to learn about your journey in this area. As you read these stories, try to answer the following questions:

1. How did the practicing statistician become aware that the problem is new statistical research?

2. What are the different avenues, routes to collaboration?

3. Can you identify win-win situations for both sides in the collaboration? What does win mean for each side?

4. Do you hear of any barriers to full collaborative engagement?

Example 1: Robust Design for Categorical Noise Variables

The first example comes from a project that dealt with optimizing

the seal of a package in which product was placed in a small single-serve tub and a foil seal is applied. We wanted to make sure that the package had a strong seal so that the contents would stay fresh. It is interesting to note that there is a range of acceptable amounts of force to open a package: If the force is too large, then the consumer struggles with opening the package, leading to dissatisfaction; while too little force, even if the package is sealed properly, can give the impression that it is not sealed properly! So in this experiment we had as the response the maximum peel strength as measured by a laboratory measurement system. The input variables are the dwell time that a given temperature and pressure are applied to the package. In addition, we had three suppliers of the tubs, so tub type is a three-level categorical variable. At first we just designed the experiment so we could estimate all main effects, all two-way interactions, and the quadratic effects of the three control variables. Initially it was not apparent that the tub type was a variable that we would want to consider as a noise variable,

Figure 2: First names of past Youden Address speakers

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but as we discussed the outcomes of the analysis more with the engineers, it became clear that they really would prefer to have settings of dwell time, temperature, and pressure that were robust to the tub type—in this way they would not have to have sealer settings for each of the tub suppliers. So once we understood the need for robustness, it soon became clear that this is a robust parameter design problem with a categorical noise variable. And my colleague, Bill Myers, and I initially thought that this was a new research problem—and indeed it was.

When you look at the interaction between the dwell time (cycles per minute) and the tub suppliers, one finds that the lower throughput leads to a more robust setting over the different tub suppliers. While this was disappointing from a project viewpoint, it was interesting for us in that we needed to find a way to get an equation for the variance of the predicted mean, taking into account the variation between the suppliers.

We also noted that there were some interesting differences in the variance equation when a categorical noise variable is present that we did not see when just continuous noise variables are present. We found that the robust setting can depend on the proportions of the categories of the categorical noise variable leading to another way to optimize the process—by changing the proportions of the tub suppliers.

This led to two research papers. The first was the initial paper that worked out the equations for the variance operator over the noise variable so that we can do multiple optimization. This research was done in-house (see Brenneman and Myers, 2003). The second came after the first through interactions at a conference with Professor Tim Robinson, University of Wyoming, to handle the optimization of the proportions of the categorical noise variables (see Robinson, Brenneman and Myers, 2006). The collaboration with Professor Robinson was done without any legal contract, as we just discussed the problems in terms of Xs and Ys. The win for the company was that we now had a systematic way to handle these problems and the win for Professor Robinson was the research problem and publication.

Example 2: Probability of Mixed Sampling Plans

The next story comes from what we call the fill weight problem—setting fill weight targets on products that have a label net content declaration, practically everything that P&G sells! The motivation for the work was to develop a consistent way to calculate fill weight targets within the company, quantify the risk of failing a government inspection (with various regulations around the world), and have a standard way to determine loss due to overpack.

Here is an introduction to one of the regulations—the U.S. fill weight regulations found in NIST Handbook 133. In this case we have Tide detergent that has a 100 fl. oz. label, so there is a table look-up value of 98 fl. oz. from the handbook (see Figure 3). Typically 12 products are sampled from the store shelf and the contents measured. The sample mean’s 97.5 percent upper confidence bound must be above label and the minimum of the sample must be above the reasonable lower limit of 98 fl. oz. (table look-up in NIST Handbook 133) in order to pass the inspection. So in this case we would pass the inspection. The intent of the regulations is to ensure companies are delivering on average the amount of product that is on the label, and there are some limits as to the amount of variation. This is called a “mixed sampling plan” since there are two criteria, one on the average and one on the minimum.

