Six Sigma Literature: A Review and Agenda for Future Research

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL

Qual. Reliab. Engng. Int. 2006; 22:335–367

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/qre.769

Review Six Sigma Literature: A Reviewand Agenda for Future ResearchJames E. Brady1,∗,† and Theodore T. Allen2

1LaBarge, Inc., 1505 Maiden Lane, Joplin, MO 64801, U.S.A.2The Ohio State University, Industrial and Systems Engineering, 1971 Neil Avenue, 210 Baker Systems, Columbus,OH 43210-1271, U.S.A.

Like quality management in general, Six Sigma has penetrated into most sectors oftoday’s business world. Although Six Sigma originated in industry, it has inspireda considerable amount of academic literature. This paper reviews this literaturedescribing the trends, sources, and findings. The paper also seeks to synthesize theliterature, with an emphasis on establishing its relationship to quality managementtheory and topics for future research. In doing so, there is an attempt to answer thefollowing fundamental questions. (i) What is Six Sigma? (ii) What are its impacts onoperational performance? (iii) What roles can academics usefully play in relation toSix Sigma? Copyright c© 2006 John Wiley & Sons, Ltd.

Received 10 June 2005; Revised 12 October 2005

KEY WORDS: quality management; statistical process control; project management

1. INTRODUCTION

Motorola’s Bill Smith initiated Six Sigma almost two and a half decades ago building on the philosophy,principles, and methods of Deming’s Total Quality Management (TQM). Since then, thousandsof organizations have become ‘Six Sigma companies’ by adopting specific training and project-

management practices. With Six Sigma’s industry-based origins, it becomes important to assess the state ofthe related academic contributions now that the associated field of study is maturing.

Ahire et al.1 reviewed the broader quality management (QM) literature. Sousa and Voss2 provided synthesisand structure of that literature from the academic viewpoint. Our aim is to provide both a description of theSix Sigma literature and to provide a similar degree of synthesis and structure. This includes establishing therelationship of Six Sigma to QM and to business practices in general.

In Section 2, we define Six Sigma, building on the definition of Linderman et al.3 Section 3 describes theterms used to classify the 201 articles we reviewed. The taxonomy is itself a synthesis of those used by Zainet al.4 and Sousa and Voss2 for similar purposes. In Section 4, we use summary statistics to depict literaturetrends related to research interest and authorship. Also, as many of the articles on Six Sigma concern ‘successfactors’, we present a tabulation of the factors identified by the most authors. Section 5 relates Six Sigma tothe broader literature on QM and Section 6 summarizes the literature regarding the impacts of Six Sigma oncompany performance. Section 7 closes with a synthesis of the literature and a discussion of areas for futureresearch.

∗Correspondence to: James E. Brady, LaBarge, Inc., 1505 Maiden Lane, Joplin, MO 64801, U.S.A.†E-mail: [email protected]

Copyright c© 2006 John Wiley & Sons, Ltd.

336 J. E. BRADY AND T. T. ALLEN

2. DEFINING SIX SIGMA

Linderman et al.3 (p. 195) emphasized the need for a common definition of Six Sigma and proposed:

‘Six Sigma is an organized and systematic method for strategic process improvement and newproduct and service development that relies on statistical methods and the scientific method tomake dramatic reductions in customer defined defect rates.’

Those authors further described that ‘the name Six Sigma suggests a goal’ of less than 3.4 defects per millionopportunities (DPMO) for every process. However, they did not include this principle in the definition because,‘Six Sigma advocates establishing goals based on customer requirements’.

One concern with the Linderman et al.3 definition of Six Sigma as a ‘method’ is that the definition leavesout philosophy and principles. For example, Dean and Bowen5 defined QM to include techniques and a set ofprinciples and practices. We suggest that emphasis on monetary gains in Harry6, Hahn et al.7, Bisgaard andFreiesleben8, and other seminal literature warrants the following addition: ‘The Six Sigma method only fullycommences a project after establishing adequate monetary justification’. Montgomery9 argues that it is thisfocus on the bottom line that keeps management interested.

Virtually all popular books and training materials on Six Sigma describe statistical methods much morevocationally than standard statistical texts (Breyfogle10, Harry6, and Pande et al.11). Specifically, they omitmuch of the associated theory and include, in some cases, simplified versions of standard statistical methods.Further, Hahn et al.12 wrote that the related education goals are not to train ‘statistics experts’ but only to givethe ‘knowledge essential to . . . obtaining business results’. We therefore propose to add the following principleto the definition of Six Sigma: ‘Practitioners applying Six Sigma can and should benefit from applying statisticalmethods without the aid of statistical experts’.

Another concern with the Linderman et al.3 definition and others, is that it may be unnecessarily vague.There is an appearance in the literature that this vagueness may be intentional in an attempt by advocates toavoid controversy. We submit that there is sufficient consensus within the Six Sigma literature to offer thefollowing additional details about the Six Sigma method in its definition:

The Six Sigma method for completed projects includes as its phases either Define, Measure,Analyze, Improve, and Control (DMAIC) for process improvement or Define, Measure, Analyze,Design, and Verify (DMADV) for new product and service development.

Widely read books such as Harry6 and Pande et al.11 clearly imply that this refinement is part of the definitionof Six Sigma.

Harry6, Pande et al.11, and others also imply that multiple techniques are often used in applying Six Sigma.Therefore, the definition of Six Sigma as ‘a method’ complicates reference to the techniques used in itsapplication. We propose to refer to these techniques as ‘sub-methods’ to clarify their scope relative to that ofSix Sigma. The existence of ‘sub-methods’ helps to connote the idea that Six Sigma is broader than a definitionas a method might imply. Also, Six Sigma then becomes more like a ‘practice’ than a ‘core method’ as definedby Sousa and Voss2. Sousa and Voss2 also defined ‘infrastructure practices’ as those that create ‘an environmentsupportive of the use of core practices’. With these definitions in mind, it becomes apparent that both of theprinciples above are associated with what might be called specific ‘Six Sigma infrastructure’ (SSI) practices.

It is also unmistakable from reading the most popular books on Six Sigma (Breyfogle10, Harry6, Pandeet al.11, and others) that there is a strong attempt to associate sub-methods with specific phases of the applicationof Six Sigma. For example, the application of gauge R&R would generally not be considered appropriate in theDefine phase. However, to our knowledge, no specific associations have currently received sufficient consensusto become part of the definition of Six Sigma. Also, any specific set of associations could justifiably be viewedas undesirable restrictions by some portion of Six Sigma users.

We define Six Sigma as a method involving either DMAIC or DMADV as phases. We include two principlesin our definition. The first emphasizes attention to the bottom line in initiating projects. The second principleemphasizes the training of non-statisticians in the vocational use of statistical tools with minimal theory.

Copyright c© 2006 John Wiley & Sons, Ltd. Qual. Reliab. Engng. Int. 2006; 22:335–367DOI: 10.1002/qre

SIX SIGMA LITERATURE: A REVIEW AND AGENDA FOR FUTURE RESEARCH 337

3. LITERATURE REVIEW METHODS

In this section, we explain the approach used to select the 201 articles covered in our review. Then, the termsused to categorize these articles are defined. Overall, it is not claimed that the list of articles is exhaustive,only that the associated database serves as a reasonably comprehensive list for understanding Six Sigma relatedresearch.

3.1. The list of articles

The list of articles was derived from a Science Citation Index (SCI) Expanded search spanning the time periodfrom 1990 through 2003. Five descriptors were used: Six Sigma, quality systems, quality improvement, qualitymanagement, and quality meta-model. The text of each article was reviewed in order to eliminate those that wereclearly not related to ‘Six Sigma’ improvement strategies. For example, articles were removed that focused ondetailed synthesis of chemicals and used the term Six Sigma in an unrelated context. Also, a small numberof articles were included from magazines and conference proceedings that were subjectively assessed to beacademic in character. The list of journals, proceedings, and magazines that provided at least one relevantarticle is shown in Table I.

3.2. The classification scheme

Articles were classified using the 11 descriptors in Table II. Authors represented either academic institutions orindustrial companies or constituted a team with representatives from both. Many articles contain definitions ofthe phases DMAIC, but most did not.

Two schemes were used to evaluate the primary topic(s) of each article. Oakland14 divided quality issuesroughly into systems, tools and technologies, and people, without providing precise definitions of these terms.We follow Zain et al.4 in using this division to classify articles (version 1). Sousa and Voss2 developed amodified scheme based on philosophies, practices, and tools and techniques (version 2). Sousa and Voss2

defined ‘philosophy’ as ‘an approach to management’, and practices as ‘an observable facet of a philosophy andit is through them that managers work to realize organizational improvements’. Those authors also described‘tools and techniques’ as ‘core elements’ with examples being process control and Pareto analysis. To clarifythe relationship between these terms, Sousa and Voss2 wrote that the practice ‘process management’ can beconducted using many optional core methods such as statistical process control (SPC).

The next descriptor was the so-called ‘journal impact factor’ from the SCI. This number constitutes a ratio ofthe citations to articles in a journal to the average number of citations to journals in that field. The impact factorcan be viewed as a rough evaluation of the academic quality of the journal. Many articles made explicit referenceto either the manufacturing or service sector issues, while others offered general contributions. A commonfeature of articles was mentioning 3.4 DPMO in relation to the definition of Six Sigma.

The articles were each affiliated with one of the following sponsoring societies or areas of study: theAmerican Institute of Chemical Engineers (AICHE), the American Society of Mechanical Engineers (ASME),the applied statistics area including publications sponsored by the American Society of Quality (ASQ) and theAmerican Statistical Association (ASA), the Institute of Industrial Engineers (IIE), the operations research ormanagement science (OR/MS) area including publications sponsored by the Institute for Operations Researchand Management Science (INFORMS) and other related journals, or the medical area in general including theAmerican Medical Association (AMA). Note that not all medicine related journals are included in the sciencecitation index and, therefore, some related articles are omitted.

