Volume 16, No. 2 Winter, 1996 Chair’s Message Basic...

Chair’s Messageby Nancy Belunis

The StatisticsDivision wasawarded divisionstatus over 16years ago in thesummer of 1979.One of themajor projects ofthe new Divisionwas the devel-

opment of The Basic References inQuality Control: StatisticalTechniques, also known as the “HowTo” series. The aim of the series “isto present the latest statistical tech-niques in a form which is easily fol-lowed by the quality control practi-tioner so that these procedures can bereadily applied to solve industrialquality problems.” The first volumein this series, How To Analyze Datawith Simple Plots, appeared in 1978while the Division was still aTechnical Committee. At present, theseries contains 16 volumes and coverstopics like sampling, mixture experi-ments, reliability data, outlier testingand sequential methods. In theseries’ history there have only beenfour editors, Ed Dudewicz, SamShapiro, John Cornell and EdMykytka. With the assistance of theseeditors, the authors have driven thecreation of a successful series.

Volume 16, No. 2 Winter, 1996

©

Basic Reference in Quality Control:Statistical Techniques1

Continued on page 3

Volume 1: How To Analyze Datawith Simple PlotsWayne Nelson

Volume 2: How to PerformContinuous SamplingKenneth S. Stephens

Volume 3: How to Test Normalityand Other DistributionalAssumptionsSamuel S. Shapiro

Volume 4: How to Perform Skip-Lotand Chain SamplingKenneth S. Stephens

Volume 5: How to Run MixtureExperiments for Product QualityJohn A. Cornell

Volume 6: How to AnalyzeReliability DataWayne Nelson

Volume 7: How and When toPerform Bayesian AcceptanceSamplingThomas W. Calvin

Volume 8: How to Apply ResponseSurface MethodologyJohn A. Cornell

Volume 9: How to Use RegressionAnalysis in Quality ControlDouglas C. Crocker

Volume 10: How to Plan anAccelerated Life Test-Some PracticalGuidelinesWilliam Q. Meeker and Gerald J.Hahn

Volume 11: How to PerformStatistical Tolerance AnalysisNeil D. Cox

Volume 12: How to Choose theProper Sample SizeGary G. Brush

Volume 13: How to Use SequentialStatistical MethodsThomas P. McWilliams

Volume 14: How to ConstructFractional Factorial ExperimentsRichard F. Gunst and Robert L.Mason

Volume 15: How to DetermineSample Size and Estimate FailureRate in Life TestingEduardo C. Moura

Volume 16: How to Detect andHandle OutliersBoris Iglewicz and David C.Hoaglin

1Available through ASQC Quality Pressat 800-248-1946

2 ASQC STATISTICS DIVISION NEWSLETTER, VOL. 16, NO. 2

VISION- Our customers’ needs will be con-tinuously anticipated and met.

- Our members will be proud to be apart of the Division.

- Our Division’s operations will be amodel for other organizations.

- We will be a widely influentialauthority on scientific approaches toquality and productivity improve-ment.

MISSION- Promote statistical thinking for qual-ity and productivity improvement.

- Serve ASQC, business, industry,academia and government as aresource for effective use of statisti-cal methods for quality and produc-tivity improvement.

- Provide a focal point within ASQCfor problem-driven developmentand effective use of new statisticalmethods.

- Support the growth and develop-ment of Division members.

STRATEGY- Our primary customers are StatisticsDivision members. Other key cus-tomers are:

- Management,- Users and potential users of sta-tistical methods for quality andproductivity improvement,

- Educators of the above cus-tomers.

- Our orientation to customers is cus-tomer focused.

- Our markets, within which weintend to offer products, are weight-ed as follows: greatest weight onintermediate statistical methods,nearly as much weight on basicmethods, and much less weight onadvanced methods.

- Our primary products are educa-tional services.

PRINCIPLES- Focus on a few key things.- Balance short-term and long-termefforts.

- Recognize that we exist for our cus-tomers.

- Value diversity (including geograph-ical and occupational) of our mem-bership.

- Be proactive.- View statistics from the broad viewof quality management.

- Apply statistical thinking ourselves(that is, practice what we preach).

- Uphold professional ethics- Continuously improve

Over the next several months, many of you will have the opportunity tospeak to Falguni Sharma. In her new role as the Acquisitions Coordinator forthe Statistics Division, Falguni’s main responsibility is to develop a list ofpotential topics consistent with the needs of Division customers, and to iden-tify potential authors for Division Publications. Please support her efforts.

Readers should consider submitting either a basic tools or mini paper forpublication. The criteria for the basic tools and mini paper columns appearon the following page. Papers can be submitted with one hard copy andone copy on a 3-1/2” diskette. Since I use Microsoft Word for Windows, thefiles should be sent in either Microsoft Word, ASCII Text File, orWordPerfect. Figures should be properly identified and labeled. (Please donot embed figures in the text.)

You will notice that there is no deadline for the Spring 1996 issue. In theplace of the Spring newsletter, the Statistics Division will present a specialpublication on Statistical Thinking. The edition will focus on StatisticalThinking concepts as presentation by Lynne Hare, Roger Hoerl and Ron Sneeat the 1995 AQC Conference in Cincinnati.

Janice ∆

Editor’s Corner

Inside This Issue

Letters........................................................................p. 4

Call for New Regional Councilors ...........................p. 4

Youden Address .......................................................p. 6

Tactical Planning Meeting......................................p. 14

Annual Quality Congress Activities .......................p. 16

Hunter Award .........................................................p. 19

Job Openings..........................................................p. 20

Deadline for Newsletter Contributions .................p. 24

ASQC STATISTICS DIVISION NEWSLETTER, VOL. 16, NO. 2 3

For the last few years, the Divisionhas been discussing improvements tothe series. We needed to developexplicit strategic and tactical plans toguide the future activities of the “HowTo” series. Specific considerationwere the types, levels and formats ofproducts and its audience and futuretopics. This was broken into smallertactical plans that could be completedwithin a year. The first plan was todocument and improve “How To”series infrastructure. At the same timethat Ed Mykytka was putting togethera straw-proposal for this plan, theDivision was looking at revising theirbylaws. The Model Division bylawsincluded a publications committee asa standing committee. While theDivision had several publications, wedid not have a formal infrastructurelinking them together or an avenue forother publication areas such as brief-ings. Hence, the birth of thePublications Committee.

The groundwork for this committeewas laid by the tactical planning teamlast Spring. The committee consists ofPublications Committee Chair, “HowTo” Series Editors, Briefings Editor,Glossary Editor, AcquisitionsCoordinator and in the future, NewProducts Coordinator. The NewsletterEditor is an ad-hoc member of thiscommittee.

Ed Mykytka has decided to retirefrom the role of “How To” SeriesEditor. We thank him for his time anddedication to the series. Replacing Edas “How To” Series Editors are WalterLiggett and Bob Brill. Bob and Walterare already hard at work in their newroles. They along with the rest of thePublications Committee met duringthe Tactical Planning Meeting at theFall Technical Conference (FTC) andestablished short-term and long-termgoals. You will find articles discussingthis meeting as well as other itemsfrom the FTC throughout the news-letter.

I would like to discuss in furtherdetail the role of regional councilors.The regional councilor serves as aliaison between the sections and theDivision. If you have looked at a mapof the regions, you will quickly realizethat we are asking one person to beresponsible for a large number of sec-tions. We believe that the role ofregional councilor is important sinceworking with sections is one way forthe Division to be closer to you, ourmembers. The General TechnicalCouncil, the overseeing body for alldivisions, realized the need to allowus to modify our bylaws with regardto regional councilors. The StatisticsDivision Council voted to change theposition of regional councilor fromelected to appointed. This will allowus to appoint more than one councilorper region. Bob Mitchell, ourMembership Chair, is leading a tacticalplanning team to work on defining therole of these councilors. If you haveany ideas on how a Statistics Divisioncouncilor might work with sections tomeet your needs, please contact Bob.

Finally, I want to thank NickMartino and Jacob Van Bowen as wellas the entire FTC committee for theirefforts in making the 1995 FTC a suc-cess. Nick served as Short CourseChair and organized short courses onresponse surface methodology anddecision and risk analysis. Van servedas Program Committee Representativeand was responsible for working withrepresentatives from the other spon-soring organizations to put togetherthe program. Van is serving in a two-year slot and will be chairing the 1996program committee. There are cur-rently opportunities for individuals toserve in similar roles for the Divisionin upcoming years. If you are inter-ested, please see the article discussingjob openings and complete the mem-ber interest form. ∆

CHAIR’S MESSAGEContinued from front

Criteria for BasicTools and Mini-Paper

ColumnsBasic Tools

Purpose: To inform/teach the “qualitypractitioner” about useful techniquesthat can be easily understood, appliedand explained to others.

Criteria:1. Application oriented/not theory2. Non-technical in nature3. Techniques that can be understood

and applied by non-statisticians.4. Approximately three to five pages or

less in length (8 1/2” x 11” typewrit-ten, single spaced.)

5. Should be presented in “how to useit” fashion.

6. Should include applicable examples.

Possible Topics:New SPC techniquesGraphical techniquesStatistical thinking principles“Rehash” established methods

Mini-PaperPurpose: To provide insight into appli-cation-oriented techniques of signifi-cant value to quality professionals.

Criteria:1. Application oriented.2. More technical than Basic Tools, but

contains no mathematical deriva-tions.

3. Focus is on insight into why a tech-nique is of value.

4. Approximately six to eight pages orless in length (8 1/2” x 11” typewrit-ten, single spaced.)Longer articles may be submittedand published in two parts.

5. Not overly controversial.6. Should include applicable examples.

General InformationAuthors should have a conceptual

understanding of the topic and shouldbe willing to answer questions relatingto the article through the newsletter.Authors do not have to be members ofthe Statistics Division.

Submissions may be made at anytime to the Statistics DivisionNewsletter Editor. All articles will bereviewed. The editor reserves discre-tionary right in determination of whicharticles are published.