Prior to working on this problem, we had not heard of mixed sampling plans. Through a literature search we found a Technometrics paper from 1982 by Elder and Muse that derived an approximation to the probability of a mixed sampling plan under the old U.S. regulations. The old regulations stated that the sample average must be above label, not the upper confidence bound as in the new regulations. So this meant that with the new regulations there was a new research problem. In addition, we

Figure 3: An introduction to the U.S. fill weight regulations found in NIST Handbook 133

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wanted to have a solution in which the assumption of “in-control” filling processes did not hold.

Something you will hear from those in industry is that there is very little time to find a solution. In fact, we can’t always find the perfect solution. In this case we solved the problem of calculating the complicated probability through simulation—a technique that can be used to solve real industrial problems (a win for the industrial statistician) but is not worthy of a publication (theory is often needed). So we created a fully automated Web tool for the enterprise solution that contains the regulations from around the world and simply has a simulation running under the hood to calculate the probability of passing. After this in-house work was done, we reached out to Professor Derek Bingham and his team at Simon Fraser University to work on the theoretical part (see Linkletter et al., 2012). Again, the win for Professor Bingham, his students and colleagues at Simon Fraser was a very interesting problem—easy to state but very difficult to solve theoretically. Professor Bingham has since told me that this is one of the most interesting and challenging problems his team has faced from industry.

The result of the research was a Technometrics paper (Linkletter et al., 2012). We also wrote a paper with just P&G authors around the full development of the enterprise solution for the Quality Engineering special issue of statistical engineering case studies (see Brenneman and Joner, 2012). Again throughout the work with Simon Fraser we did not have a legal agreement in place, and the work was partially funded by Discovery Grants from the Natural Sciences and Engineering Research Council of Canada.

Example 3: Mixture-of-Mixture Problem

The third story comes from a mixture-of-mixture experiment. I was working with Pringles formulators on a project to reformulate Pringles to go into a bag. The trick was to make the crisp harder so that it would not get broken up while being shipped and jostled by consumers. There were also other responses, one of which was the percent of fat. There were three major components to the mixture, two of which contained minor components. If you are wondering what language I am speaking, it is mixture-of-mixture experimental language.

Sometimes in mixture experiments we have components, called major components, that in and of themselves can be broken down into sub-components, called minor components. Here is a schematic of a situation with three major components, A, B, C, with A1 and A2 minor components making up major component A. B1, B2, B3 are minor components of major component B. And here component C does not have any minor components (see Figure 4).

Figure 4: Schematic of classic mixture and mixture-of-mixture (MoM)

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I really struggled to design the Pringles experiment, the regular mixture designs just did not work out. After reviewing the literature, we soon realized that the multiple-Scheffé model in the literature had some limitation:

1. Discontinuity when major components go to zero2. Designs are prohibitively large3. Some terms are not meaningful

So we worked on the problem initially with Professor Lulu Kang, who at the time was a Ph.D. student under Professor Roshan Joseph at Georgia Tech. We brought her in for a summer internship and worked on this problem throughout the summer and then continued after that as the problem became a chapter in her thesis. So in this case, Professor Kang had full disclosure of all the details of the problem, the actual ingredient names and the reasons for the mixture-of-mixture setup during the manufacturing process. This made the problem much more enjoyable for her—not just using Xs and Ys. However, for her thesis and then subsequent Technometrics paper (see Kang, Joseph, and Brenneman, 2011), we had to remove some of the specific information and resort to Xs and Ys and transform the data.