Following Zain et al.4, articles were classified as focused on case studies, survey results, literature review,comparative analysis, or theoretical with application. Approximately 27% of the articles investigated the factorscontributing to the success of Six Sigma implementations. For those articles, the specific success factorsmentioned were tabulated. The terminology used to describe the success factors was standardized to correspondto the dimensions of quality management practice in Sousa and Voss2.



Table I. List of journals or proceedings with at least one article in the study

Accreditation and Quality Assurance Journal of Engineering DesignAIAA-2002-1471 Journal of Evaluation in Clinical PracticeAnnual Quality Congress Transactions Journal of Healthcare ManagementAnnual Reliability and Maintainability Symposium Journal of Management Engineering

Proceedings Journal of Manufacturing Science and EngineeringArchives of Pathology and Laboratory Medicine Transactions of the ASMEAssembly Automation Journal of Mechanical DesignAviation Week and Space Technology Journal of Operations ManagementBuilding Research and Information Journal of Quality and ParticipationBusiness Management Journal of Quality TechnologyBusiness Month Journal of The IESCancer Journal Lecture Notes In Computer ScienceChemical Engineering Communications Manufacturing EngineeringChemical Engineering Progress Milbank QuarterlyChemical Week Proceedings of the 2001 Winter SimulationClinical Chemistry ConferenceComputers and Industrial Engineering Proceedings of the 2002 Winter SimulationComputers In Industry ConferenceControl Engineering Proceedings of the ASME Design EngineeringElectronic Business Technical ConferenceFortune Professional EngineeringGenetic Engineering News Quality and Reliability Engineering InternationalHospitals and Health Networks Quality DigestHydrocarbon Processing Quality EngineeringIEEE Engineering Management Review Quality Management in Health CareIEEE Software Quality ProgressIEEE Transactions on Neural Networks R&D MagazineIEEE Transactions on Semiconductor Radiology

Manufacturing Research-Technology ManagementIIE Solutions Six Sigma Forum MagazineIndustrial Management and Data Systems TechnometricsInternational Journal of Production Research The American StatisticianInternational Journal of Quality and Reliability The Physics Teacher

Management Therapeutic ApheresisInternational Journal of Quality Science Total Quality ManagementJournal of American Geriatrics Society Total Quality Management and Business ExcellenceJournal of Applied Statistics Training and Development

Table II. Descriptors used to classify articles

Descriptor Source Levels

Authorship Allen13 Industrial (I), Academic (A), or Both (I A)Define DMAIC Allen13 Yes (Y) or No (N)Topics Version 1 Oakland14 Systems (Sy), Tools and Techniques (To), and People (Pe)Topics Version 2 Sousa and Voss2 Philosophy (Ph), Practices (Pr), Tools and Techniques (To), and OtherIndustrial Sector Zain et al.4 Manufacturing (M), Service (Se), or General (G)Journal Impact Factor SCI 0.13 to 4.76Mention of 3.4 DPMO Allen13 Yes (Y) or No (N)Research Approach Zain et al.4 Case Study (Ca), Comparative (Co), Survey (Su), Literature Review (R),

or Theoretical with Application (TA)Society or Area Allen13 AIChe, ASME, ASQ, IIE, INFORMS, or MedicalSuccess factors Allen13 All combinations of 13 possible factorsSpeculative in Nature Allen13 Yes (Y) or No (N)



Finally, articles that recommended the use of one or more practice without clarifying conditions in which thispractice has provable properties were classified as ‘speculative’ in nature. This classification differentiated thesearticles from others without rigorous statistics or optimization related justifications.

4. LITERATURE REVIEW

In this section, we present a characterization of the database of articles using statistics derived from the classifiersdescribed in Table II. Goals include the identification of trends including those that relate to the authorship ofarticles and the subjects addressed. There is also an investigation of the interaction of authorship with researchfocus and a tabulation of the associated sponsoring societies or areas of study. Finally, we discuss results relatingto success factors, including a tabulation of the success factors cited most often in the literature.

4.1. Literature trends

Figure 1 plots the number of articles versus the year. It suggests two findings. First, the number of articlesby industrial authors peaked in 2000. We hypothesize that a subsequent declining trend was influenced bycondemnations of Six Sigma in the popular press such as Clifford15 in Fortune Magazine. Second, at thesame time, interest among academics grew through 2003. Over the entire search period, 69.2% of the authorshad industry affiliations and 30.8% had academic affiliations. These proportions have been changing to thepoint where 53% of the authors reviewed in 2003 were associated with a university or college. This trend inauthorship from industry dominated to academic dominated is not surprising considering the industrial originsof Six Sigma.

Another trend is the diversification of research topics from primarily manufacturing focused to more generalin nature as indicated by Figure 2. This trend is characterized with increased emphases on generic issues andon service-related business sectors. Particularly, there is an increased emphasis on generic statistical tools suchas design of experiments (DOE), probabilistic design, and SPC in the context of Six Sigma, e.g., Mason andYoung16, Coleman et al.17, McCarthy and Stauffer18, Koch19, and Goh20. Figure 3 charts the percentages ofarticles associated with different areas. The fact that the earliest medical related publication in the database isBuck21 supports the finding that the medical area is playing an important role in increasing the topic diversity.

Figure 3 also indicates that the applied statistics journals such as the Journal of Quality Technology,Quality Engineering, Quality Management Journal, Quality Progress, Quality and Reliability EngineeringInternational, and Technometrics dominate scholarly publications on Six Sigma. This dominance is perhapssurprising considering that many people who apply Six Sigma have little or no formal training in statistics asnoted by Hahn et al.12 and others.

4.2. Research topics and methods

Next, we examine the topics and research approaches of the reviewed articles. We begin by focusing on thetopics covered and the dependence of the number of articles and scholarly impact on the authorship. Then, weinvestigate the methods used in relation to scholarly impact.

Table III contains a cross tabulation of the topic and authorship variables. It supports three findings. First, themajority of articles focused on either philosophy or systems topics. The percentages on these topics were 54.7%and 66.2%, respectively. In general, papers in these categories provided a general description of Six Sigma andadvocated its use, e.g., Rayner22, Harry6, Snee23, and Does et al.24. We estimate that 32% of the total articlesare in this category and introductory in nature. Often, these articles included definitions of Six Sigma in terms of3.4 DPMO (100%) and/or reviewed the DMAIC structure (75%). Overall, only 6% of the articles were written atthe practices level which Sousa and Voss2 argued are most useful for stimulating actual organizational changesor improvements. Academic authors wrote about practices with higher frequency (10%).

Second, Table III also shows that academics were more likely to choose topics such as tools andtechniques and practitioners were more likely to present philosophical or systems-level contributions.For example, only 17% of articles by exclusively industrial authors concerned tools and techniques, comparedwith 36% of the articles by exclusively academics.



0

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Figure 1. The yearly number of Six Sigma related articles and their authorship

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Figure 2. The percentage of articles focused on manufacturing topics

Figure 4 is a Pareto chart of the number of articles associated with the different research methods. The paperscontaining case studies constituted a sizable fraction of papers on all topics. For example, 40% of the papersclassified as philosophy focused contained case studies and 50% of the papers exclusively on tools andtechniques contained a case study. The majority of all types of article, even those on tools and techniques,contained no new techniques but rather standard techniques adapted to the context of Six Sigma projects. Of thearticles with case studies, the majority contained a single case.



0% 10% 20% 30% 40% 50%

Applied Statistics

AICHE

ASME

Medical

OR+MS

IEEE

Figure 3. Percentages of articles sponsored by different societies or areas

Table III. Tabulations of articles by focus and authorship

Academic Industrial Mixed Totals

(a) Topics Version 1 from Oakland14

Systems 23 83 4 110Tools and Techniques 20 28 5 53People and Systems 3 18 2 23People and Tools and Techniques 4 5 0 9Systems and Tools and Techniques 0 5 1 6

(b) Topics Version 2 from Sousa and Voss2

Philosophy 26 101 6 133Tools and Techniques 18 25 5 48Philosophy and Tools and Techniques 1 6 1 8Philosophy and Practices 3 3 0 6Practices 1 2 0 3Practices and Tools and Techniques 1 2 0 3

Totals 50 139 12 201

Journal impact factors were developed by the SCI to provide a rough measure of journal quality or impact.Figure 5 is a box and whisker plot of the journal impact factors associated with the articles in our review.Surprisingly, academic authors exhibited only a slight tendency to publish in journals with higher scholarlyimpact. Figure 6 is a box and whisker plot of the journal impact factors. These factors are associated with articlespertaining to: (1) manufacturing; (2) service of business; or (3) all sectors, i.e. of generic interest. The plot showsthat service-related publications have the highest scholarly impact. The impacts of service-related publicationscan be attributed to the relatively larger audience associated with the specific health care related journals ClinicalChemistry and Laboratory Medicine, the Milbank Quarterly, and the Journal of the American GeriatricsSociety. The associated articles covered topics classified as systems and tools and techniques.

A sizable fraction of the articles in the most prestigious statistics journals concerned the tools that Six Sigmablack belts ‘should know’, e.g., the discussion of Hoerl25,26 and Montgomery et al.27. These articles wereclassified into people combined with tools and techniques in the Oakland14 scheme and philosophy and toolsusing the Sousa and Voss2 scheme. These articles caused both categories to be associated with the highestmedian journal impact factors. Figures 7 and 8 provide box and whisker plots of the impact factors associatedwith research topics. Surprisingly, the topic associated with the least impact is ‘practices’. Sousa and Voss2

argued that this topic was the one most likely to stimulate ‘organizational improvement’ or change.