Acceptance of articles does notimply any agreement that a given arti-cle will be published.

∆


Dear Janice,Please pass on my appreciation to

Stu (Hunter) for the article on therobustness of the EWMA. I do haveone comment that may be relevant. Ibelieve in Table 4 of the article, thecolumn labeled “Yt+1” should reallybe labeled “Yt”, and that’s read: “Yforecasted for period t, made at periodt-1.” Otherwise the math and rowsdon’t line up.

The control algorithm on page 10 iskind of hard to follow.....e.g. the tab

misalignment in the 2nd row, and Ithink the 1st “44.5” under New Obs.should be under the EWMA column.Trying to follow it is somewhat “userhostile”.

Alex T.C. LauImperial Oil

Toronto, Ontario, Canada

Thanks for your input. I con-tacted Larry and Stu for clarifica-tion. Their input will be publishedin a later edition.

Letters to the Editor

The 13th Quality and ProductivityResearch Conference and the 3rdSpring Research Conference onStatistics in Industry and Technologywill be held jointly at the NationalInstitute of Standards and Technology(NIST) in Gaithersburg, MD (a suburbof Washington, DC) from May 29through May 31, 1996. The goal ofthis conference is to stimulate interdis-ciplinary research among statisticians,engineers, and physical scientists inquality and productivity, industrialneeds, and the physical and engineer-ing sciences. The conference is jointlysponsored by the ASA Section onPhysical and Engineering Sciences, theASA Section on Quality andProductivity, IMS, E. I. du Pont deNemours & Co. and NIST.

The conference will feature presen-tations by scientists and engineers forstatisticians and presentations by sta-tisticians for scientists and engineers.Statistical issues and researchapproaches drawn from collaborativeresearch will be highlighted.

Professor Vijay Nair, from theUniversity of Michigan, and ProfessorWilliam Golomski, from the Universityof Chicago Business School, will beplenary speakers. They will discussbroad issues and opportunities ininterdisciplinary research from differ-ent perspectives. There will also beboth invited and contributed papersessions.

For information on submitting con-tributed papers, contact Will Guthrie([email protected] or (301) 975-2854). The deadline for submittingcontributed papers is February 1,1996.

For further information about theconference, check the NIST StatisticalEngineering Division Home Page(http://www.cam.nist.gov/caml/sed/)or contact one of the conference co-chairs: Eric Lagergren([email protected] or 301-975-3245)or Raghu Kacker([email protected] or 301-975-2109). ∆

Related EventsJOINT RESEARCH CONFERENCE ON STATISTICS IN QUALITY,

INDUSTRY AND TECHNOLOGY

Call for NewRegional Councilors

The Statistics Division has openingsfor two Regional Councilors, in region#5 (Pennsylvania, Delaware andMaryland) and in region #9 (Indiana,Kentucky, and northwestern parts ofOhio). If you would like to becomemore involved in the Statistics Divisionand enjoy networking with fellow sta-tistical practitioners, we would like tohear from you.

At the October 18 Statistics DivisionCouncil meeting, a change to theDivision bylaws was approved thatallows for more flexibility in appoint-ing as many councilors as needed toaddress the needs of our membersand customers, The appointedRegional Councilor is a non-votingmember of the Statistics DivisionCouncil. Regional Councilors serve asa link between the Division and theASQC Sections. An excerpt of theexisting job description is summarizedbelow.

Purpose:To serve as section liaison for the

Statistics Division.

Responsibilities:Participate with the Statistics

Division Council to determine howthe local sections can help implementDivision tactical plans, communicateDivision news, and seek active mem-ber participation.

Contact Section Chairs to determinehow the Statistics Division can helpprovide speakers, communicateevents, schedule short courses, etc.

Exhibit the Statistics Division dis-play booth at local section meetings,disseminate pamphlets and brochures,seek new members.

Interested members are asked to fillout the enclosed “Member InterestRecord Form” and/or contact: BobMitchell, Membership CommitteeChair, (612) 234-1864 ∆


ASQC STATISTICS DIVISION1995 -1996

REGIONAL COUNCILORSRegional Councilor Coordinator

Bob Mitchell3M Consumer Products Plant

915 Highway #22 SouthHutchinson, MN 55350Phone: (612) 234-1864

Region 1: Region 6: Region 11:Bob Gillies Marilyn Hwan George Marrah32 Dartmouth Drive LSI Logic, MS J-202 Department of MathematicsMystic, CT 06355 3115 Alfred Street & Computer SciencePhone: (203) 445-3145 Santa Clara, CA 95050 James Madison University

Phone: (408) 433-6362 Harrisburg, VA 22807Phone: (703) 568-6534

Region 2: Region 7: Region 12:Mary Garfield Tom Vaden Bob Dovich205 Bryant Street Consultant Ingersoll Cutting Tool CompanyRochester, NY 14613 5765 Grand Avenue 505 Fulton AvenuePhone: (716) 722-2392 Riverside, CA 92504 Rockford, IL 61103

Phone: (714) 382-5525 Phone: (815) 987-6542

Region 3: Region 8: Region 13:Rich Christy Bill Bleau Rick SchleusenerAmerican Premier, Inc. Picker International Inc. Kodak Colorado DivisionP. O. Box 1569 1130 Stonecrest Dr Building C-42, 3rd Floor901 East 8th Avenue Tallmadge, OH 44278 Windsor, CO 80551-1672King of Prussia, PA 19406 Phone: (216) 473-2385 Phone: (303) 686-4530Phone: (215) 337-1100

Region 4: Region 9: Region 14:Michael Cohen Vacant Oz GodseySatisfied Brake Products 303 Ridgebriar Drive650 100th Avenue Richardson, TX 75080Chomedy Laval Phone (214) 690-1744Quebec, Canada H7W-3Z6Phone: (514) 337-3280

Region 5: Region 10: Region 15:Vacant Greg Gruska Dan Dankovic

The Third Generation, Inc. Westinghouse Electric4439 Rolling Pine Drive CorporationWest Bloomfield, MI 48323 700 Energy LanePhone: (313) 363-1654 Fort Payne, AL 35967

Phone: (205) 845-9601, Ext. 6033


USING ON-LINE PROCESS DATA TO IMPROVE QUALITYIs There a Role for Statisticians?Are They Up for the Challenge?

John F. MacGregorDept. of Chemical Engineering

McMaster UniversityHamilton, ON, Canada L88 4L7

ABSTRACTIn keeping with the conference theme of “Tapping Diverse Data Sources to Improve Quality” this paper presents a

case for using the very large number of process variables measured routinely by on-line computers. Multivariate statisti-cal methods can be used to project the data down into low dimensional spaces where analysis, monitoring and diagnosisare easily performed. Strong justifications for taking this approach are presented, and several examples are given.

The statistical process control community has been slow in picking up on the data explosion brought about by thecomputer era. It has continued to stick with traditional univariate charts on the quality variables, and ignored this richsource of additional information on the process. This paper explores some of the reasons for this and argues that theSPC community must adapt rapidly to this multivariate reality or lose control of their field to the scientists and engineers.

INTRODUCTIONThe period of Jack Youden’s lifetime (to 1972) represents, in my opinion, the golden years of applied statistics.

Statistical methods and thinking were introduced into nearly all disciplines of science and engineering by people such asFisher, Youden, Box and Tukey. These “statisticians” were primarily scientists themselves - geneticists, chemists, engi-neers. They understood the way scientists and engineers thought, and they understood their problems. As a result theydeveloped statistical methods to treat these real world problems, not by starting with the statistical theory and trying tofind a problem it could treat, but by starting from the very real problems and developing methods to treat them.

The last two decades have seen statistics grow as a mathematical discipline. However, this period has seen much lessinteresting growth in applied statistics, not because there were no new problems, but because the leadership in the statis-tical disciplines passed on to a new generation of mathematical statisticians.

My belief is that we are once again seeing a major shift in the leadership and direction of the statistical community.This new era is being ushered in by the advent of on-line process, laboratory, and management computer systems.These have totally changed the nature of the data we are seeing. We now live in a highly multivariate, data-rich society,and are being inundated with data from all directions. The small sample univariate methods of the past are hard pressedto handle this new situation. This has opened the door to the need for an explosion of new statistical methods to treatthe new problems associated with large volumes of messy data that are being collected routinely every second, minute orhour. These massive data sets and the problems with interpreting them arise in all areas where computers have penetrat-ed - communications, image and speech analysis, management information systems, chemistry, the process industries,etc. The leadership in developing statistical methods for these data rich problems has again returned to the owners ofthe problems - scientists and engineers - paralleling the earlier era of applied statistics when scientists such as Youdendeveloped methods to treat the problems in their areas of science.

The theme of this conference - “Tapping Diverse Data Sources to Improve Quality” - has motivated the main topic ofthis address - “Using On-Line Process Data to Improve Quality”. Underthis topic I will discuss some new multivariate approaches to SPC whichacknowledge that on-line process computers have become commonplace,and that huge amounts of process data are being collected on a routinebasis. A secondary theme concerning the role that statisticians might playin these developing areas is then explored.

The SPC ProblemTo illustrate the problem of monitoring process performance and prod-

uct quality, and diagnosing assignable causes for special events, considerthe process situation depicted in Figure 1. Process computers routinely

Continued on page 7

Youden Address

Figure 1


YOUDEN ADDRESSContinued from page 6

collect measurements on a large number of process variables such as flows, temperatures, pressures, etc. Let us denotethese process variables by xj; j = 1, 2, ... ,k, and let the (N x k) matrix X contain observations on them over some timeperiod (i = 1, 2, ...,N). In addition to these frequently measured process variables we also usually measure several qual-ity and productivity variables ( ; = 1, 2, ...,m). These variables are often measured much less frequently on samplescollected and analyzed off-line in a quality control lab.