Example 4: Slack Variable Mixture Model Approach

The fourth story also comes from mixture land. This work will be coming out in Technometrics (see Kang, Salgado, and Brenneman, to appear), is also joint work with Professor Lulu Kang, and was funded through a P&G Innovation Grant. However, we did not have a nondisclosure legal document agreed with P&G and the university, so we went with the Xs and Ys and transformed the data approach. The original problem came to me back in 2007. The P&G statistics department had put together a course on mixture experiments, borrowing all the ideas from the textbooks and published papers since we had no experience with mixture experiments at that time. And we gave the inaugural course to a large group of shampoo formulators. Well, soon into the class, a troublesome student in the back row, a Ph.D. in chemical engineering with more than 20 years of formulation experience, having done many experiments with standard designs, asked why we needed to use these mixture designs when the standard designs ignoring water have worked for years with much success. At the time, we had no good answer other than “this is what is in the textbooks.” Not a very convincing reply, but an honest one! In Figure 5, we provide a simple mixture design on the left in which the largest ingredient is water and if you treat water as a “filler” (that is, design in the other variables and just “fill in” with water to make 100 percent mixture), then the resulting, equivalent, design is shown on the right.

Figure 5: Example of a simple mixture design on the left in which water is used as a filler for the classical nonmixture design on the right

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The resulting model is what is called a “slack variable model” in the literature. I subsequently moved to hair care and worked directly with this formulator as well as many of the other formulators, and over time came to realize that there must be something here—the formulators all think in terms of a slack model. They were using a slack variable model with a lot of success. I also talked to practicing statisticians in other companies and many would say, in hushed tones, as if they were worried about someone overhearing, that they too were using slack variable model design and analysis for formulation experiments.

One of the main results of the paper is a simulation study to get at the predictive ability of the Scheffé, Kronecker, and Slack variable models after model reduction—the real outcome that is important in industry. Kang, Salgado, and Brenneman (to appear) shows that the Slack variable model performs as good as or better than the Scheffé and Kronecker models, especially when there is a filler variable (i.e., when the nonfiller ingredients can be manipulated independently from one another).

Example 5: Data Science in the Consumer Complaints System at P&G

This final example is not related to new statistical research; we are too new to this area to be able to contribute. But this showcases how those practicing make use of published research papers and other sources (e.g., free-ware for text mining) to get the job done. We hope to gain enough knowledge in this area so that in the future we will be “aware” of new problems. Right now we just want to be aware of existing techniques to apply! So we are utilizing the top half of the research cycle, “Perfect Makes Practice.”

As a large consumer products company, we want to make use of the consumer feedback as it contains rich insights that can be incorporated into designing and producing highly desirable products. In addition, regulators expect that complaints are managed appropriately and that an end-to-end system to capture, extract, analyze, and act upon complaints is good business.

We have used data science to drive the signal detection and evaluation phase of the consumer complaints system through state-of-the-art data mining techniques. We have also used text mining techniques to improve the data quality through (1) the identification of misclassified adverse events and critical product quality complaints and (2) fraud detection and removal prior to downstream processes. As we build our knowledge in data science around what is already available in the literature and open source software, we hope at some point to be able to contribute to the body of new research or new software.

Figure 6: Interaction plot of the hard and soft skills needed for success in statistical practice

Final Thoughts About Practicing Statistics I would like to leave the readers with a few thoughts around the skills needed to be effective in statistical practice. Hard skills like understanding statistical theory and having a strong ability to do statistical computing are the backbone and are absolutely essential for being effective in statistical practice. However, the soft skills like communication, conflict resolution, responsibility, integrity, and honesty are also critical for success. I have often thought that having strong hard skills will always win out, but that there is an interaction between the soft and hard skills as depicted in Figure 6. When one’s hard skills are low, effective statistical practice changes slightly with stronger soft skills. But when one’s hard skills are high, effective statistical practice changes dramatically with increased soft skills. We who are practicing statistics strive to be in the upper right-hand corner, but without a doubt, the hard (perfect) skills are the foundation and underlying driver of success.

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I hope that through this discussion I have motivated those in practice to actively find problems that come your way that have research potential and reach out to our academic friends to collaborate on those problems. Only by actively taking a role in the research cycle can we in industry and government have a way to drive the direction of academic research. And for those in academics trying to reach out to those practicing statistics, remember that often the problems we face are messy and often don’t fit into the easy assumptions (e.g., normality or independence) that make the theory easy. For as Yogi Berra once said, “In theory there is no difference between theory and practice. In practice there is.”