Number of Articles

0 10 20 30 40 50 60 70 80

Case Study

Theoretical with Application

Survey

Comparative

Literature Review

Other

Figure 4. Pareto chart of articles by research approach

I AIA

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Figure 5. Journal impact factors of articles by type of author

Two other important themes related to people topics. First, articles focusing on the cultural implications ormanagement actions included Sanders and Hild28 and Wiklund and Wiklund29. Second, leadership and trainingare also popular themes (see, e.g., Hahn et al.7 and Hoerl25). Many of these articles examined success factors,as we describe next.

4.3. Success factors

In our database, 27% of the articles made reference to at least one ‘success factor’ helpful or necessary for SixSigma to succeed. We identified 13 distinct success factors mentioned and standardized the language aroundthat used by Sousa and Voss2 to describe dimensions of QM practices. Figure 9 shows the fraction of articlesmentioning success factors that cited each factor.

Inspection of Figure 9 suggests three findings. First, close to 50% of the articles that mentioned atleast one success factor included ‘top management commitment’, which might be regarded as a consensus



ServiceManufacturingGeneral

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Figure 6. Journal impact factors of articles by business sector

ToSy ToSyPe ToPe Sy

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Figure 7. Journal impact factors of articles on people (Pe), tools and techniques (To), systems (Sy), or a combination of these

view among authors. Second, a sizable fraction of the articles mentioning success factors emphasized trainingprograms involving adult participants from multiple disciplines, e.g., Hahn et al.12 and Snee30. Third, some ofthe success factors appear to be incompatible such as bottom line focus and customer focus. This highlights theheuristic nature of articles in general on the subject of success factors.

For reference, a dissertation by Lee31 used survey results from practitioners to investigate success factors.Survey findings generated the following ordering of success factors from most to least important (paraphrasing):top management commitment, statistical/analytical tool usage, managerial capabilities of trained participants(i.e. black belts), managerial process, personality of black belts, Six Sigma training programs, adoption ofprevious quality programs and others. Combining results from Lee31 and from our own literature review, wefind consensus in relation to top management support and the importance of established training programs.In addition, statistical tools and ‘data systems’ are considered important both in the literature and through thesurvey in Lee31.



ToPr ToPrPh ToPh PrPh

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Figure 8. Journal impact factors of articles on philosophy (Ph), practices (Pr), tools and techniques (To), or a combinationof these

0% 20% 40% 60% 80% 100%

Top management commitment

Team training

Data system

Structured approach

Forming the right team

Bottom line focus

Team involvement

Project selection

Customer focused

Right project leadership

Goal based approach

Change management

Adaptable system

Figure 9. Percentages of articles mentioning each of 13 success factors



5. SIX SIGMA IN THE CONTEXT OF MANAGEMENT THEORY

Six Sigma was developed by industry practitioners at Motorola who were not primarily interested in academiccontributions. It is not surprising, then, that the role of Six Sigma in the context of management theory isobscure and, as noted in Linderman et al.3, only a small fraction of the Six Sigma literature has been devotedto theory. In this section, we synthesize the description of Six Sigma to bring its contribution into clearer focus.Then, we suggest modifications appropriate for the evaluation of Six Sigma to the quality performance modelof Garvin32.

5.1. The academic contribution of Six Sigma

We begin by reviewing four facts established previously about Six Sigma. First, it can be debated whetheror not the principle of establishing monetary justification for applying the Six Sigma method belongs in thedefinition. Yet, monetary justification of projects assuredly is associated with Six Sigma. Second, Six Sigma isrelatively specific in nature in relation to the pantheon of QM practices. This fact is established by the definitionin Linderman et al.3 of Six Sigma as a ‘method’. Also, 24% of articles defined the DMAIC phases and many ofthe most popular books on Six Sigma associate specific core statistical methods with phases (e.g., Breyfogle10,Harry6, and Pande et al.11). Third, the books and training materials associated with Six Sigma are relativelyvocational in nature. For example, Hahn et al.12 wrote that the aim is not to train ‘statistical experts’. Fourth, themost important success factors associated with Six Sigma were believed to be (1) top management commitmentand (2) multidisciplinary team training.

We begin by connecting the emphasis on monetary justification with achieving top management commitment.As noted by Hahn and Hoerl33, money is the language spoken by management and key to getting projectsfunded (paraphrasing). Next, we connect greater specificity and relatively vocational materials with traininglarge numbers of practitioners from multiple disciplines. Intuitively, greater specificity about what ‘should’ beused and when it ‘should’ be used in the context of a project, combined with the omission of complicated theory,would seem appropriate for motivating adult learners to use the methods.

Combining these observations, we conclude that widespread multidisciplinary usage of statistical techniquesis the implied goal of Six Sigma and its main contribution to the business world. Academically, we see threerelated contributions. First, the bottom line and multi-phase nature of Six Sigma has likely increased the scope ofresearch to embrace total projects and not just the portions associated with the application of a single statisticalmethod. This explains the interest on modeling quality savings in Bisgaard and Feriesleben8 and why over onethird of the articles contained case studies.

Second, the relatively greater emphasis on specific core (SC) methods and specific infrastructure (SI) hasspawned considerable academic discussion with greater specificity. For example, there is a substantial academicthread focused on what tools ‘should’ be learned and used by Six Sigma trained participants or ‘black belts’(see, e.g., Hoerl25 and the related discussion in the Journal of Quality Technology). Although the discussion oftraining materials is not new to the quality literature, the emphasis on relatively specific references to the phasesof projects is somewhat new.

Third, Six Sigma has caused many people from multiple disciplines to become aware of and apply statisticalmethods. It is perhaps remarkable that 69% of authors of academically relevant publications had industryaffiliations. Although, in general, Six Sigma practitioners have learned only standard methods, they constitutea large potential market for research and, perhaps, new methods. Distinguishing features of this market includethat participants: (1) are relatively practical and focused on business results; (2) need techniques for predictingthe bottom line impacts of projects before they embark upon them; and (3) apply statistical methods without,in general, being experts in statistics.

In Section 6, we discuss the implications of these findings on the roles that academics can most usefullyplay in relation to Six Sigma. Next, we discuss where the SI and SC elements of Six Sigma fit into the qualityperformance model.



Quality Management Practice

Six Sigma Practice

Six Sigma

Infrastructure

Six Sigma Core

Internal Process

Quality

Operational

Performance

Business

Performance

Product Quality

Performance

Figure 10. Extended quality performance model of Garvin32

5.2. The quality performance model

Much research has focused on the relationship of QM practices with the various aspects of companyperformance (Sousa and Voss2). Garvin32 introduced a quality performance model to set up an empiricalexamination of the separate effects of management practices on internal process quality and product qualityperformance (QP) and their effects on operational performance (OP) and business performance (BP).In reviewing the literature on Six Sigma, we feel that it is helpful to place Six Sigma into this diagram.

In our definition of Six Sigma in Section 2, we identified principles and methods associated with SSI and ‘SixSigma core’ (SSC) QM practices. It was argued that the method of Six Sigma is itself a quality practice whilesharing some characteristics with a core method. Figure 10 shows the placement of these specific core practicesand infrastructure in the Garvin32 model.

6. REVIEW OF EMPIRICAL EVALUATIONS OF SIX SIGMA

In this section, the literature relating to the performance evaluation of Six Sigma is briefly reviewed. Only a smallfraction of articles in our database pertain to an empirical model or evaluation with scope greater than estimatingthe savings associated with a single case study. Table IV lists five of these articles with reference to SSC andSSI practices. The other acronyms used are referenced in the extended quality performance model in Figure 10.The fourth article by Gautreau et al.34 does not make specific reference to Six Sigma but was included becauseit addresses issues related to the inclusion of specific methods in the context of quality projects. This seemsrelevant given Six Sigma’s emphasis on specific core methods and business outcomes.

Table IV contains a description of the roles each article plays for empirical validation in relation to Six Sigma.Goh et al.35 examined stock performance associated with announcements of Six Sigma programs and dates ofquality awards. They found hints of short-lived abnormal returns but no significant evidence of short- or long-term returns. Another data-driven meta-analysis that we found was Lee31, which was based on survey data.The associated surveys indicated positive self assessments of the value of the company’s own Six Sigma efforts.Also, to our knowledge, the impacts of specific core sub-method selection on bottom line impacts has not beenstudied empirically, with Gautreau et al.34 providing one of few relevant theory based modeling approaches.

Considering the emphasis on modeling profits to justify each project, it is not surprising to all attemptsto provide meta-modeling tools, see, e.g., Bisgaard and Feriesleben8. Yet, Bisgaard and Feriesleben8 explore



Table IV. Literature pertinent to evaluation of Six Sigma’s effects on company performance

Study Quality performance model Study method Main findings

Bisgaard and Feriesleben8 SSI → BP Economicbreak-evenanalysis model

Fraction non-conforming and unnecessaryactivities can significantly influence cost andreduce profit

Gautreau et al.34 C → QP → BP Partially observedMarkov decisionprocess

Decision-based model of process improvementactivities, e.g., do nothing or inspect and separatecan each be optimal depending on assumptions

Goh et al.35 SSI/SSC → BP(stocks)

Hypothesis testing The majority of companies show positive returnsafter announcing Six Sigma programs, but nostatistical significance was established

Lee31 SSI/SSC → BP Survey of 106companies

Top management commitment, project selection,team leader, training and the specific tools usedeffect business results

Linderman et al.3 SSC → QP/OP → BP Goal-theoreticmodel

Types of goals effect quality and operationalperformance that effect business results

prediction of product value under simple, generic assumptions. As they themselves suggest, more research onrelated topics will likely be needed for broad applicability.