Traditional approaches to statistical process/quality control established a set of univariate control charts on each of thequality variables (y ). Occasionally multivariate extensions of these charts based on Hotelling’s T2 statistic areemployed on a small number of the most highly correlated y’s. The process data (X) are almost never used directly inmonitoring the process, but only in searching for an assignable cause once one of the control charts on the y’s has indi-cated the presence of a special event.

(iv) Missing DataMost large process data sets contain missing data (sometimes up to 20%). Therefore, if we are to analyze such

process data, and if we are to establish multivariate control charts to monitor the future behavior of the process, then the

Projection to Latent Structures (PLS) is another multivariate projection method which can be applied when onehas two data matrices - a process data matrix (X) and a quality data matrix (Y). Now one is interested in the high vari-ance directions in the process data (X), but more specifically the high variance directions in X that are related to the vari-ation in the quality data Y. In the first dimension PLS accomplishes this by extracting that linear combination of theprocess variables t1 = w1

T x which maximizes the covariance of X and Y. Subsequent PLS latent variables (t2, t3...) areagain required to be orthogonal to preceding ones. Latent variables (ui = ci

T y) are also obtained for the Y space whichare most correlated with their corresponding ti’s. The analysis, interpretation, and monitoring can again be more easilyperformed in the reduced dimension defined by the orthogonal latent variables (ti), but unlike PCA, PLS is focusing atten-tion on that variation in the process variables that is most explanative of the quality data.

Problems with Traditional ApproachesAlthough such an approach was well suited to information-poor systems of past decades, there are two major flaws in

it when applied to today’s information-rich systems.

(i) The Multivariate Nature of QualityThe first problem is that product quality is a multivariate property and must be treated as such. By this I mean

that a high quality product must simultaneously have the right combination of all the individual y’s. Each individual yhas little meaning by itself. Furthermore, with the improvement in measurement and material characterization methods, itis now common to obtain many more quality measures on a product. These additional quality measures are usuallyhighly correlated. For example with synthetic fibers up to 10 or more quality variables are typically measured, but aPrincipal Component Analysis (PCA) often reveals that they are so highly correlated that the true dimension of the qualityspace is more likely about four. In such situations monitoring univariate control data on all the individual y’s can be verymisleading. This is illustrated in Figure 2 with two correlated y’s (correlation coefficient ≈ 0.9). The point indicated bythe x, although well within the control limits on the individual charts, is clearly very unusual when plotted in the multi-variate space. It is unlikely that product with this unusual combination of y’s would perform in the same manner as theother product in a customer’s end use application. Extending the univariate quality control charts to multivariate onesbased on Hotelling’s T2 would normally handle this problem well as long as the dimension is not too large. Any point xlying outside the elliptical contour in Fig. 2 would be detected as unusual by multivariate T2 charts.

It is surprising that more multivariate charts are not advocated by applied statistics and SPC groups. This is in sharpcontrast to the current practice in the design of experiments (DOE) where, horrified by the desire of some scientists andengineers to perform experiments on one variable at a time, statisticians have been quite successful in introducing theideas of designed experiments in which many variables are changed simultaneously. And yet many of these same statisti-cians appear to be content to meet with quality control groups and recommend that factors be monitored one at a timevia univariate charts, thereby leading to the situation depicted in Figure 2. Is not the use of univariate SPC charts in mul-tivariate situations directly analogous to one factor at a time experimentation in DOE? The presence of variable interac-tions in DOE leads to the same difficulties in interpreting the results of one factor at a time experimentation as does thepresence of correlation among variables in interpreting univariate SPC charts. Perhaps, as applied statisticians, we shouldtry to be more consistent in our approaches, or at least explain the reasons for our lack of consistency if we are to beconsidered credible by those we are advising.

Continued on page 8


(ii) Ignoring the Process DataThe second, and by far the most important problem with current SPC approaches, is that they ignore most of the

available data - namely the massive amounts of data that are routinely collected on the process variables (X). Theseprocess data (X) must play an extremely important role in SPC for the following reasons.

(a) Many more process variables (x’s) are usually measured than quality variables (y’s). In chemical processes it isnot uncommon for the ratio of process variables to quality variables to be in the order of a hundred to one.

(b) The process variables are almost invariably measured much more frequently, usually in the order of seconds orminutes as compared to hours for the quality variables.

(c) The process variables are measured on-line by process computers as opposed to quality variables which are usu-ally measured off-line in a quality control laboratory. As a result the process variables are available instanta-neously while the quality variables are often available only after a considerable delay (often several hours).

(d) The process variables such as temperatures, pressures, flows, etc. are usually measured more precisely than thequality variables since the latter often involve complex off-line chemical preparation and analysis.

(e) Any faults or special events that occur in the process will leave their fingerprints in the process data as well asthe quality data. It is rarely the case that events affecting product quality will not show up in any of the mea-sured process variables. However, in my experience the converse is sometimes true - that events affecting prod-uct end use quality usually show up in the process variable measurements, but perhaps not in the limited set ofquality measurements (y) made by the manufacturer. This of course implies that the quality data (y) being mea-sured by the manufacturer are an insufficient characterization of end use performance.

(f) Eventually, the process data will be needed in any case. If a fault or special event is detected by a control chart,then one must usually go back to the process data in order to look for an assignable cause. As shown later thistask will be much easier if the X data are used in the control charts in the first phase.

From the above points it should be apparent that both the process data (X) and the product quality data (Y) should beused in any Statistical Process Control scheme. Of the two sets of data (X, Y), in my opinion the process data (X) is usu-ally of much greater value to SPC than the quality data (Y).

Difficulties with Multivariate Process DataHaving made the claim that we should be using all of our process data in SPC schemes, it is also important to

point out that there are many difficulties with these data.

(i) DimensionalityThe first problem is that the dimension of the problem is usually very large. It is not uncommon in the chemical

industry for on-line process computers to measure hundreds or even thousands of process variables every few seconds,and for ten or more quality variables to be measured off-line every few hours. Recent trends in process instrumentationare also allowing for on-line measurement of some quality variables every few minutes. The shear magnitude of theproblem leads to a “DATA OVERLOAD” situation in which, overwhelmed by the massive amount of data, operators andengineers have resorted to following only a few “key” process variables on their computer screens.

(ii) ColinearityEven though hundreds of process variables are measured there are not hundreds of independent events happen-

ing in the process. Only a few underlying common cause events (eg. normal raw material variations, etc.) are usuallyoperating under “in-control” situations and most faults or special events are the result of a single cause or only a fewsimultaneous causes. The hundreds of measured process variables are all related to these small number of underlyingevents, and are therefore highly correlated with one another. Hence, process data is very difficult to use because its “truerank” is very much less than the number of variables measured. However, each measured variable is valuable because itcontains a little different information on each of underlying events.

(iii) NoiseAll of the process variables (as well as the quality variables) are measured with errors (measurement error, sam-

pling error etc.) and the signal-to-noise ratio in any one measured variable is very small during “in-control” operation.This is understandable when one realizes that the objective of operators and process engineers is to make the signal-to-noise ratio as small as possible during normal operating conditions. As a result plotting univariate control charts on manyprocess variables is of little value. However, if each of these process measurements contain a small amount of “signal”and each of them contributes some new information on the underlying events, then by using suitable multivariate statisti-


Continued on page 9


cal methods, we can obtain SPC charts having an extremely high information content or signal on the underlying eventsoccurring in the process.

(iv) Missing DataMost large process data sets contain missing data (sometimes up to 20%). Therefore, if we are to analyse such

process data, and if we are to establish multivariate control charts to monitor the future behavior of the process, then themultivariate methods used must not require that the data on all variables be present at all times. The procedures must beable to handle missing data in a very transparent manner and still extract as efficiently as possible the information fromthe remaining measurements.

In the next sections I describe some multivariate methods capable of addressing the above difficulties, use themto develop multivariate SPC charts, and illustrate their application to process data.

Multivariate Statistical Projection MethodsFor treating large, ill-conditioned data sets that are less than full statistical rank there exists a number of very use-

ful multivariate projection methods. Among them are well known multivariate statistical methods such as PrincipalComponent Analysis (PCA)(Jackson,1991), and Canonical Correlation Analysis (Johnson and Wichern, 1988) as well assome perhaps less well known methods such as Reduced Rank Regression (or Redundancy Analysis) and PLS (PartialLeast Squares or Projection to Latent Structures - Martens and Naes, 1989). All of these methods simplify the data analysisand the subsequent process monitoring problems by projecting the data into low dimensional “latent variable” spaces.Burnham et al. (1996) present an objective function framework that shows how all ofthese methods are related. In this paper I will only be considering PCA and PLS.

PCA is a method extracting the major variance components from a single matrixof data (X). The projection aspects of PCA are well treated by Wold et al. (1987), andare illustrated in Figure 3. After mean centering and scaling X, the first principal com-ponent (t1) is that variable defined as a linear combination of the x variables (t1 = p1

T x)which has greatest variance. The second principal component is that linear combinationt2 = p2

T x with next greatest variance subject to the condition that it is orthogonal to thefirst component. The vectors of observed values of t1 and t2 are referred to as thescores and the coefficient vectors p1 and p2 as the loadings. The scores show the sizeof the variation in any particular multivariate observation, while the elements of loadingvectors show the relative importance of each variable to the corresponding principalcomponent. One can extract as many principal components as one has either variablesor observations. However, in SPC situations the number of statistically significant com-ponents is usually very small (two to five) reflecting the fact that under common causevariation only a small number of events are affecting the process. Cross-validation is often used to determine the numberof dimensions necessary. In Figure 3 the projection aspect of PCA is illustrated assuming that only two components arenecessary. The variation in X explained by the first two principal components is represented by the projection of thedata onto the two dimensional plane defined by the loading vectors p1 and p2. The scores (t1 and t2) define the positionof the projected data points on this plane. If most of the important variation in the data lies in the plane, then one candisplay this plane on the computer screen, and examine the movement of the process in the space of the latent variablest1 and t2. This provides us with a two-dimensional window onto our hundred dimensional process, analogous to ourviewing the projection of a three-dimensional world on a two-dimensional television screen. The analysis, interpretationand monitoring of the process data is thus greatly simplified by working in a much lower dimensional space which con-tains most of the important process variations.