References Brenneman, W.A. and Joner, M.D., 2012, “Setting appropriate fill weight targets – a statistical engineering case study,” Quality Engineering, 24(2), 241-250.

Brenneman, W.A. and Myers, W.R., 2003, “Robust Parameter Design with Categorical Noise Variables,” Journal of Quality Technology, 35(4), 335-341.

Cornell, J. A. (1992), “W.J. Youden – The Man and His Methodology,” ASQ Statistics Division Newsletter, Vol. 13, No. 2, 9-18.

Elder, R. S., and Muse, D. H., 1982, “An Approximate Method of Evaluating Mixed Sampling Plans,” Technometrics, 24, 207–211.

Kang, L., Joseph, V.R. and Brenneman, W.A., 2011, “Design and Modeling Strategies for Mixture-of-Mixtures Experiments,” Technometrics, 53(2), 125-136.

Kang, L., Salgado, J.C. and Brenneman, W.A. (to appear). “Comparing the Slack-Variable Mixture Model with Other Alternatives,” Technometrics. DOI:10.1080/00401706.2014.985389

Lasater, H.A. editor (1972), William John Youden, 1900-1971. Journal of Quality Technology, 4(1), 1-67.

Linkletter, C.D, Ranjan, P., Lin, C.D., Bingham, D.R., Brenneman, W.A., Lockhart, R.A. and Loughin, T.M., 2012, “Compliance Testing for Random Effects Models with Joint Acceptance Criteria,” Technometrics, 54(3), 243-255.

Robinson, T.J., Brenneman, W.A. and Myers, W.R., 2006, “Process Optimization via Robust Parameter Design when Categorical Noise Variables are Present,” Journal of Quality and Reliability Engineering International, 22(3), 307-320.

Steinberg, D. M. (to appear), “Industrial Statistics: The Challenges and the Research,” Quality Engineering.

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2016 World Conference on Quality and ImprovementWhen and Where:

• May 16 – 18, 2016

• The Wisconsin Center, Milwaukee, WI

• Website: asq.org/wcqi

Focus Areas:• Building and Sustaining a Culture of Quality

• Quality Fundamentals

• The Quality Profession (Today and Tomorrow)

• Leadership Risk and Change

• Customer Experience

Meet CPID• Statistics Division/CPID social at

Rock Bottom Restaurant and Brewery on Monday, May 16, from 6:00 p.m. – 8:00 p.m. Address: 740 N. Plankinton Ave.

• CPID open member meeting: Sunday, May 15, 2:00 p.m., location: Golda Meir Room, Hilton Milwaukee City Center, 5th Floor

• CPID exhibit hall booth: 338

• Exhibitor show dates: Sunday, May 15 – Tuesday, May 17, 2016

Exhibitor Show TimesDay Date TimeSunday May 15 6:30 p.m. – 8:30 p.m.

Monday May 16 9:00 a.m. – 5:00 p.m.

Tuesday May 17 9:00 a.m. – 4:00 p.m.

ProgramWith more than 100 sessions focusing on the 2016 theme—Quality Expanded: In the Profession, Through the Organization, and Around the World—browse through the program by day, level, focus area, and session type to find exactly what you need. Then create your own personal schedule to customize your conference experience! The program is categorized according to level: Basic, Intermediate, and Advanced; and according to session type: Concurrent Session, Workshop, Flip Session, and After 5 Session. You can find the full program at:asq.org/wcqi/program.aspx

2016 WCQI Pricing details can be found at: asq.org/wcqi/2016/pricing.aspx

http://asq.org/wcqi

http://asq.org/wcqi/program.aspx

http://asq.org/wcqi/2016/pricing.aspx

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Keynote Speakers

Monday, May 16, 8:00 a.m. – 9:45 a.m.

Stephen J. Dubner

Award-Winning Author, Journalist, and Radio and TV Personality

Monday, May 16, 4:15 p.m. – 5:15 p.m.

James Kane

Renowned Loyalty Expert and Author

Tuesday, May 17, 8:00 a.m. – 9:00 a.m.