7. SYNTHESIS AND FUTURE RESEARCH

In this paper, we proposed a definition of Six Sigma and characterized the associated body of academic literature.We also clarified Six Sigma’s contributions to scholarly research and its relationship to QM theory. Reflectingon the findings, we next return to the questions. (i) What is Six Sigma? (ii) What are its impacts on operationalperformance? (iii) What roles can academics usefully play in relation to Six Sigma?

7.1. Synthesis

We define Six Sigma as a method involving either DMAIC or DMADV as phases. This definition of Six Sigma asa method builds on that proposed by Linderman et al.3. The inclusion of DMAIC and DMADV in the definitionis supported by the fact that 75% of introductory articles on Six Sigma reference these structures. We includetwo principles in our definition. The first emphasizes attention to the bottom line in initiating projects. This wassupported by comments of seminal writers relating to how Six Sigma differs from TQM, e.g., Harry36 andMontgomery9. Also, bottom line focus was mentioned by 24% of relevant articles as a critical success factor.The second principle emphasized the training of non-statisticians with minimal theory. We support this inclusionbased on remarks by Hahn et al.12 and others about the goals of Six Sigma training. In addition, we foundrelatively frequent mention of multidisciplinary training as a critical Six Sigma success factor (roughly 24% ofrelevant articles). Finally, popular books on Six Sigma such as Breyfogle10, Harry6, and Pande et al.11 noticeablydeemphasized theory.

The span of time surveyed was from 1990 to 2003, which provided a representative sample. We feel thetrends presented are still relevant as can be seen by inspecting more recent works such as the special issueof Quality and Reliability Engineering International on Six Sigma (2005; 21(3):221–328). In this issue, ninepapers on Six Sigma were presented. Walters37, Hahn38, and Snee39 discussed definitions for Six Sigma andcritical success factors relating to its implementations. Following the major theme of training, Montgomeryet al.40 and Anderson-Cook et al.41 presented Six Sigma training currently incorporated into two university



Table V. Overview of proposed agenda for future research

Proposed area Level Possible outcomes

Opinion surveys All Improved understanding of needs for researchand improvements

Quantitative analyses of managementpractices

Macro Improved adoption and management guidelines

New statistics methods Micro (newsub-methods)

User-friendly software offering additionalmethod options

Meso-analyses of project databases Meso and macro Improved training materials and strategies,expert system software

Optimal design of project strategies Meso Improved training materials and strategies,expert system software

Testbed for project decision-makingevaluation

Meso Criteria for theoretical evaluation of Six Sigmaand other strategies

programs at Arizona State University and Virginia Tech. Edgeman et al.42, Paterson et al.43, Neagu and Hoerl44,Frings and Grant45, and Noble46 all presented detailed case studies involving non-manufacturing applicationscontinuing the trend away from the traditional application of Six Sigma. These authors represent a good mix ofacademic and industrial backgrounds.

7.2. Agenda for future research

Considering that methods such as X̄ and R charting and regular fractional factorials used in Six Sigma wereproposed by researchers, it seems reasonable to expect researchers to continue to make useful contributionsto Six Sigma. In general, Cooper and Noonan47, Linderman et al.3, and Snee48 suggest that, in general, toomuch research has been focused on descriptions of practice rather than on theory development that is of use tomanagers and scholars.

As a preliminary to our proposed agenda, consider the descriptors:

• micro—dealing with individual statistical methods;• meso—supervisor-level decision-making about method selection and timing such as which methods to

apply and project budgets, objectives, and timing;• macro—related to overall quality programs including stock performance.

Table V overviews the proposed areas for future research. The first two represent continuing on-going threadsof research likely to be received gratefully by practitioners in the short run. In general, we are not aware of anyresearch that treats Six Sigma as a product and surveys what its customers, i.e. users of the method, believe areopportunities for its improvement. Also, continued quantitative research on the value of Six Sigma programswill likely be of interest to stock holders and management partly because past results are somewhat inconclusive.

Goh et al.35 and others analyzed the macro-level effects of the adoption of Six Sigma on corporate stockperformance and found hints of short-lived benefits whereas their long-term analysis was largely inconclusive.Those authors also included specific caveats about the ability to connect Six Sigma programming effects atdivisions with overall parent company performance. Sousa and Voss2 highlighted the continuing need forempirical justifications including macro-level assertions in the QM literature. Examples include self-reportedprofits, the effects of success factors, and advocacy for Six Sigma in general.

Snee48,49 calls for research to help practitioners identify a robust set of what are essentially ‘micro-level’improvement tools to be used in conjunction with the DMAIC process. The focus in these recommendations isnot so much on new techniques as on refined techniques associated with specific phases. However, we suggestthat advances in computational speed and optimization heuristics provide unprecedented opportunities for newmethod development of all types. For example, Allen and Bernshteyn50 proposed new fractional factorial arraysrelevant for high cost experimentation. It is likely that their micro-level methods could not have been developed



before the advent of modern computing. Also, new methods can potentially dominate many or all performancecriteria relative to time-tested methods, e.g., the EIMSE optimal DOE arrays from Allen et al.51 offer methodswith fewer runs and lower expected prediction errors than central composite or Box Behnken designs.

The definition of Six Sigma in Section 2 suggests that it is a method with a ‘meso-level’ or project-levelscope and focus. For example, DMAIC and DMADV specify the phases in a project. Also, the principle of costjustification applies at the entire projects and not individual component methods or phases. Owing to Six Sigma’sfocus on the meso level, our proposed research agenda also focuses on meso-level analyses. Related questionsinclude the following.

(1) Which methods should be applied to which problems and in which phases?(2) Do additional investments in training make financial sense?(3) Does it make sense to terminate a given project early or apply a different method?(4) Is a Six Sigma program in statistical control or should management intervene?(5) Can the methods to forecast savings in a given operation be improved?

Related to meso-level research and the above questions, Six Sigma has spawned the creation of manycorporate databases containing the methods used in projects and the associated financial results. Althoughthe majority of these databases are confidential, practitioners at specific companies generally have accessto their own organization’s database. Brady52 proposed several approaches for analyzing such databases toanswer meso-level questions. The data were also analyzed using regression and Markov decision processes.Results included prescriptive recommendations about which core or sub-methods can be expected (at therelated manufacturing unit) to achieve the highest profits. Additional research can consider larger databasesand, possibly, achieve stronger inferences with larger scope than a single manufacturing unit. Also, a widervariety of possible analysis methods can be considered including neural nets, logistic regression, and manyother techniques associated with data mining. Each of these methods might offer advantages in specific contextsand answer new types of questions. Overall, it seems the topic of analyses of project databases is largelyunexplored.

The definition of Six Sigma as an improvement or design method suggests that it might be useful toexplore evaluation or Six Sigma and specific core method selections as if they were optimization algorithms.Implementation timing, costs, and the quality of solutions derived can be compared with alternatives usingtest problems. ‘Testbeds’ that include sets of test projects can be developed and published. Possible benefitsinclude scenarios usable in training and immediate, quantitative feedback about specific and general strategies.This benefit is particularly relevant considering the difficulties of macro-level financial measurements.Using testbeds, it might be possible to detect whether a method that goes ‘beyond Six Sigma’ in an importantsense has been created.

7.3. Conclusions

We have proposed that Six Sigma is both a method and two principles. These principles related to building andmaintaining management support and to fostering use of methods among practitioners who are not expertsin statistics. Trends in the literature included an increasing academic participation and broader focus thansolely on manufacturing. We found only partial consensus about the factors that make Six Sigma effective.We suggested opportunities for new research on Six Sigma including the development of more realistic projectpayback models, clarifying which techniques are most applicable in which situations, and possibly even for thedevelopment of new statistical methods with clear advantages for business.

Acknowledgements

We would like to thank Gavin Richards and Chaitanya Joshi for their contributions to the preparation of the text.We thank the Edison Welding Institute for support of related research.



REFERENCES

1. Ahire S, Landeros R, Golhar D. Total quality management: A literature review and an agenda for future research.Production and Operations Management 1995; 4(3):277–306.

2. Sousa R, Voss C. Quality management re-visited: A reflective review and agenda for future research. Journal ofOperations Management 2002; 20:91–109.

3. Linderman K, Schroeder R, Zaheer S, Choo A. Six Sigma: A goal-theoretic perspective. Journal of OperationsManagement 2003; 21(2):193–203.

4. Zain Z, Dale B, Kehoe D. Total quality management: An examination of the writings from a U.K. perspective. TheTQM Magazine 2001; 13(2):129–137.

5. Dean J, Bowen D. Managing theory and total quality: Improving research and practice through theory development.Academy of Management Review 1994; 19(3):392–418.

6. Harry M. Six Sigma: A breakthrough strategy for profitability. Quality Progress 1998; 31(5):60–62.7. Hahn G, Hill W, Hoerl R, Zinkgraf S. The impact of Six Sigma Improvement—a glimpse into the future of statistics.

The American Statistician 1999; 53(3):208–215.8. Bisgaard S, Freiesleben J. Quality quandaries: Economics of Six Sigma program. Quality Engineering 2000;

13(2):325–331.9. Montgomery D. Beyond Six Sigma. Quality and Reliability Engineering International 2001; 17(4):iii–iv.

10. Breyfogle FW. Implementing Six Sigma: Smarter Solutions Using Statistical Methods. Wiley: New York, 2003.11. Pande P, Neuman R, Cavanaugh R. The Six Sigma Way: How GE, Motorola, and Other Top Companies are Honing

Their Performance. McGraw-Hill: New York, 2000.12. Hahn G, Doganaksoy N, Stanard C. Statistical tools for Six Sigma—what to emphasize and de-emphasize in training.