Projection to Latent Structures (PLS) is another multivariate projection method which can be applied when onehas two data matrices - a process data matrix (X) and a quality data matrix (Y). Now one is interested in the high vari-ance directions in the process data (X), but more specifically the high variance directions in X that are related to the vari-ation in the quality data Y. In the first dimension PLS accomplishes this by extracting that linear combination of theprocess variables t1 = w1

T x which maximizes the covariance of X and Y. Subsequent PLS latent variables (t2, t3...) areagain required to be orthogonal to preceding ones. Latent variables (ui = ci

T y) are also obtained for the Y space whichare most correlated with their corresponding ti’s. The analysis, interpretation, and monitoring can again be more easilyperformed in the reduced dimension defined by the orthogonal latent variables (ti), but unlike PCA, PLS is focusing atten-tion on that variation in the process variables that is most explanitive of the quality data.


Continued on page 10

Figure 3

variables

obse

rvat

ions


Multivariate SPCIf one’s objective is to explore a process database or monitor the behavior of a process by using all the available

process data (X) and quality data (Y), then using individual plots and univariate control charts is not a feasible approach.Based on the discussions in the previous sections, an obvious approach is to project the data into the low dimensionalspaces defined by the latent variables (ti’s for the X space) and (ui’s for the Y space). Since the process data is availablemuch more frequently and has many other advantages as discussed earlier, score plots of t1 vs t2, etc. provide the most

useful windows into the behavior of the process.

Exploring Process DatabasesTo illustrate the utility and simplification of such projection plots con-

sider data from an industrial batch polymerization process such as the one illus-trated in Figure 4. The analysis ofbatch processes via multivariate pro-jection methods is discussed inNomikos and MacGregor (1994, 1995)and Kourti et al. (1995). The database from such discontinuous batchprocesses is of the structure shown inFigure 5. For each of the batches (55in this example) data is available on

the raw material data and initial operating conditions (Z), and on the finalproduct quality (Y). Also available are on-line measurements of process vari-

ables, X (temperatures, pressures, agitator power, etc.) over the entire history of the batch.These process variables attempt to track preprogrammed setpoint trajectories. In thisexample the trajectory measurements on 10 variables at 200 time periods was used. Ascore plot for an MPCA model in the space of the second and third latent variables (t2, t3)is shown for the 55 batches in Figure 6. It is clear from this plot that there are several dis-tinct clusters of batches that differ from the main cluster of batches. The main cluster con-tains all those batches that yielded good quality product while the other three clusters showbatches from which poor quality material was obtained, each cluster representing a differ-ent type of fault in the process operation. (Note that the clear distinction between the bot-tom two clusters is apparent in the other latent variable dimension - ie. one of these clus-ters falls well behind the other in that dimension). By examining the loading vectors (wi)

of the model the process variables contributing to the shift in these clusters away from the main “in-control” group canbe determined, thereby suggesting assignable causes for these poor quality batches. In this particular industrial exampleit led the company to make important process modifications to all their batch plants to eliminate the identified source ofthe majorproblem.

Multivariate SPC ChartsOnce this phase of the analysis and exploration of past historical data has been completed, and any assignable

causes for problems that have been observed in the historical data have been corrected, it is desirable to set up SPCcharts to monitor the future behavior of the process. To accomplish this one must built a PCA or PLS model for theprocess when it is operating well and producing only good quality product, and then reference future behavior againstthis model. This is exactly analogous to Shewhart’s SPC philosophy where univariate charts are devised based on amodel for the “in-control” mean and variance of that process variable and future behavior referenced against the model.The only difference in the multivariate case is that we are using all variables (X and Y) simultaneously to develop themodel, and will reference future multivariate data against it. To accomplish this we need to collect a reference data seton the process which we believe represents the various modes of process operation which lead to good product quality.Such a data set might be the central cluster of batches in Fig. 6 of the last example.

On-line multivariate SPC charts which use multivariate projections of the process data (X) and quality data (Y)have been developed for both continuous processes (see for example Kresta et al., 1991; MacGregor et al., 1994;MacGregor and Kourti, 1995), and for batch processes (eg. Nornikos and MacGregor, 1994, 1995; Kourti et al., 1995).Using the “good” reference data a PCA or PLS model is built and the variation in the scores and residuals calculated from



Figure 4

Figure 5

Figure 6

Condenser

Monomers Catalyst Emulsifier

Jacket effluent

Normal batches

Fault I

Fault II

Fault III

Steam

Water


this reference data are used to define control limits in the score space, and in the residual space. The future behavior ofthe process is then monitored by plotting the projection of the data in the score space (either as joint t1 - t2 plots, or asindividual Shewhart plots on the scores since the score vectors are orthogonal), and a control chart on the SquaredPrediction Error SPE = ∑ (xi - x̂i)2. Any value of the SPE above its control limit suggests that one has moved off the planedefining “good” process behavior, and hence that a new event not in the reference data base has occurred. Any valuesof the score (t1, t2, ..) outside their control limits also signal an event. If an accompanying increase in SPE has notoccurred then large scores simply imply that the process is operating according to its normal model (ie. on the plane),but that unusually large variations have occurred.

To illustrate these charts I use an example from Nomikos and MacGregor (1994) on a batch process for the pro-duction of styrene-butadiene rubber (SBR) by emulsion polymerization. A reference data base of 50 batches, 9 variablesand 200 time intervals/batch was used to build an MPCA model having three latent variables. Control charts (t1 vs. t2; t1vs time; SPE vs. time) for a new batch producing good quality product are shown in Figure 7. The control limits repre-sent those for approximate 5% and 1% type I errors. These control charts reveal no unusual events over the history ofthis batch. With a result such as this it might be reasonable to allocate or ship the product from this batch as “in-control”material based on this process variable control charts, rather than waiting many hours for the laboratory results on Y tocome back. In Figure 8 control charts are shown for a new batch which resulted in poor quality product. It is obviousfrom the SPE chart and the score plots that a problem developed half way through the batch. To help diagnose an

assignable cause for the event, one can interrogate the underlying PCA model at point 106 where the SPE chart detectsthe event, and plot the contribution of the process variables to the SPE at that point in time. This plot (in Fig. 8) showsthat variables 4, 5 and 6 are the main contributors to the large SPE. Variable contribution plots for the t scores andHotelling’s T2 can similarly be calculated (see Miller et al., 1993; MacGregor et al., 1994; Kourti and MacGregor, 1996).

Some Statistical IssuesWhen dealing with large multivariate data sets and multivariate control charts in the projection space, a number

of statistical issues arise, many of which are not yet resolved. A few of them are discussed below.

(i) Control Limits on the ChartsTo determine control limits for the SPE and t-score charts we do not have to make the usual assumptions of

Normality and independence. With large data sets available from the historical databases of on-line computers, themodel can be built and the control limits can be established directly from the reference distribution of scores and SPEprovided by the database. The philosophy behind this approach using reference distributions is well described in chap-ters 2 and 3 of Box, Hunter and Hunter (1978), and was employed to obtain multivariate control limits in Nomikos andMacGregor (1995). The only important assumption needed is that the historical data set provides a representative sampleof common cause variation.



Figure 7

Figure 8

i=1

m

Variables

Contribution PlotC

ontr

ibut

ion

%


(ii) Hypothesis Testing and Control ChartsThe basis of most univariate control charts lies in hypothesis testing. We usually assume a distributional model

for the process and established a null or “in-control” hypothesis that specifies parameters such as the mean and varianceof the distribution. The alternative hypothesis is usually that either the mean or the variance has shifted.

In the multivariate industrial processes the null or “in-control” hypothesis is that new data are generated from aprocess with the same PCA or PLS model and that the distributions of the scores and SPE from this model are the same astheir reference distributions obtained from the historical data. The alternative hypothesis is simply the process no longerfollows this model and its reference distribution. In general it is not possible to be more specific and hypothesize thatjust the process mean has shifted, or the variance-covariance matrix has changed. The exact nature of the effect of a spe-cial event on the process is not easily specified. Consider, for example, the special event occurring in the polymerizationprocess in Figure 6 where an increase in the level of impurities in one of the feedstreams occurred. Such an eventaffects not only the means of many of the process variables, but also their variances and covariances in an unknownway.

The complexity of the multivariate processes also makes it difficult to find assignable causes for events once theyhave been detected. For this reason they need to be supplemented with diagnostic methods such as contribution plotswhich greatly simplify the subsequent search for assignable causes.

(iii) Degrees of FreedomIn evaluating a multivariate hypothesis test or determining control limits based on distributional theory the

degrees of freedom must be determined. In multivariate analysis we usually assume that a matrix of data X with k vari-ables and n observations has nk degrees of freedom, and if we estimate p parameters from the data we are left with (nk-p) degrees of freedom. Most of our concern is usually with determining the number of degrees of freedom lost throughestimating certain terms such as the scores and loadings in PCA. However, the real difficulty lies in estimating the num-ber of degrees of freedom we have in the original X matrix. Consider the batch reactor examples discussed earlier. Bothhad in the order of 100,000 observations in the reference data set. Is it reasonable to assume that we are starting with100,000 df.? If so, then a Likelihood ratio test to compare two PCA or PLS models developed from different data setswould imply the use of an F(100,000; 100,000) distribution and the null hypothesis would be rejected in essentially allcases.

In reality we never have (nk) degrees of freedom in such process data. The batch reactor data sets probablyhave at most a few hundred degrees of freedom. The difference between the effective statistical rank of a matrix and thenumerical rank is an important issue yet to be addressed adequately by statisticians, as is the development of statisticalmethods to treat these rank deficient data. Even the notion of an effective number of degrees of freedom has not beenadequately defined. Methods for treating these real world reduced rank problems are not generally available in the litera-ture or in multivariate statistical texts.