Liz Wiseman

Best-Selling Author, Speaker, and Executive Advisor

Tuesday, May 17, 1:15 p.m. – 2:15 p.m.

Brian Little

Psychology Scholar and Best-Selling Author

Wednesday, May 18, 10:30 a.m. – Noon

Josh Linkner

Four-Time Tech Entrepreneur, Hyper-Growth

CEO, New York Times Best-Selling Author,

Venture Capitalist, and Speaker

Training/Courses

The following training courses are available in connection to the conference. More information about course content, pricing, and schedule is available at the conference website.

• Certified Quality Auditor Certification Preparation

• Certified Manager of Quality/Organizational Excellence Certification Preparation

• ISO 9001:2015 – The Path Forward

• Lean Enterprise

• Auditing for Improvement

• ASQ’s Quality 101

• Risk Management Essentials and Implementation Strategies

• Lean Kaizen: A Simplified Approach to Process Improvement

• Toyota Kata: “Improving and Coaching Your Way to Success”

WCQI 2016 Sponsors

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Welcome to the 60th Annual Fall Technical Conference, 2016!

The 60th Fall Technical Conference will be held in Minneapolis, MN, October 6 – 7, 2016. This year, the theme of the conference is “Statistics and Quality: Twin Pillars of Excellence.” The program committee has put together an outstanding group of sessions including topics in design of experiments, measurement assessment, statistical process control, modeling, and simulation. Also, the program will include another great SPES special session on Friday afternoon - Leadership Perspectives: A Multifaceted Discussion. Please visit the conference website at asq.org/conferences/fall-technical for information on the program, hotel, short courses, and Minneapolis. Early-bird registration rates are in effect until September 10. Please register early!

Short CoursesIn addition to the conference program, the short course offerings include: “Methods for Designing and Analyzing Mixture Experiments,” by Greg Piepel, MIXSOFT. This course will provide an overview of various approaches and methods used in designing mixture experiments and analyzing the resulting data.

“Analysis of Big Data using R,” by Brian Reich and Eric Laber, North Carolina State University. This course starts with an introduction to the open-source language R, and then provides an overview of foundational statistical methods required to analyze big data. No prior exposure to R required.

“Design and Analysis of Experiments with R,” by John Lawson, Brigham Young University. This course will cover variance components, traditional and modern screening designs, along with factorial, response surface, and mixture experiments. It will also include split-plot and optimal designs. Participants should be familiar with R.

“Beyond Split-Plot Design and Analysis,” by Peter Goos, University of Antwerp and Erasmus University Rotterdam. This course starts with a brief review of standard split-plot designs and then expands to split-split-plot and strip-plot designs. Split-plots involving an additional blocking factor, mixture-process variable and mixture-of-mixture experiments will also be discussed.

Statistics and Quality: Twin Pillars of ExcellenceOctober 6 – 7, 2016 @ Courtyard by Marriott, Minneapolis, MN

http://asq.org/conferences/fall-technical

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Accommodations: A block of rooms at the Courtyard by Marriott Downtown Minneapolis is available at a special FTC rate through September 13. Use the link on the FTC website to make your reservations online.

I look forward to welcoming you all to Minneapolis!

Shari KraberFTC Chair, 2016

Please visit the conference website for complete information on the program, hotel, and short courses.asq.org/conferences/fall-technical

CPID remembers Connie Borror, past chair and longtime member of CPID. Connie, a professor at Arizona State University West, passed away April 10 after a hard-fought battle with breast cancer. Connie was an extraordinary colleague, mentor, teacher, and friend. She will be missed.

General FTC ChairShari KraberStat-Ease, [email protected]

Program CommitteeASA: Q&PJohn Szarka (Chair)W.L. Gore & [email protected]

ASA: SPESGreg SteenoPfizer, [email protected]

ASQ: CPIDStephanie DeHartEastman Chemical [email protected]

ASQ: STATMindy HotchkissAerojet [email protected]

In Remembrance of Connie Borror

http://asq.org/conferences/fall-technical






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Nominations Sought for 2016 William G. Hunter Award

William G. Hunter was the founding chair of the Statistics Division of the American Society for Quality Control (now ASQ). His leadership as a communicator, consultant, educator, innovator, and his ability to integrate statistical thinking into many disciplines serve as exemplary models for the division’s members.