Quality Progress 2001; 34(9):78–82.13. Allen T. Introduction to Engineering Statistics and Six Sigma: Statistical Quality Control and Design of Experiments

and Systems. Springer: London, 2006.14. Oakland J. Total Quality Management. Butterworth-Heinemann: London, 1989.15. Clifford L. Trend spotting—why you can safely ignore Six Sigma. Fortune 2001; 143(2):140.16. Mason R, Young J. Interpretive features of a T(2) chart in multivariate SPC. Quality Progress 2000; 33(4):84–89.17. Coleman S, Arunakumar G, Foldvary F, Feltham R. SPC as a tool for creating a successful business measurement

framework. Journal of Applied Statistics 2001; 28(3–4):325–334.18. McCarthy B, Stauffer R. Enhancing Six Sigma through simulation with IGRAFX process for Six Sigma. Proceedings

of the 2001 Winter Simulation Conference. IEEE Press: Piscataway, NJ, 2001; 1241–1247.19. Koch P. Probabilistic design: Optimizing for Six Sigma quality. Proceedings of the 43rd AIAA/ASME/ASCE/AHS

Structures, Structural Dynamics, and Materials Conference, 4th AIAA Non-Deterministic Approaches Forum, Denver,CO, 2002. American Institute of Aeronautics and Astronautics: Reston, VA, 2002; paper AIAA-2002-1471.

20. Goh T. A strategic assessment of Six Sigma. Quality and Reliability Engineering International 2002; 18(5):403–410.21. Buck C. Health care through a Six Sigma lens. Milbank Quarterly 1998; 76(4):749–753.22. Rayner B. Market-driven quality: IBM’s Six Sigma crusade. Electronic Business 1990; 1(10):68–74.23. Snee R. Impact of Six Sigma on quality engineering. Quality Engineering 2000; 12(3):ix–xiv.24. Does R, van den Heuvel E, de Mast J, Bisgaard S. Quality quandaries: Comparing nonmanufacturing with traditional

applications of Six Sigma. Quality Engineering 2002; 15(1):177–182.25. Hoerl R. Six Sigma Black Belts: What do they need to know? Journal of Quality Technology 2001; 33(4):391–406.26. Hoerl R. Response—Six Sigma Black Belts: What do they need to know? Journal of Quality Technology 2001;

33(4):432–435.27. Montgomery D, Lawson C, Molnau W, Elias R. Six Sigma Black Belts: What do they need to know? Journal of Quality

Technology 2001; 33(4):407–409.28. Sanders D, Hild C. Six Sigma on business processes: Common organizational issues. Quality Engineering 2000;

12(4):603–610.29. Wiklund H, Wiklund P. Widening the Six Sigma concept: An approach to improve organizational learning. Total

Quality Management 2002; 13(2):233–239.30. Snee R. Six Sigma improves both statistical training and processes. Quality Progress 2000; 33(10):68–72.31. Lee K. Critical success factors of Six Sigma implementation and the impact on operations performance. PhD

Dissertation, Cleveland State University, 2002.32. Garvin DA. Managing Quality: The Strategic and Competitive Edge. Free Press: New York, 1988.33. Hahn G, Hoerl R. Key challenges for statisticians in business and industry. Quality Progress 1998; 31(8):195–200.



34. Gautreau N, Yacout S, Hall R. Simulation of partially observed Markov decision process and dynamic qualityimprovement. Computers and Industrial Engineering 1997; 32(4):691–700.

35. Goh T, Low P, Tsui K, Xie M. Impact of Six Sigma implementation on stock price performance. Total QualityManagement and Business Excellence 2003; 14(7):753–763.

36. Harry M. A new definition aims to connect quality with financial performance. Quality Progress 2000; 33(1):64–66.37. Walters L. Six Sigma: Is it really different? Quality and Reliability Engineering International 2005; 21(3):221–224.38. Hahn GJ. Six Sigma: 20 key lessons learned. Quality and Reliability Engineering International 2005; 21(3):225–233.39. Snee RD. Leading business improvement: A new role for statisticians and quality professionals. Quality and Reliability

Engineering International 2005; 21(3):235–242.40. Montgomery DC, Burdick RK, Lawson CA, Molnau WE, Zenzen F, Jennings CL, Shah HK, Sebert DM, Bowser MD,

Holcomb DR. A university-based Six Sigma program. Quality and Reliability Engineering International 2005;21(3):243–248.

41. Anderson-Cook CM, Patterson A, Hoerl R. A structured problem-solving course for graduate students: Exposingstudents to Six Sigma as part of their university training. Quality and Reliability Engineering International 2005;21(3):249–256.

42. Edgeman RL, Bigio D, Ferleman T, Six Sigma and business excellence: Strategic and tactical examination of ITservice level management at the Office of the Chief Technology Officer of Washington, DC. Quality and ReliabilityEngineering International 2005; 21(3):257–273.

43. Patterson A, Bonissone P, Pavese M. Six Sigma applied throughout the lifecycle of an automated decision system.Quality and Reliability Engineering International 2005; 21(3):275–292.

44. Neagu R, Hoerl R. A Six Sigma approach to predictiong corporate defaults. Quality and Reliability EngineeringInternational 2005; 21(3):293–309.

45. Frings GW, Grant L. Who moved my Sigma . . . effective implementation of Six Sigma methodology to hospitals.Quality and Reliability Engineering International 2005; 21(3):311–328.

46. Noble T. Six Sigma boosts the bottom line. Chemical Engineering Progress 2001; 97(4):9–11.47. Cooper N, Noonan P. Do teams and Six Sigma go together? Quality Progress 2003; 36(6):25–28.48. Snee R. Why should statisticians pay attention to Six Sigma? Quality Progress 1999; 32(9):100–103.49. Snee R. ‘Dealing with the Achilles’ heel of Six Sigma initiatives—project selection is key to success. Quality Progress

2001; 34(3):66.50. Allen TT. Bernshteyn M. Supersaturated designs that maximize the probability of finding the active factors.

Technometrics 2003; 45(1):1–8.51. Allen TT, Yu L, Schmitz J. The expected integrated mean squared error experimental design criterion applied to die

casting machine design. Journal of the Royal Statistical Society Series C: Applied Statistics 2003; 52(1):1–15.52. Brady JE. Six Sigma and the university: Teaching, research, and meso-analysis. PhD Dissertation, The Ohio State

University, Industrial and Systems Engineering, 2005.

APPENDIX A. THE ARTICLE DATABASE

The descriptors used in the table below are defined in Section 3. The complete references to the articles aredescribed below in Appendix B.



Author(s)

Year

Authorship

DefineDMAIC

Define3.4

Topic(s)Version1Oakland14

Topic(s)Version2SousaandVoss2

Industrialsector

Impactfactor

Researchapproach

Speculativeinnature?

Successfactors

Managementcommitted

Rightteam

Training

Changemanagement

Datasystem

Teaminvolvement

Projectselection

Bottomline

Projectleadership

Goalsbased

Structuredapproach

Customerfocused

Adaptablesystem

Abr

aham

etal

.20

01A

00

PeTo

PhTo

G1.

5C

oN

N—

——

——

——

——

——

——

Ack

erm

ann

1993

I0

0Pe

ToPh

ToG

0.7

CN

N—

——

——

——

——

——

——

Ack

erm

ann

etal

.19

93I

01

ToTo

M0.

7C

NN

——

——

——

——

——

——

—A

liet

al.

1999

I0

0To

ToM

1.5

CN

N—

——

——

——

——

——

——

Ant

ony

etal

.20

02I

01

SyPh

M0.

3C

YN

——

——

——

——

——

——

—A

rvid

sson

2003

A0

0Sy

PhM

0.3

SuN

N—

——

——

——

——

——

——

Bai

ley

2001

I0

0Pe

SyPh

M1.

5C

NN

——

——

——

——

——

——

—B

arto

s19

99I

00

SyPh

M0.

3TA

YY

—Y

——

——

——

——

——

—B

asu

2001

I0

1Sy

PhM

0.2

Co

NN

——

——

——

——

——

——

—B

ehar

aet

al.

1995

IA

01

SyPh

MN

AC

NN

——

——

——

——

——

——

—B

ened

etto

2003

I1

0Sy

PhSe

NA

CN

YY

—Y

Y—

——

——

——

——

Ber

low

itz20

03A

01

ToTo

Se2.

9C

RN

Y—

——

—Y

——

——

——

——

Bin

der

1997

I1

1Sy

PhM

0.8

Co

NN

——

——

——

——

——

——

—B

isga

ard

etal

.20

00A

00

SyPh

MN

ATA

YN

——

——

——

——

——

——

—B

lake

slee

1999

I0

0Sy

PhSe

0.2

CY

YY

—Y

—Y

Y—

Y—

——

——

Bla

nton

2002

A0

0Sy

PhSe

NA

CY

N—

——

——

——

——

——

——

Bos

sert

2003

I0

0Sy

PhG

0.2

TAY

N—

——

——

——

——

——

——

Bre

yfog

le20

02I

00

SyPh

Se0.

2C

NN

——

——

——

——

——

——

—B

reyf

ogle

etal

.20

01a

I0

0Pe

SyPh

G1.

5TA

YN

——

——

——

——

——

——

—20

01b

I0

0To

ToG

0.2

CY

N—

——

——

——

——

——

——

2002

I1

1Sy

PhSe

NA

TAY

N—

——

——

——

——

——

——

2003

I0

0Sy

PhM

0.7

TAY

N—

——

——

——

——

——

——

Bro

deri

cket

al.

2002

A0

0Sy

PhSe

NA

CN

N—

——

——

——

——

——

——

Buc

k19

98I

11

SyPh

Se1.

9C

SuR

NN

——

——

——

——

——

——

—20

01I

11

SyPh

SeN

AC

NN

——

——

——

——

——

——

—B

uck

etal

.20

01I

10

SyPh

SeN

AC

NY

Y—

——

——

——

——

——

—B

uggi

e20

00I

00

SyPh

M0.