Will Statisticians Play a Useful Role?I return now to the secondary theme of this address - Is there a role for statisticians to play? and Are they up to

the challenge? The answer to the first question is clearly yes. We have entered a new era that has been ushered in bythe use of on-line computers to collect data on an ever increasing number of variables. Many new approaches are need-ed to efficiently analyse these data. Whether the statistical community is up to this challenge is still an open question.As an example consider the recent history of statistics in chemistry. Ten to twenty years ago analytical chemistry was stillbased on off-line chemical analysis of samples. Very few measurements were made on each sample and univariate statis-tical methods were quite adequate. The switch over to automated instrumental analysis such as spectrophotometric andchromatographic methods provided hundreds to thousands measurements on each sample. Statisticians offered little helpand showed little interest in these problems.

As a result the field of chemometrics sprang up in which chemists developed their own set of statistical tools andprocedures. This resulted in the creation of two new journals - the J. of Chemometrics and the J. of Chemometrics andIntelligent Laboratory Systems - which now attract most of the applied statistics articles in chemistry that once might havegone to Technometrics. It is interesting to note that the ASA recently rejected a move to create a chemometrics division,and that this rejection led directly to the creation of a North American Chemometrics Society with no connection to theASA.

The area of Statistical Process Control appears to be headed in a similar direction. Although Shewhart presenteda philosophy for SPC which is as valid today as it was in his time, we seem to be constrained by the type of traditional




univariate statistical approaches that worked well in the past. Faced with the large multivariate problems of todayapplied statisticians appear immobilized by the immensity of the problems, and by peer pressures under which they aremore worried about the detailed assumptions and “correctness” of the methods they use than providing even an approxi-mate solution to the problem. On the other hand, they haven’t fully recognized the tremendous advantages that arisefrom having such large data sets. A further inhibition stems from a lack of general knowledge of processes. As onestarts to use data on process variables, a greater understanding of the process itself, and the nature of process data, isrequired in order to use and interpret the data in an efficient manner. Another very real barrier to the statistician’sinvolvement is that the engineers have ownership of the computers and hence of the data they generate. They are oftenvery reluctant to part with these data. In the absence of involvement by statisticians, they have also developed their ownapproaches. An extensive literature exists in fault detection and identification using both theoretical and empirical mod-els, in artificial intelligence approaches, and in some of the multivariate projection (PCA/PLS) approaches highlighted ear-lier in this presentation.

I have also observed a tremendous resistance by statisticians to using more complex and alternative approachesto SPC. It is almost as though a certain set of classical univariate methods have been officially sanctioned and no alterna-tives will be considered until they are also sanctioned by some higher authority. Furthermore, I cannot remember howmany times I have heard the statement: “We have difficulty getting people to use even simple methods. How can youexpect them to use multivariate PCA/PLS methods?” My standard answer to this argument goes as follows. If simple uni-variate charts, etc. are not being used, it is probably because they often reveal little useful information, and are much toodifficult to interpret. Personally, I have a lot of difficulty trying to interpret a set of univariate charts on more than four orfive correlated variables. Multivariate projection methods are much simpler to use. All the data are projected down intolow dimensional orthogonal latent variable spaces that contain most of the information. The information is easily pre-sented and easily interpreted in these spaces. Operators don’t need to know PCA/PLS theory in order to use these chartsany more than they need to know Normal distribution theory in order to use univariate charts. The only important char-acteristic of SPC charts are their simplicity of presentation and simplicity of interpretation. The complexity or simplicityof the underlying statistical methods used to develop the charts is not an issue for the operator or engineer using thecharts.

SummaryI hope that I have been able to make a case in this paper, that a new and exciting era for applied statistics has

been opened up by the advent of on-line computers. The massive data sets we now collect contain a wealth of informa-tion that can be extracted by multivariate statistical methods. This is particularly true in the area of statistical process con-trol where process computers and information management systems have improved sufficiently that we can obtain dataon hundreds of process variables on a frequent and regular basis. By using these data in effective multivariate SPCschemes we have the potential to achieve major improvements in the understanding and control of our processes. Ihope that the statistical community will accept the challenge that this new computer era presents, and work with theengineers and scientists in realizing this potential. ∆

ReferencesG.E.P. Box, W.G. Hunter and J.S. Hunter, 1978, “Statistics for Experiments”, John Wiley & Sons, N.Y.

A.J. Burnham, R. Viveros and J.F. MacGregor, 1996, “Frameworks for Latent Variable Regression”, J. of Chemometrics, In Press.

J.E. Jackson, 1991, “A User’s Guide to Principal Components”, Wiley-Interscience, John Wiley & Sons, Inc., New York.

R.A. Johnson and D.W. Wichern, 1988, “Applied Multivariate Statistical Analysis”, Prentice Hall, N.J.

T. Kourti, P. Nomikos and J.F. MacGregor, 1995. “Analysis, Monitoring and Fault Diagnosis of Batch Processes using Multi-Block, Multi-Way PLS”, J.

Proc. Control, 5 No. 4, 277-284.

T. Kourti and J.F. MacGregor, 1995. “Process Analysis, Monitoring and Diagnosis using Multivariate Projection Methods”, J. Chemometrics and Intell.

Lab. Systems, 28, 3-21.

T. Kourti and J.F. MacGregor, 1996, “Multivariate SPC Methods for Monitoring Process and Product Performance”, J. Quality Tech., In Press.

J.F. MacGregor, J. Jaeckle, C. Kiparissides and M. Koutoudi, 1994, “Monitoring and Diagnosis of Process Operating Performance by Multi-Block PLS

Methods with an Application to Low Density Polyethylene Production”, Amer. Inst. Chem. Eng. J., 40, 826-838.

J.F. MacGregor and T. Kourti, 1995, “Statistical Process Control of Multivariate Processes”, Control Eng. Practice, 3, 403-414.

H. Martens and T. Naes, 1989, “Multivariate Calibration”, John Wiley & Sons, N.Y.

P. Nomikos and J.F. MacGregor, 1994, “Monitoring of Batch Processes using Multi-Way Principal Components Analysis”, Amer. Inst. Chem. Eng. J.,

40, 1361-1375.

P. Nomikos and J.F. MacGregor, 1994, “Multivariate SPC Charts for Batch Processes”, Technometrics, 37, No. 37, 1995.

S. Wold, K. Esbensen, P. Geladi, 1987, “Principal Component Analysis”, Chemometrics and Intelligent Laboratory Systems, 2(1987), p. 37-52.


A one day tactical planning meetingwas held following the Fall TechnicalConference in St. Louis. This meetingwas held to work on existing tacticalplans. These plans are developed toimplement the Division’s strategy.

Assessing Member’s Needs(Leader: Bob Mitchell)

The Assessing Member’s NeedsTactical Plan is complete . A surveyhas been developed to measure mem-ber satisfaction. The survey informa-tion will be used to identify areas ofneed and opportunities for continuousimprovement based on member input.Initially, surveys were conducted viatelephone but future surveys will beconducted via mail on a quarterlybasis.

Enable Broad Application ofStatistical Thinking

(Leader: Roger Hoerl)This Tactical Planning Committee isworking on a special edition of theNewsletter on Statistical Thinking.

The special edition should be pub-lished Spring 1996.

The group is also working on devel-oping a presentation on StatisticalThinking that would be made avail-able to interested individuals to pre-sent.

Publications Committee (Leader: Ed Mykytka)

The new Publications Committee metfor the first time and reviewed thebackground, purpose and objectivesof the committee. The group identi-fied and prioritized important policyissues and developed short-termobjectives and long-term goals.

Integrating Statistical Thinkinginto Education

(Leader: Chris Ayers)The team developed a vision of whata educationally integrated curriculumwould look like. A process was flow-charted for developing a pilot area.

The group will select one or two“sites” as pilots.

Two other areas which were identifiedat the AQC Tactical Planning Meetingwere worked on in small groups.

Division Dashboard (Leaders: Division Officers)

The team reviewed the concept of abalanced scorecard for the Division.A straw proposal was developed andincludes goals and measures in fourperspectives: financial, customer,internal business and innovation andlearning.

Define role of Regional Councilor (Leader: Bob Mitchell)

A straw proposal for this tactical planwill be developed by January 1. Thepurpose is to define responsibilities ofRegional Councilors or SectionLiaisons, identify “support” mecha-nisms and measurements of perfor-mance. ∆


The Statistics Division Councilmeets formally twice per year - in theSpring at the Annual Quality Congress,and the Fall at the Fall TechnicalConference.

A council meeting was held in con-junction with the Fall Technical Con-ference in St. Louis on October 18,1995. This article briefly summarizesthe meeting. Anyone interested thefull minutes of the meeting may ob-tain a copy by contacting Don Emerl-ing, Secretary. The meeting was wellattended by Council members andother interested Division members.

Bob Mitchell reported that member-ship was 12,410. A full membershipreport appears elsewhere in theNewsletter.

The Council voted on a bylawsrevision. This revision would changeRegional Councilors from elected toappointed positions. The revision was

approved and will take effect on the1996 ASQC ballot.

Nancy Belunis announced that theDivision will hold several meetings inconjunction with the Annual QualityCongress. A Tactical PlanningMeeting will start at 12:00 p.m. onSaturday, May 11 and continue onSunday, May 12th until 12:00 p.m.The council meeting will be heldSunday, May 12 from 8:00 - 10:00 p.m.The Annual Business Meeting will beMonday, May 13 from 5:30 - 7:30 p.m.All interested members are invited toattend any of these meetings.

Ray Waller, incoming ExecutiveDirector of American StatisticalAssociation, discussed possible inter-action between ASA and StatisticsDivision. Possible areas of synergyare continuing education and comput-er networks.

Ed Mykytka announced that the

new publications committee wouldhold its first meeting during theTactical Planning Meeting. A new edi-tion of the glossary will be availablethrough Quality Press this Spring.