Objective: The Statistics Division established the William G. Hunter Award in 1987 to encourage and promote outstanding accomplishments during a career in the broad field of applied statistics, and to recognize implementers who get results.

Qualifications: Any outstanding leader in the field of applied statistics, regardless of ASQ or ASQ Statistics Division membership status, is qualified. Candidates must have demonstrated a high level of professionalism, significant contributions to the field, and a history of inspirational leadership. A person may be nominated many times, but can win the award only once.

Procedure: The nominator must have the permission of the person being nominated and letters from at least two other people supporting the nomination. Claims of accomplishments must be supported with objective evidence. Examples include publication lists and letters from peers. Nominators are encouraged to read the accompanying article, “William G. Hunter: An Innovator and Catalyst for Quality Improvement,” written by George Box in 1993 (http://williamghunter.net/george-box-articles/william-hunter-an-innovator-and-catalyst-for-quality-improvement) to get a better idea of the characteristics this award seeks to recognize.

Nominations: Nominations for the current year will be accepted until June 30. Those received following June 30 will be held until the following year. A committee of past leaders of the Statistics Division selects the winner. The award is presented at the Fall Technical Conference in October.

The award criteria and nomination form can be downloaded from the “Awards” page of the ASQ Statistics Division website (http://asq.org/statistics/about/awards-statistics.html) or may be obtained by contacting Necip Doganaksoy ([email protected]).

The ASQ Statistics Division is pleased to announce that nominations are now open for its William G. Hunter Award for 2016.

http://williamghunter.net/george-box-articles/william

http://williamghunter.net/george-box-articles/william

http://asq.org/statistics/about/awards-statistics.html


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LEADERSHIP POSITION NAME EMAIL

Chair Flor Castillo [email protected]

Chair-Elect Anne Driscoll [email protected]

Secretary Jennifer Van Mullekom [email protected]

Treasurer Ashley (Nelson) Childress [email protected]

Nominating Chair (Past Chair) Brooke Marshall [email protected]

Audit Chair (Past Chair 2) Stephanie DeHart [email protected]

Examining Committee Chair Malcolm Hazel [email protected]

Membership Chair/VoC Chair Jennifer Kensler [email protected]

Communications Chair (Newsletter/Internet) Sarah Burke [email protected]

Website Manager Sarah Burke [email protected]

Education Chair Dotty Sempolinski [email protected]

Shewell Awards Chair Malcolm Hazel [email protected]

Wilcoxon/Youden Awards Chair Bill Notz [email protected]

Standards Committee Rep Brenda Bishop [email protected]

FTC Steering Committee Rep Kevin White [email protected]

FTC 2016 General Chair Shari Kraber [email protected]

FTC 2016 Technical Program Rep Stephanie DeHart [email protected]

FTC 2016 Short Course Rep Maria Weese [email protected]

Call for Nominations The Chemical and Process Industries Division announces a call for nominations for the elected positions of chair-elect, secretary, and treasurer. The term of these elected positions is from January 1, 2017, through December 31, 2017.

Nominations from the general division membership require the submission of a nomination petition, signed by at least 10 regular members and should be submitted to Jennifer Van Mullekom, CPID secretary ([email protected]) by Friday, September 16, 2016.

If you have any questions regarding this nomination, call. Or if you are interested in a non-elected position, please contact Brooke Marshall, CPID nomination chair ([email protected]).

Newsletter Editor: Sarah Burke ([email protected])

CPID Leadership, 2016

CPID’s Mission: The Chemical and Process Industries Division facilitates the interchange of ideas, tools, practices, and solutions necessary to address real chemical and process industry problems.

CPID’s Vision: To be the customer-centered volunteer organization which is the primary and most credible global resource for quality improvement and management practices in the Chemical and Process Industries.























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