1TA

YN

——

——

——

——

——

——

—



Author(s)

Year

Authorship

DefineDMAIC

Define3.4



Industrialsector

Impactfactor

Researchapproach


Successfactors

Managementcommitted

Rightteam

Training

Changemanagement

Datasystem

Teaminvolvement

Projectselection

Bottomline

Projectleadership

Goalsbased

Structuredapproach

Customerfocused

Adaptablesystem

Car

d20

00I

00

ToTo

M0.

8C

oN

N—

——

——

——

——

——

——

Cau

lcut

t20

01I

11

SyTo

PhPr

M0.

3C

NY

——

——

Y—

——

——

——

—C

han

etal

.20

01A

01

ToTo

M0.

4C

YN

——

——

——

——

——

——

—C

hass

in19

98A

01

SyPh

Se1.

9R

NN

——

——

——

——

——

——

—C

how

dhur

y20

00I

10

SyPh

G0.

3TA

YY

——

Y—

——

——

——

——

—C

liffo

rd20

01I

01

SyPh

MN

AC

Co

NY

Y—

——

——

——

——

——

—C

olem

anet

al.

2001

IA

00

ToTo

M0.

3TA

YN

——

——

——

——

——

——

—C

onno

lly20

03I

00

SyPh

M0.

7C

NN

——

——

——

——

——

——

—C

onne

r20

03I

00

SyPh

M0.

3C

NN

——

——

——

——

——

——

—C

oope

r19

92I

01

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M0.

1C

NN

——

——

——

——

——

——

—20

03I

00

PeSy

PhSe

0.2

SuN

Y—

——

——

Y—

——

——

——

Cro

m20

00I

00

PeSy

PhG

0.2

Co

YY

Y—

——

——

——

——

——

—D

asgu

pta

2003

A1

1Pe

SyPh

SeN

AC

YY

Y—

——

——

——

——

——

—D

avie

s20

01A

00

SyPh

Se0.

8R

NN

——

——

——

——

——

——

—D

avig

etal

.20

03A

00

SyPh

M0.

2Su

NY

Y—

——

——

——

——

——

—D

eM

ast

2003

A0

0Sy

PhG

0.2

Co

YN

——

——

——

——

——

——

—D

eM

aste

tal.

2000

A1

0Sy

PhG

0.2

Co

NN

——

——

——

——

——

——

—D

edhi

a19

95I

00

SyPh

SeN

ATA

YY

Y—

Y—

——

——

——

——

—D

eFeo

2000

I0

1Pe

SyPh

GN

ASu

NN

——

——

——

——

——

——

—D

eshp

ande

1998

A0

0To

ToM

0.2

CY

N—

——

——

——

——

——

——

Des

hpan

deet

al.

1999

IA

11

SyPh

G0.

4C

NN

——

——

——

——

——

——

—D

oes

etal

.20

02A

10

SyPh

SeN

AC

oN

N—

——

——

——

——

——

——

Dog

anak

soy

etal

.20

00I

A0

0To

ToM

0.2

CY

N—

——

——

——

——

——

——

Dor

nhei

m20

01I

00

ToTo

M0.

3TA

YN

——

——

——

——

——

——

—D

ougl

as20

00I

00

SyPh

GN

AC

SuN

N—

——

——

——

——

——

——

Du

etal

.20

00A

01

ToPr

ToM

0.5

Co

YN

——

——

——

——

——

——

—D

ugue

saoy

etal

.20

02I

00

ToTo

G0.

4C

NN

——

——

——

——

——

——

—



Author(s)

Year

Authorship

DefineDMAIC

Define3.4



Industrialsector

Impactfactor

Researchapproach


Successfactors

Managementcommitted

Rightteam

Training

Changemanagement

Datasystem

Teaminvolvement

Projectselection

Bottomline

Projectleadership

Goalsbased

Structuredapproach

Customerfocused

Adaptablesystem

Eid

etal

.19

97A

00

ToTo

M0.

4TA

YN

——

——

——

——

——

——

—Fa

rntz

2001

I1

0Sy

PhM

0.3

CN

N—

——

——

——

——

——

——

Feng

etal

.19

97A

00

ToTo

M0.

4C

NN

——

——

——

——

——

——

—Fe

rrin

etal

.20

02I

11

ToTo

MN

ASu

NN

——

——

——

——

——

——

—Fi

nn19

99I

10

SyPh

M0.

3TA

YN

——

——

——

——

——

——

—Fo

nten

otet

al.

1994

IA

01

ToTo

M0.

2Su

NN

——

——

——

——

——

——

—Fu

ller

2000

I0

0Sy

PhM

NA

TAY

Y—

——

——

——

——

—Y

——

Gan

o20

01A

00

ToTo

GN

AC

NN

——

——

——

——

——

——

—G

autr

eau

etal

.19

97A

00

ToTo

M0.

4TA

YN

——

——

——

——

——

——

—G

ill19

90I

01

SyPh

MN

ASu

NN

——

——

——

——

——

——

—G

nibu

s20

00I

00

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G0.

2C

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——

——

——

——

——

——

—G

oh20

01A

00

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3TA

YY

Y—

——

——

——

——

——

—20

02a

A1

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NN

——

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02b

A1

0Pe

SyPh

G0.

2R

NY

——

Y—

——

Y—

——

——

—G

ohet

al.

2003

aA

10

SyPh

MN

AR

NN

——

——

——

——

——

——

—20

03b

A1

1To

ToG

NA

TAY

N—

——

——

——

——

——

——

Gor

don

2002

I0

0Sy

PhG

0.2

Co

YN

——

——

——

——

——

——

—G

rand

zole

tal.

1998

A0

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PrM

NA

SuN

N—

——

——

——

——

——

——

Gre

ek20

00I

01

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M0.

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——

——

——

——

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—G

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2001

I0

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TAY

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——

——

——

——

——

——

Hah

n20

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TAY

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——

——

——

——

——

——

Hah

net

al.

1998

I0

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SyPh

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——

——

——

——

——

——

—19

99I

11

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PhM

1.2

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——

——

—Y

——

——

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2000

I1

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NA

TAY

YY

——

—Y

—Y

——

—Y

——

2001

I0

0To

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0.2

TAY

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—Y

——

——

——

——

——

Ham

mer

2002

I1

0Sy

PhM

NA

TAN

Y—

——

——

—Y

——

——

——



Author(s)

Year

Authorship

DefineDMAIC

Define3.4



Industrialsector

Impactfactor

Researchapproach


Successfactors

Managementcommitted

Rightteam

Training

Changemanagement

Datasystem

Teaminvolvement

Projectselection

Bottomline

Projectleadership

Goalsbased

Structuredapproach

Customerfocused

Adaptablesystem

Har

rold

1999

I1

1Pe

SyPh

M0.

3C

NY

——

——

—Y

——

——

——

—H

arro

ldet

al.

1999

I1

0Sy

PhM

0.3

TAY

N—

——

——

——

——

——

——

Har

ry19

98I

11

PeSy

PhM

0.2

CN

Y—

—Y

——

——

——

——

——

2000

aI

00

SyTo

PhTo

G0.

2Su

NY

——

——

Y—

—Y

——

——

—20

00b

I0

0To

ToG

0.2

TAY

N—

——

——

——

——

——

——

2000

cI

00

ToTo

G0.

2TA

YN

——

——

——

——

——

——

—20

00d

I0

1Sy

PhG

0.2

TAY

N—

——

——

——

——

——

——

2000

eI

00

ToTo

G0.

2TA

YN

——

——

——

——

——

——

—20

00f

I0

0To

ToG

0.2

TAY

N—

——

——

——

——

——

——

Hen

retta

etal

.20

03I

11

SyPh

Se0.

2C

NN

——

——

——

——

——

——

—H

ildet

al.

2000

I0

0Sy

PhM

NA

Co

NN

——

——

——

——

——

——

—H

ill20

01I

00

PeTo

PhTo

M1.

5C

NN

——

——

——

——

——

——

—H

oerl

1998

I0

0Sy

PhG

0.2

CN

Y—

Y—

——

——

——

——

——

2001

aI

00

PeSy

PhG

1.5

TAN

Y—

——

——

——

—Y

——

——

2001

bI

00

PeSy

PhG

1.5

TAY

N—

——

——

——

——

——

——

Hor

st19

99I

00

SyPh

Se0.

3C

Co

NN

——

——

——

——

——

——

—H

owel

l20

00I

00

SyPh

M0.

3Su

NN

——

——

——

——

——

——

—20

01I

00

SyPh

M0.

3Su

NY

Y—

——

——

——

——

——

—H

unte

r19

99I

00

SyPh

Se0.

3C

NN

——

——

——

——

——

——

—20

00I

00

SyPh

G0.

3TA

YN

——

——

——

——

——

——

—H

unte

ret

al.

1999

I0

0Sy

PhM

0.3

CN

YY

——

——

——

——

——

——

Hut

chin

s20

00I

00

PeSy

PhG

0.2

TAY

N—

——

——

——

——

——

——

Ingl

eet

al.

2001

IA

11

PeSy

PhM

NA

Co

NN

——

——

——

——

——

——

—Jo

hnso

n20

02I

01

PeSy

PhG

0.3

TAY

N—

——

——

——

——

——

——

John

son

etal

.20

03I

11

SyPh

M0.

3Su

NN

——

——

——

——

——

——

—Jo

hnst

one

etal

.20

02I

01

SyPh

Se2.

9C

NN

——

——

——

——

——

——

—20

03I

01

SyTo

PhPr

Se1.