Jacob Van Bowen, ProgramRepresentative for the 1995 and 1996Fall Technical Conference, announcedthat the theme for next year’s FTC is“Leveraging Data for the QualityTransformation.” The site for the 1996FTC is Scotsdale, Arizona.

The Standards Committee held ameeting at the FTC. See report else-where in the Newsletter.

Mark Kiel, Bulletin BoardAdministration, reported on ASQC Netand encouraged all members to partic-ipate. Mark has set up two librariesfor Division members only. Otherforums like the Statistics Corner areopen to all ASCQ members. ∆

Division Council Meeting

Division Tactical Planning Meeting


1995 FTC ShortCourse Summary

The two pre-conference shortcourses presented on October 18thwere attended by 80 people. Shortcourse participants represented morethan 25% of the total number regis-tered for the conference.

Dr. Douglas C. Montgomery pre-sented the short course “ResponseSurface Methods and Designs” basedon his new book, Response SurfaceMethodology (co-authored withRaymond H. Myers and available fromJohn Wiley.& Sons, NY). A shortcourse entitled “The Fundamentals ofDecision and Risk Analysis” was pre-sented by Dr. Gerald A. Bush ofDecision Strategies. Both courseswere well received based on partici-pant feedback.

These Division sponsored coursescost only $100 to attend, yet aretaught by leading professionals.That’s why they are consistently com-plimented as “the best value in a shortcourse." Pre-conference courses willalso be offered at the 1996 FTC inScottsdale, AZ. ∆

The 13th Quality and ProductivityResearch Conference and the 3rdSpring Research Conference onStatistics in Industry and Technologywill be held jointly at the NationalInstitute of Standards and Technology(NIST) in Gaithersburg, MD (a suburbof Washington, DC) from May 29through May 31, 1996. The goal of thisconference is to stimulate interdiscipli-nary research among statisticians,engineers, and physical scientists inquality and productivity, industrialneeds, and the physical and engineer-ing sciences. The conference is jointlysponsored by the ASA Section onPhysical and Engineering Sciences, theASA Section on Quality andProductivity, IMS, E. I. du Pont deNemours & Co. and NIST.

The conference will feature presen-tations by scientists and engineers forstatisticians and presentations by sta-tisticians for scientists and engineers.Statistical issues and researchapproaches drawn from collaborativeresearch will be highlighted.

Professor Vijay Nair, from theUniversity of Michigan, and ProfessorWilliam Golomski, from the Universityof Chicago Business School, will beplenary speakers. They will discussbroad issues and opportunities ininterdisciplinary research from differ-ent perspectives. There will also beboth invited and contributed papersessions.

For information on submitting con-tributed papers, contact Will Guthrie([email protected] or (301) 975-2854). The deadline for submittingcontributed papers is February 1,1996.

For further information about theconference, check the NIST StatisticalEngineering Division Home Page(http://www.cam.nist.gov/caml/sed/)or contact one of the conferenceco-chairs: Eric Lagergren([email protected] or 301-975-3245)or Raghu Kacker([email protected] or 301-975-2109). ∆

Joint Research Conference on Statistics

Division representatives areworking to establish proce-dures for awarding scholar-ships to deserving, recent col-lege graduates and graduatestudents who are pursuingcareers in applied statisticsand quality management.Original funding for thesescholarships was provided bya grant from the Ellis R. OttFoundation with the under-standing that scholarshipswould be given to promotestudies consistent with thephilosophies of Professor Ott.These include applied statis-tics, quality management,quality engineering, qualitycontrol and quality assurance,

with the emphasis on appliedresearch and direct applica-tions as opposed to the devel-opment of theory. A broadscholarship program descrip-tion has been written, butdetails must be fleshed out.Those interested in assistingin this formative effort areencouraged to contact LynneHare. His address is:

National Institute of Standards& Technology

Statistical EngineeringDivision

Bldg. 101/Room A337Gaithersburg, MD 20988-0001email: [email protected]∆

Scholarship Formation


Statistics Division members who arealso Certified Quality Engineers(CQE’s) are needed to assist in thedevelopment of CQE exams. A keyelement in ensuring the quality of thisexamination is having the participa-tion of top quality professionals whoare also CQE’s. Since statistical meth-ods constitute such a large part of theexam, the participation from the Stat-istics Division is particularly critical.

As part of the process of develop-ing new exams each year, volunteersare needed to participate in any oneof three workshops held at ASQCheadquarters. The three workshopsare:

1. Item Writing - developingexam questions and answers;each workshop consists of 30volunteers, at least 7 are neededfrom the Statistics Division,

2. Item Review - critiquing itemsproduced during the ItemReview Workshop; each work-shop consists of 12 volunteers, atleast 3 are needed from theStatistics Division, and

3. Exam Review - reviewing theexam prior to its administration;each workshop consists of 12volunteers, at least 3 are neededfrom the Statistics Division.

The Exam Review Workshop isheld twice each year, the others areheld annually.

The next opportunity is an ExamReview Workshop scheduled forMarch 22-23, 1996. An Item WritingWorkshop is tentatively scheduled forSeptember 13-15, 1996, followed byan Item Review Workshop December6-7, 1996. Reasonable expenses fortravel to Milwaukee, meals and lodg-ing are covered by ASQC. The cycleof workshops is repeated annuallyand you may participate once, or asoften as you like.

You must be a CQE to participateand be willing to sign a non-disclo-sure contract. For additional informa-tion and details on how to volunteer,please contact Statistics DivisionCertification Chair Nick Martino byphone at (508) 534-2556 between 7AM and 4 PM EST; or by mail, NickMartino, Novacor Chemicals Inc., 31Fuller St., Leominster, MA 01453; or byE-mail, [email protected].

∆

The Statistics Division will sponsorone technical session at the 1996Annual Quality Congress in Chicago.The session will take place onTuesday, May 14 from 8:00 - 10:00 am.The session is entitled In Search of theFuture: Models and Methods forWhole Systems Change.

In a world of increasing complexityand change, new models and methodsare needed to rapidly adapt. This ses-sion will present a change model,

using the seven new management andplanning tools, which can be used ina wide variety of applications.

The session will be presented byTom Swails. Tom is the quality man-ager for the Taping Systems BusinessUnit of 3M Company. He has facilitat-ed numerous planning session within3M, non-profit organizations andindustry. Tom led the development ofthe most recent Statistics Divisionlong-range plan. ∆

Annual QualityCongress Activities

Saturday, May 11Tactical Planning Meeting 12:00 pm - 5:00 pm

Sunday, May 12Tactical Planning Meeting 8:00 am - 12:00 pmPreconference Tutorial:

Committee Meetings 1:00 pm - 5:00 pmCouncil Meeting 8:00 pm - 10:00 pmSTAT Division Hospitality Suite 10:30 pm - 11:30 pm

Monday, May 13Annual Meeting 5:30 pm - 7:30 pmSTAT Division Hospitality Suite 9:00 pm - 11:30 pm

Tuesday, May 14Technical Session: In Search of the 8:00 am - 10:00 amFuture: Models and Methods forWhole Change Systems STAT Division Hospitality Suite 9:00 pm - 11:30 pm

1996 AQC Technical Session Sponsored by Statistics Division

CQE’s Needed


The workshop is directed towardsindividuals who want to encourageand support managers in the use ofcontrol charts in “non-manufacturing”applications. This workshop is direct-ed primarily towards the statisticalpractitioner who will guide managersin the proper setup of the charts.

“Non-manufacturing applications”of control charts refers to the activityof manufacturing a product ratherthan the manufacturing function. Thetraditional applications of controlcharts have been in the manufactureof products (e.g. control of peelstrengths, coating weights or percentadditives). By contrast, non-manufac-turing applications may include man-agement of labor hours, safety, sales,computer uptime, logistics cycle time.manufacturing costs, etc. To avoidconfusion, we have adopted the termi-nology of “business process charts”.

Applications of business processcharting are motivated by the perspec-tive known as Statistical Thinking,which states that:

• All work is done by means of aprocess.

• All processes exhibit variation inperformance.

• Variation is of two types: sys-temic from common causes andsporadic from special causes.

• Improvement means the reduc-tion of variation from thedesired performance, and mustby approached differentlydepending on the type of varia-tion present.

• Control charts are an effectiveway to distinguish the two typesof causes.

While SPC methods and conceptsare clearly applicable in any processapplication, there are some importantdifferences for business processes.Most business processes are less tangi-ble than manufacturing processes, are

less easily and less frequently mea-sured than manufacturing processes,and have greater variation (versusrequirements) than the manufacturingprocesses. In addition, for some busi-ness processes, some patterns in thedata are expected and/or desirable,such as growth in sales or cycles inenergy use. For these, the straightfor-ward application of the Shewhart con-trol chart may prove unsatisfactory. Inthese cases, certain modifications willbe needed as discussed in the work-shop.

Examples will be used to exploredata issues specific to businessprocess charts. Topics will include:

• Initial assessment of data

• Dealing with limited data

• Trends in the data

• Autocorrelation

• Normalization

• Seasonality

• Tuning sensitivity of a measure

• Aggregate numbers

• Low frequency events

• Related measures

• Resetting control limits

About the speaker:

Andrew Kirch is an AdvancesStatistical Specialist for the 3MCompany. He has an M.S. degree instatistics from the University ofWisconsin and an M.A. in appliedmathematics from the University ofMassachusetts.

Andy has worked with 3M plantsand laboratories worldwide for thepast twelve years on a wide variety ofproducts and processes, from elec-tronics to abrasives. He also hasresponsibilities in the areas of statisti-cal software, education and methodsresearch. ∆

AQC Preconference TutorialEffective Application of Statistical Thinking

to Business Processes and Systems

StandardsCommittee ReportThe Statistics Division is responsi-

ble for four ASQC Standards: B1, B2,B3 and S1. These standards are inneed of review for eventual reaffirma-tion of a five year cycle. The stan-dards cover the following areas:

ASQC Standard B1 (ANSI Z1.1) -Guide for Quality Control Charts

ASQC Standard B2 (ANSI Z1.2) -Control Chart Method for AnalyzingData

ASQC Standard B3 (ANSI Z1.3) -Control Chart Method for ControllingQuality during Production

ANSI/ASQC S1 - An Attribute Skip-Lot Sampling Program

A Standards Committee meetingwas held at the Fall TechnicalConference. A task-force was estab-lished to help determine the initialapproach to B1, B2 and B3 before for-mally setting up a writing group.Individuals who might be interested inwriting or reviewing standards shouldcomplete the member interest form.

∆


As of September 30, 1995, the Statistics Division total membership stands at 12,410 (10,469 Regular, 683 Senior, 126Fellow, 1 Honorary, 283 Student, 688 Sustaining, and 160 New). This current total is down considerably from our mem-bership peak of 15,985 in September 1992.

The Statistics Division membership is shrinking by approximately 100 members per month.

Being a Not-for-Profit organization it is not necessarily the Division’s goal to grow at some given rate; rather, we seekto continuously anticipate and meet our customers’ needs. Our goal is to serve our customers well and to provide value.It is somewhat disconcerting that we continue to lose more members than we gain despite our customer-focus orienta-tion. Recent Long Range Planning sessions have focused on developing the Division’s Mission, Vision, Values, Strategy,and Principles. The “Special Edition” Newsletter (Vol. 13, No. 2) published in Winter ’94 offers a summary of theDivision’s Long Range Planning efforts. Several Tactical Plans have emerged as the result of our strategic planning:“Enabling the broad application of Statistical Thinking”, “Integrating Statistical Thinking into Education”, “Establishing aPublications Committee”, “Assessing Members Needs”, and several tactical plans involving improvements to the “HowTo...” Series. In addition, the Division has recently named Mark Kiel as BBS Administrator to manage Statistics Divisionforums on the ASQCNet electronic bulletin board. Two newly assigned Tactical Plans are the development of a DivisionScorecard (a “dashboard” to monitor the health of the Division) and to Define the Role of the Regional Councilor. Achange to the Division by-laws was approved in October by the Statistics Division Council which will enable the Divisionto appoint more than one Councilor per region. The Regional Councilors serve as Statistics Division ambassadors to theASQC local Sections.

In an attempt to better understand why we are continuing to lose members, the “Assessing Member Needs” tacticalplan seeks to develop a customer satisfaction measurement for the Statistics Division. Member surveys are being conduct-ed by telephone and via mail to gain feedback on our direction and insight to member satisfaction. Exit surveys are beingdeveloped to probe the reasons for lost members. Results from these survey instruments will be fed into the

Division tactical planning process to ensure customer focus. A pilot Member Needs telephone survey was recentlyconducted by the Regional Councilors. Though, admittedly, a small sample size, results of this survey indicate an overallmember satisfaction rating of 97.9%. Most members place a high degree of importance, in order, on the DivisionNewsletter, the Statistics Division’s involvement in the CQE Body of Knowledge, Short Courses, maintenance ofANSI/ASQC Standards, and the “How to...” Series. Perceived lower value benefits of Statistics Division membershipinclude co-sponsorship of the Applied Statistics Conference, Nomination of Fellows, and the development of Divisionawards. Areas where most survey respondents would like the Division to concentrate its efforts are: keep membershipcurrent on new tools and techniques; promote Statistical Thinking everywhere; more Case Studies in the Newsletter; and,more technical Mini Papers. Two of the least desired potential activities are the development and administration of aCertified Quality Statistician exam, and software reviews.

The pilot survey was PDCA’d during the Tactical Planning Session in October and the improved Member Needs surveywill be mailed to a statistically representative sample of our membership during the month of November. The MemberNeeds survey will be conducted quarterly and the results published in the Newsletter. ∆

Robert MitchellMembership Committee Chair

Membership Report


The 1995 William G. Hunter Awardwas presented to William H. Lawton atthe Fall Technical Conference in St.Louis, MO. The ASQC StatisticsDivision established the Hunter Awardin memory of the Division’s foundingchair. The purpose of the award is topromote, encourage, and acknow-ledge outstanding accomplishmentsduring a career in the broad field ofapplied statistics. Bill Lawton followsBill Hunter’s model of statistical lead-ership as a communicator, consultant,educator and innovator, with the abili-ty to integrate statistical thinking intomany disciplines.

Bill Lawton is a Fellow of bothASQC and the American StatisticalAssociation (ASA) and is a past chairof ASA’s Section on PhysicalEngineering Sciences (SPES). Hereceived his Ph.D. in Statistics fromthe University of California atBerkeley. Bill made outstanding con-tributions during his 28 years withEastman Kodak, first as a statisticalconsultant to manufacturing and R&D,and later as a leader who championedthe use of statistical thinking andmethods throughout the company. Inthe 1980’s he served on Kodak’sQuality Advisory Council, a council ofsenior managers reporting to thePresident. During the 1990’s, Bill was

a Senior Research Associate withJoiner Associates and Professor ofMarketing at the William E. SimonSchool of Business Administration.During this period, Bill did pioneeringwork on quality-based customerresearch involving the linking of prod-uct development to the “voice of thecustomer.”

Bill is the author of over 20 papersin the application of statisticalmethods in the physical and engineer-ing sciences, business forecasting andmarketing. He received the WilcoxonAward in 1971 and 1974 and theShewell Award in 1970 and 1980. Heplayed significant roles as editor ofTechnometrics and in helping estab-lish the Fall Technical Conference as apremiere meeting of practicing statisti-cians, scientists and engineers. Billmade these remarks in accepting theaward at the FTC:

“I am genuinely grateful and hon-ored to be the 1995 William G. HunterAward recipient. As Ted Jacksonnoted last year, the citation for thisaward characterizes the sort of personBill was, and I am pleased to have mycontributions recognized in this man-ner. Bill was a good friend and pro-fessional colleague. We workedtogether for over 20 years in theChemical Division (now CPID), SPES,Gordon Research Conference andStatistics Division. These organiza-tions had a major impact on mygrowth as an applied statistician. Theorganizations and their associatedconferences provided a valued forumfor the exchange of ideas on how tofurther the field of applied statistics.Bill was master in the use of this typeof forum. There are two aspects ofBill’s contributions I’d like to mentiontoday.

“First, Bill had intrinsic recognitionthat statistics is a tool and the valuecreated by statistics occurs only in theuse in an application. Hence, Bill’soverriding concern was in keepingany discussions of statistics tied to a

field of application. Let me use ananalogy. It may be ‘nice’ to have acrescent wrench on a desert island, itmay be beautiful, intellectually satisfy-ing -- but of little value. But whenthat same wrench is used to close theopen seacock on a sinking boat, itsvalue is clear.

“Second, Bill was not just a com-municator, in my opinion; he was amaster of networking. His personalcontacts from the forum includedapplication disciplines in theUniversity, as well as broad industryand government practitioners ofapplied statistics. He used this net-work tirelessly to promote variousopportunities for the exchange ofideas in statistical application. Thiswas demonstrated by his roles in theformation of the Statistics Division andthe Center for Quality and ProductivityImprovement, and his constantinvolvement in the FTC.

“We sorely miss his rare skill in thisbroad, multidisciplinary networking.Yet, to some extent, his networkinglegacy remains with us today in theform of the FTC with its programderived by the networking of CPID,the Statistics Division, SPES and boththe industrial and academic communi-ties. Keep up the good work, Billwould be proud. Again, thank youfor this recognition.”

Nomination forms for the 1996award can be obtained from theWilliam G. Hunter Award CommitteeChair:

Steven P. BaileyDuPont EngineeringQuality Management &

Technology Center (QM&TC)Nemours Bulding, Room 65431007 Market StreetWilmington, DE 19898phone: 302-774-2375fax: 302-774-2458e-mail:

[email protected] must be received no

later than Monday, July 1, 1996. ∆

Bill Lawton Receives StatisticsDivision’s Hunter Award

Bill Lawton

The Statistics Division engages inmany activities in order to achievetheir mission.

• Publishing the quarterlyStatistics Division Newslettercontaining technical and non-technical mini-papers andbasic-tools articles, and infor-mation on division activitiesand upcoming events.

• Editing the Glossary and Tablesof Statistical Quality Controland the ASQC Basic Referencesin Quality Control: StatisticalTechniques. The glossary and16-volume “How To” series areavailable through Quality Pressin Milwaukee.

• Co-sponsoring the annual FallTechnical Conference, jointly

with ASQC’s Chemical andProcess Industries Division andthe American StatisticalAssociation’s Section onPhysical and EngineeringSciences. The conference fea-tures new tools and opportuni-ties for applications of statisticaland quality technologies.

1996 FTC - October 23 -25, Scotsdale, Arizona

• Sponsoring a technical sessionat the Annual Quality Congress.

1996 AQC - May 13 - 15,Chicago, Illinois

• Offering short courses prior tothe Fall Technical Conferenceand Annual Quality Congress.

• Sponsoring young researchersto attend the Gordon Research

Conference on Statistics inChemistry and ChemicalEngineering and students toattend the Fall TechnicalConference.

• Providing a speakers list andotherwise sponsoring technicalsessions at local conferences,upon request.

• Maintaining and updatingANSI/ASQC standards for quali-ty programs and statistical pro-cedures.

• Presenting the William G.Hunter Award annually to rec-ognize solid records of achieve-ment in the development andcreative application of statistics.

∆


Division Activities

The Statistics Division is seekingmembers to work in various capaci-ties. The job descriptions are printedbelow. If you have an interest in anyof these openings or any other activi-ties of the Division, complete theMember Interest Record Form andreturn it to Rick Lewis. Rick’s addressis included on the page with the formitself.

1997 AQC Short Course Chair

Purpose:To coordinate with ASQC national

in setting up a Statistics Division spon-sored short course at the 1997 AQC inOrlando.