2C

NY

Y—

——

——

——

——

——

—



Author(s)

Year

Authorship

DefineDMAIC

Define3.4



Industrialsector

Impactfactor

Researchapproach


Successfactors

Managementcommitted

Rightteam

Training

Changemanagement

Datasystem

Teaminvolvement

Projectselection

Bottomline

Projectleadership

Goalsbased

Structuredapproach

Customerfocused

Adaptablesystem

Kan

debo

1999

I0

0Sy

PhM

0.3

TAN

N—

——

——

——

——

——

——

Kan

e19

98I

01

SyPh

M0.

2C

NN

——

——

——

——

——

——

—K

azm

eret

al.

2002

A0

1To

ToM

NA

CY

N—

——

——

——

——

——

——

Kaz

mie

rcza

k20

03A

01

ToTo

Se1.

6R

NN

——

——

——

——

——

——

—K

enda

llet

al.

2000

I0

0To

ToG

0.2

TAY

N—

——

——

——

——

——

——

Ken

ette

tal.

2003

IA

00

ToTo

M0.

2C

NN

——

——

——

——

——

——

—K

now

les

etal

.20

03A

00

ToTo

Se0.

2C

NN

——

——

——

——

——

——

—K

och

2002

I0

1To

ToM

0.8

CN

N—

——

——

——

——

——

——

Koo

nce

etal

.20

03A

00

ToTo

M0.

4C

YN

——

——

——

——

——

——

—K

rouw

er20

02I

01

ToTo

Se0.

8C

NN

——

——

——

——

——

——

—K

unes

2002

I1

0Sy

PhG

0.2

TAY

N—

——

——

——

——

——

——

Lan

din

etal

.20

01A

00

SyPh

G0.

3Su

NY

——

——

——

——

——

Y—

—L

effe

wet

al.

2001

IA

11

SyTo

PhTo

M0.

4C

NN

——

——

——

——

——

——

—L

inde

rman

etal

.20

03A

11

SyPh

G1.

5TA

YY

——

——

——

——

—Y

——

—L

ucas

2002

aI

11

SyTo

PrTo

G0.

2TA

YY

Y—

——

——

——

——

——

—20

02b

I0

0Sy

PhG

0.2

TAY

N—

——

——

——

——

——

——

Mad

er20

02I

00

SyPh

M0.

2TA

YN

——

——

——

——

——

——

—M

agui

re19

99a

I0

1To

ToM

0.2

TAN

N—

——

——

——

——

——

——

1999

bI

01

SyPh

M0.

2C

NY

——

——

——

——

——

Y—

—M

anda

leta

l.19

98A

00

PeTo

PhPr

GN

ASu

RN

YY

YY

——

——

——

—Y

——

Mas

onet

al.

2000

I1

0To

ToG

0.2

CN

Y—

——

——

——

——

——

——

McC

arth

yet

al.

2001

I1

1To

ToM

NA

CN

N—

——

——

——

——

——

——

McF

adde

1993

A1

1Sy

PhM

0.2

TAY

N—

——

——

——

——

——

——

Mon

tgom

ery

2000

A0

0Pe

ToPh

PrG

0.2

TAY

N—

——

——

——

——

——

——

2001

A0

0Sy

PhG

0.2

TAY

YY

——

——

——

——

Y—

——

2002

A0

0Pe

ToPh

PrG

0.2

TAY

N—

——

——

——

——

——

——



Author(s)

Year

Authorship

DefineDMAIC

Define3.4



Industrialsector

Impactfactor

Researchapproach


Successfactors

Managementcommitted

Rightteam

Training

Changemanagement

Datasystem

Teaminvolvement

Projectselection

Bottomline

Projectleadership

Goalsbased

Structuredapproach

Customerfocused

Adaptablesystem

Mon

tgom

ery

etal

.20

01I

A0

0Pe

SyPh

G1.

5TA

YN

——

——

——

——

——

——

—M

ukes

h20

03I

11

SyPh

M0.

4C

NN

——

——

——

——

——

——

—M

unro

2000

I0

1Sy

PhM

0.2

Co

NN

——

——

——

——

——

——

—M

urug

appa

net

al.

2003

I0

0Sy

ToPh

PrSe

0.8

CN

N—

——

——

——

——

——

——

Nav

e20

02I

10

SyPh

G0.

2C

oY

N—

——

——

——

——

——

——

Neu

sche

ler

etal

.20

01I

10

SyPh

G0.

2TA

YN

——

——

——

——

——

——

—N

eval

aine

net

al.

2000

aI

00

SyPh

Se1.

3TA

YN

——

——

——

——

——

——

—20

00b

I1

1Sy

PhSe

1.3

Co

NN

——

——

——

——

——

——

—N

iels

enet

al.

1999

I0

0Sy

PhSe

0.3

CN

N—

——

——

——

——

——

——

Nob

le20

01I

01

SyPh

M0.

4Su

NN

——

——

——

——

——

——

—O

lexa

2003

I0

0To

PrM

0.3

CN

Y—

——

—Y

——

Y—

——

——

Pear

son

2001

I0

0To

ToG

0.2

TAY

N—

——

——

——

——

——

——

Plot

kin

etal

.19

99I

01

SyPh

MN

AC

NN

——

——

——

——

——

——

—Py

zdek

2001

aI

00

SyPh

MN

AC

oN

N—

——

——

——

——

——

——

2001

bI

00

PeTo

PhTo

G1.

5TA

YN

——

——

——

——

——

——

—R

ambe

rg20

00A

01

SyPh

GN

ATA

YY

——

——

——

——

Y—

——

—R

asis

etal

.20

02a

IA

11

SyPh

MN

AC

NN

——

——

——

——

——

——

—20

02b

IA

10

SyPh

MN

AC

NN

——

——

——

——

——

——

—R

ayne

r19

90I

00

SyPh

GN

AC

NN

——

——

——

——

——

——

—R

ibar

doet

al.

2003

IA

00

ToTo

M0.

2C

NN

——

——

——

——

——

——

—R

iley

etal

.20

02A

10

SyPh

SeN

AC

NN

——

——

——

——

——

——

—R

owla

nds

etal

.20

03A

00

ToTo

M0.

6C

NN

——

——

——

——

——

——

—Sa

nder

set

al.

2000

aI

00

SyPh

GN

ATA

YY

Y—

Y—

——

—Y

——

—Y

—20

00b

I0

0Sy

PhSe

NA

CN

N—

——

——

——

——

——

——

2001

I0

0Sy

PhG

NA

TAY

N—

——

——

——

——

——

——

Sare

witz

2000

I0

0Sy

PhSe

1.3

TAY

N—

——

——

——

——

——

——



Author(s)

Year

Authorship

DefineDMAIC

Define3.4



Industrialsector

Impactfactor

Researchapproach


Successfactors

Managementcommitted

Rightteam

Training

Changemanagement

Datasystem

Teaminvolvement

Projectselection

Bottomline

Projectleadership

Goalsbased

Structuredapproach

Customerfocused

Adaptablesystem

Scal

ise

2001

I0

0Sy

PhM

NA

SuN

N—

——

——

——

——

——

——

2003

I0

0Sy

PhSe

NA

CN

N—

——

——

——

——

——

——

Schm

itt20

00I

01

SyPh

M0.

3Su

NN

——

——

——

——

——

——

—20

01I

01

SyPh

M0.

3Su

NN

——

——

——

——

——

——

—20

02I

01

SyPh

G0.

3Su

NN

——

——

——

——

——

——

—Si

gale

tal.

2001

A1

0Sy

PhSe

NA

CN

N—

——

——

——

——

——

——

Smith

2003

I0

0Sy

PhM

0.2

CN

YY

——

——

——

——

——

——

Snee

1999

I1

1Pe

SyPh

G0.

2TA

YY

——

——

—Y

—Y

——

Y—

—20

00a

I0

1Pe

SyPh

GN

AR

YN

——

——

——

——

——

——

—20

00b

I0

0Pe

ToPh

ToG

0.2

TAY

Y—

—Y

——

——

——

——

——

2001

aI

00

ToTo

G0.

2TA

YY

Y—

——

——

Y—

——

——

—20

01b

I0

0Pe

ToPh

ToG

1.5

TAY

N—

——

——

——

——

——

——

2003

I1

0Sy

PhG

0.2

TAY

N—

——

——

——

——

——

——

Stam

atis

2000

I0

0Sy

PhM

NA

CC

oY

N—

——

——

——

——

——

——

Stei

n20

01I

00

ToTo

G0.

2TA

YN

——

——

——

——

——

——

—St

udt

2002

I1

0Sy

PhG

0.7

TAY

Y—

Y—

——

——

——

——

——

Taki

kam

alla

1994

A0

1To

ToM

0.2

TAY

N—

——

——

——

——

——

——

Tang

etal

.19

97A

00

ToTo

M0.

2C

oY

N—

——

——

——

——

——

——

Tre

ichl

eret

al.

2002

I0

0To

PrG

0.2

CN

YY

YY

—Y

Y—

——

—Y

Y—

Tri

vedi

2002

I0

0Sy

PhM

0.4

CN

Y—

—Y

——

——

——

——

——

Tylu

tkie

tal.

2002

A0

0Sy

PhSe

0.2

CN

N—

——

——

——

——

——

——

Van

denb

rand

e19

98I

00

ToTo

M0.

2TA

YN

——

——

——

——

——

——

—V

augh

am19

98A

01

ToTo

GN

AC

NN

——

——

——

——

——

——

—V

eloc

ci19

98a

I0

1Sy

PhM

0.3

Co

NN

——

——

——

——

——

——

—19

98b

I0

0Sy

PhM

0.3

CN

N—

——

——

——

——

——

——

1998

cI

00

SyPh

M0.

3C

NN

——

——

——

——

——

——

—20

00I

01

SyPh

M0.

3C

NN

——

——

——

——

——

——

—20

02I

00

SyPh

M0.