Responsibilities:1) Identify short course(s) presen-

ter.

2) Submit a proposal to ASQC byAugust 1996.

3) Serve as the contact betweenASQC and the presenter(s).

4) Moderate the short course atAQC.

1997 Fall Technical ConferenceProgram CommitteeRepresentative

Purpose: The Fall Technical Conference pro-

vides a unique forum to present cur-rent and emerging quality technolo-gies to a national audience of qualityprofessionals. It is jointly sponsoredby ASQC’s Statistics and Chemical &Process Industries Divisions and theAmerican Statistical Association’sSection on Physical and EngineeringSciences.

Responsibilities:1) Work on a 3-member FTC

Program Committee to plan a 2-day technical program. Thisincludes issuing a Call forPapers, inviting speakers andmoderators, accepting or reject-ing submitted papers andpreparing the final program.

2) Attend the 1996 and 1997 FTC. 3) Submit items for publication in

the Division newsletter. ∆

Statistics Division Job Openings


Fall Technical Conference &Tactical Planning Meeting

From left to right: Chulho Lee - Student Grant Awardee; Walter

Liggett - “How-To” Series Editor; Ray Waller - Executive

Director, ASA; Galen Britz - Past Chair; Steve Bailey - Hunter

Award Chair

Janice Shade - Editor; Ed Mykytha - outgoing “How-To” Series

Editor; Don Strickert - interested member

John MacGregor - YoudenMemorial Address speaker

Bill Lawton - Hunter Award Winner;Steve Bailey - Hunter Award Chair

Nancy Belunis; Nick Martino, 1995 FTC Short Course Chair


Statistics Division Job OpeningsThe Statistics Division has several job openings for which we are seeking members willing to do some work for theDivision. The openings are as follows:

1. 1997 AQC Short Course Chair - Please see job description elsewhere in the Newsletter2. 997 FTC Program Committee Representative - Please see job description elsewhere in the Newsletter3. Authors and Reviewers for Basic Tools and Mini Paper Articles for the Newsletter4. Standards Committee Members - Please see article elsewhere in Newsletter5. Authors for “How To” booklets related to Process Improvement

The Job Descriptions are printed in this Newsletter. If you have an interest in any of these openings, please fill out theform below and return it to Rick Lewis.

Monsanto Co., Mail Zone 04B, 800 N. Lindbergh Blvd., St. Louis, MO 63167

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

ASQC STATISTICS DIVISIONMEMBER INTEREST RECORD FORM

Name ______________________________________________________________DATE________________________________COMPANY __________________________________________________________POSITION____________________________ADDRESS __________________________________________________________

____________________________________________________________________________________________________________________

PHONE (WORK) _____________________________________________________(HOME) _____________________________FAX _____________________________________________________

MEMBER NUMBER_________________________________ STATUS (MEMBER/SENIOR)______________________________

MEMBER AREAS OF INTEREST1997 AQC Short Course Chair ______ 1997 FTC Program Committee Rep. ______How-To-Series Author ______ Basic Tools/Mini Paper Author ______Standards Committee ______ Basic Tools.Mini Paper Reviewer ______Other ______

RELEVANT EXPERIENCE/EDUCATION__________________________________________________________________________________________________________________________________________________________________________________________________________________

MEMBER TIME AVAILABILITY/COMPANY SUPPORT/TRAVEL ETC.__________________________________________________________________________________________________________________________________________________________________________________________________________________

DATE _________________

OTHER COMMENTS____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________


ChairNancy BelunisMerck & Company, Inc.One Merck DriveP.O. Box 100, WS1E-45Whitehouse Station, NJ 08889-0100 Phone: (908) 423-3423Fax: (908) [email protected]

Chair-ElectBeth Propst1507 East 53rd Street #332Chicago, IL 60615Phone: (312) 288-4468Fax: (312) 288-4468

SecretaryDon Emerling3M Center235-3C-23St. Paul, MN 55144-1000Phone: (612) 737-2606Fax: (612) 736-7616

TreasurerDon WilliamsProcess Improvement Consultants2515 Jamestown LaneDenton, TX 76201-2212Phone: (817) 382-5992Fax: (817) 382-5992

Past ChairRick LewisMonsanto CompanyMail Zone O4A800 N. Lindbergh Blvd.St. Louis, MO 63167Phone: (314) 694-7735Fax: (314) 694-5614

Membership ChairBob Mitchell3M Consumer Products Plant915 Highway #22 SouthHutchinson, MN 55350Phone: (612) 234-1864Fax: (612) 234-1629

Newsletter EditorJanice ShadeR.M. Schaeberle Technology Center200 DeForest AvenueP.O. Box 1944East Hanover, NJ 07936-1944Phone: (201) 503-4915

Education Committee ChairChris AyersHamilton Beach/Proctor-Silex, Inc.4421 Waterfront DriveGlen Allen, VA 23060Phone: (804) 527-7158Fax: (804) 273-9825

Publications Committee ChairVacant

Acquisitions CoordinatorFalguni Sharma8858 South CourtApt. 303Allison Park, PA 15101Phone: (412) 364-0717

Briefings EditorRick LewisMonsanto Company, Mail Zone O4A800 N. Lindbergh Blvd.St. Louis, MO 63167Phone: (314) 694-7735Fax: (314) 694-5614

“How-To” Series EditorsWalter LiggettStatistical Eng. DivisionComputing and Applied MathematicsAdministration 101, Rm. 339National Institute of Standards &TechnologyGaithersburg, MD 20899

Bob BrillMonsanto CompanyMail Zone T1B800 N. Lindbergh Blvd.St. Louis, MO 63167Phone: (314) 694-1684

“Glossary” EditorJim Bossert5650 Alliance GatewayFort Worth, TX 76178Phone: (817) 490-7147

Awards Committee ChairLynne Hare21212 Chrisman Hill TerraceBoyds, MD 20841Phone: (301) 975-2840Fax: (301) 990-4127

William G. Hunter Award ChairSteve BaileyDuPont Engineering, QMTCNemours Building, Room 65431007 Market StreetWilmington, DE 19898Phone: (302) 774-2375Fax: (302) 774-2458

Standards Committee ChairEd SchillingRochester Institute of TechnologyCenter for Qual. & Applied Stat.1 Lomb Memorial Drive, Bldg. 14P.O. Box 9887Rochester, NY 14623-0887Phone: (716) 475-6129Examining Committee ChairBob PerryPillsbury Company330 University Avenue S.E.Minneapolis, MN 55414Phone: (612) 330-8144Fax: (612) 330-8294

Certification ChairNick MartinoNovacor Chemicals Inc.31 Fuller StreetLeominster, MA 01453Phone: (508) 534-2556Fax: (508) 840-0112

ASA Q&P LiaisonJoe VoelkelRochester Inst. of TechnologyCtr. for Quality and Applied Stat.1 Lomb Memorial DriveBldg. 14, P.O. Box 9887Rochester, NY 14623-0887Phone: (716) 475-2231

1996 Fall Technical Conference -ProgramJacob Van BowenStatistics and Computer ScienceUniversity of RichmondRichmond, VA 23173Phone: (804) 289-8081Fax: (804) 287-6444

1996 Fall Technical Conference - ShortCourseBill BleauPicker International Inc.1130 Stonecrest DriveTallmadge, OH 44278Phone: (216) 473-2385

Conference on Applied Statistics -ProgramFrank AltUniversity of MarylandCollege of Bus. & ManagementCollege Park, MD 20742Phone: (301) 405-2231

1996 Annual Quality Congress- ProgramLori CoonsEastman Kodak CompanyQTAS/MQAQBuilding 6, 7th FloorKodak ParkRochester, NY 14652-4608Phone: (716) 722-5217

1996 Annual Quality Congress -Short CourseCarol Meeter 3M Center224-4S-19 St. Paul, MN 55144-1000Phone: (612) 736-6297

Bulletin Board AdministratorMark KielAcme Steel Company13500 S. Perry AvenueRiverdale, IL 60627-1182Phone: (708) 841-8383 ext. 216Fax: (708) 841-0661

ASQC STATISTICS DIVISION1995-1996

OFFICERS AND COMMITTEE CHAIRS


The ASQC Statistics Division Newsletteris published quarterly by the StatisticsDivision of the American Society forQuality Control.

All communications regarding this pub-lication, EXCLUDING CHANGE OFADDRESS, should be addressed to:

Janice Shade, EditorASQC Statistics Division NewsletterNabisco, Inc.200 DeForest AvenueP.O. Box 1944East Hanover, NJ 07936-1944Phone: (201) 503-4915Fax: (201) 503-4884

Other communications relating to theStatistics Division of ASQC should beaddressed to:

Nancy BelunisMerck & Company, Inc..One Merck DriveP.O. Box 100, WS1E-45Whitehouse Station, NJ 08889-0100Phone: (908) 423-3423Fax: (908) 735-1107

Communications regarding change ofaddress should be sent to ASQC at:

American Society for Quality ControlP.O. Box 3005Milwaukee, WI 53201-3005

This will change the address for allpublications you receive from ASQCincluding the newsletter. You can alsohandle this by phone (414) 272-8575 or(800) 248-1946.

E Printed on Recycled Paper

STATISTICS DIVISIONAMERICAN SOCIETY FORQUALITY CONTROLc/o Janice Shade200 DeForest AvenueP.O. Box 1944East Hanover, NJ 07936-1944

Non-Profit Org.U.S. Postage

PAIDCedarburg, WIPermit No. 199

UPCOMING NEWSLETTERDEADLINES

Issue Vol. No. Due Date

Summer ’96 16 3 May 17, 1996

Fall ’96 16 4 Aug. 16, 1996

Winter ’97 16 5 Nov. 18, 1996

Volume 16, No. 2 Winter, 1996 Chair’s Message Basic...

Documents

Transcript of Volume 16, No. 2 Winter, 1996 Chair’s Message Basic...