3Su

NY

—Y

——

Y—

—Y

——

—Y

Y



Author(s)

Year

Authorship

DefineDMAIC

Define3.4



Industrialsector

Impactfactor

Researchapproach


Successfactors

Managementcommitted

Rightteam

Training

Changemanagement

Datasystem

Teaminvolvement

Projectselection

Bottomline

Projectleadership

Goalsbased

Structuredapproach

Customerfocused

Adaptablesystem

Voe

lkel

2002

A0

0To

ToG

0.2

CN

Y—

—Y

——

——

——

——

——

Wal

shet

al.

2000

I0

0Sy

PhG

0.3

SuN

N—

——

——

——

——

——

——

Wat

son

2000

I0

0Pe

SyPh

G0.

2C

oN

N—

——

——

——

——

——

——

2002

aI

00

ToTo

Se0.

2C

Co

NN

——

——

——

——

——

——

—20

02b

I1

0Sy

PhSe

0.5

TAY

N—

——

——

——

——

——

——

Wau

rayn

iak

2002

I0

0To

ToM

0.3

SuN

Y—

——

—Y

——

——

——

——

Wei

nste

inet

al.

1998

A0

0Pe

SyPh

G0.

2Su

NN

——

——

——

——

——

——

—W

estg

ard

2002

I0

0To

PrTo

SeN

ATA

NN

——

——

——

——

——

——

—W

heel

er20

02I

00

SyPh

M0.

4C

oN

Y—

——

—Y

——

——

——

——

Wik

lund

etal

.20

02A

00

SyPh

SeN

AC

NY

Y—

Y—

Y—

——

——

Y—

—W

ood

2001

I0

0Sy

PhG

0.3

CN

YY

Y—

——

——

——

——

——

Wyp

eret

al.

2000

I1

1Pe

SyPh

SeN

AC

NN

——

——

——

——

——

——

—Y

eung

etal

.20

03A

00

SyPh

M1.

5Su

NY

Y—

——

——

——

——

——

—Y

uet

al.

1994

A0

0Sy

PhM

0.4

RN

N—

——

——

——

——

——

——



APPENDIX B. ARTICLE REFERENCE LISTING

Abraham B, Mackay J. Discussion of Six Sigma Black Belts: What do they need to know? Journal of QualityTechnology 2001; 33(4):410–413.

Ackermann C. Supplier improvement via SPC application workshops. IEEE Transactions on SemiconductorManufacturing 1993; 6(2):178–183.

Ackermann C, Fabia J. Monitoring supplier quality at ppm levels. IEEE Transactions on SemiconductorManufacturing 1993; 6(2):189–195.

Ahire S, Landeros R, Golhar D. Total quality management: A literature review and an agenda for future research.Production and Operations Management 1995; 4(3):277–307.

Ali O, Chen Y. Design quality and robustness with neural networks. IEEE Transactions on Neural Networks1999; 10(6):1518–1527.

Antony J, Coronado R. Design for Six Sigma. Manufacturing Engineer 2002; 81(1):24–26.

Arvidsson M, Gremyr I, Johansson P. Use and knowledge of robust design methodology: A survey of Swedishindustry. Journal of Engineering Design 2003; 14(2):129–143.

Bailey S. Discussion of Six Sigma Black Belts: What do they need to know? Journal of Quality Technology2001; 33(4):426–431.

Bartos F. Six Sigma for complex systems. Control Engineering 1999; 46(3):90.

Basu R. Six Sigma to fit sigma. IIE Solutions 2001; 33(7):28–33.

Behara R, Fontenot G, Gresham A. Customer satisfaction measurement and analysis using Six Sigma.International Journal of Quality and Reliability Management 1995; 12(3):9–18.

Benedetto A. Adopting manufacturing-based Six Sigma methodology to the service environment of a radiologyfilm library. Journal of Healthcare Management 2003; 48(4):263–280.

Berlowitz D. Striving for Six Sigma in pressure ulcer care. Journal of American Geriatrics Society 2003;51(9):1320–1321.

Binder R. Can a manufacturing quality model work for software? IEEE Software 1997; 14(5):101.

Bisgaard S, Freiesleben J. Quality quandaries: Economics of Six Sigma program. Quality Engineering 2000;13(2):325–331.

Blakeslee J. Implementing the Six Sigma solution—how to achieve quantum leaps in quality andcompetitiveness. Quality Progress 1999; 32(7):77–85.

Blanton P. Quality tools in science education. The Physics Teacher 2002; 40:188–189.

Bossert J. Lean and Six Sigma—synergy made in heaven. Quality Progress 2003; 36(7):31–32.

Breyfogle F. Golf and Six Sigma—use Six Sigma metrics to drive proper process behavior. Quality Progress2002; 35(11):83–85.

Breyfogle FW. Implementing Six Sigma: Smarter Solutions Using Statistical Methods. Wiley: New York, 2003.

Breyfogle F, Connolly M. Six Sigma methods to ensure organizations health. R&D Magazine 2003;45(4):28–29.

Breyfogle F, Enck D. Six Sigma goes corporate. Business Management 2002; (May):70.

Breyfogle F, Enck D, Meadows B. Discussion of Six Sigma Black Belts: What do they need to know? Journalof Quality Technology 2001a; 33(4):424–425.

Breyfogle F, Meadows B. Bottom-line success with Six Sigma—define key process output variables and theireffects on the cost of poor quality. Quality Progress 2001b; 34(5):101–104.

Broderick L, Knuteson H, Rankin R, Woodward L. Use of Six Sigma methodology to enhance capacitymanagement in an academic center-first year’s experience. Proceedings of the 88th Meeting of theRadiology Society of North America, 2002. Radiology Society of North America: Oak Rook, IL, 2002.



Buck C, Miller R, Desmarais J. Six Sigma—the quest for quality. Hospitals and Health Networks 2001;75(12):41–48.

Buck C. Health care through a Six Sigma lens. Milbank Quarterly 1998; 76(4):749–753.

Buck C. What hospital leaders say about Six Sigma. Hospitals and Health Networks 2001; 75(12):43.

Buggie F. Beyond ‘Six Sigma’. Journal of Management Engineering 2000; 16(4):28–31.

Card D. Sorting out Six Sigma and the CMM. IEEE Software 2000; 17(3):11–13.

Caulcutt R. Why is Six Sigma so successful? Journal of Applied Statistics 2001; 28(3–4):301–306.

Chan K, Spedding T. On-line optimization of quality in a manufacturing system. International Journal ofProduction Research 2001; 39(6):1127–1145.

Chassin M. Is healthcare ready for Six Sigma quality? Milbank Quarterly 1998; 76(4):565–591.

Chowdhury S. Working toward Six Sigma success. Manufacturing Engineering 2000; 127(1):14.

Clifford L. Trend spotting—why you can safely ignore Six Sigma. Fortune 2001; 143(2):140.

Coleman S, Arunakumar G, Foldvary F, Feltham R. SPC as a tool for creating a successful businessmeasurement framework. Journal of Applied Statistics 2001; 28(3–4):325–334.

Connolly M. Six Sigma deployment at DuPont. R&D Magazine 2003; 45(4):29.

Connor G. Benefiting from Six Sigma. Manufacturing Engineering 2003; 130(2):53–59.

Cooper D, Babcock J, Dipietro F. Application of 6 Sigma-statistical quality-control to monitoring the depositionof contaminating particles. Journal of the IES 1992; 35(5):27–32.

Cooper N, Noonan P. Do teams and Six Sigma go together? Quality Progress 2003; 36(6):25–28.

Crom S. Implementing Six Sigma—a cross-cultural perspective. Quality Progress 2000; 33(10):73–75.

Dasgupta T. Using the Six-Sigma metric to measure and improve the performance of a supply chain. TotalQuality Management and Business Excellence 2003; 14(3):355–366.

Davies H. Exploring the pathology of quality failings: Measuring quality is not the problem—changing it is.Journal of Evaluation in Clinical Practice 2001; 7(2):243–251.

Davig W, Brown S, Friel T, Tabibzadeh K. Quality management in small manufacturing. Industrial Managementand Data Systems 2003; 103(1–2):68–77.

Dean J, Bowen D. Managing theory and total quality: Improving research and practice through theorydevelopment. Academy of Management Review 1994; 19(3):392–418.

Dedhia N. Survive Business challenges with the total quality management approach. Total Quality Management1995; 6(3):265–272.

DeFeo J. An ROI story. Training and Development 2000; 54(7):25–26.

De Mast J. Quality improvement form the viewpoint of statistical method. Quality and Reliability EngineeringInternational 2003; 19:255–264.

De Mast J, Schippers W, Does R, van den Heuvel E. Steps and strategies in process improvement. Quality andReliability Engineering International 2000; 16(4):301–311.

Deshpande P. Emerging technologies and Six Sigma. Hydrocarbon Processing 1998; 77(4):55.

Deshpande P, Makker S, Goldstein M. Boost competitiveness via Six Sigma. Chemical Engineering Progress1999; 95(9):65–70.

Does R, van den Heuvel E, de Mast J, Bisgaard S. Quality quandaries: Comparing nonmanufacturing withtraditional applications of Six Sigma. Quality Engineering 2002; 15(1):177–182.

Doganaksoy N, Hahn G, Keeker W. Product life data analysis: A case study. Quality Progress 2000; 33(6):115.

Dornheim M. Implement Six Sigma. Aviation Week and Space Technology 2001; 155(1):25.



Douglas P, Erwin J. Six Sigma focus on total customer satisfaction. Journal of Quality and Participation 2000;23(2):45–49.

Du X, Chen W. Towards a better understanding of modeling feasibility robustness in engineering design. Journalof Mechanical Design 2000; 122(4):385–394.

Duguesnoy L, Berger J, Prevot P, Sandoz-Guermond F. SIMPA: A Training Platform in Work Station IncludingComputing Tutors (Lecture Notes in Computer Science, vol. 2363). Springer: Berlin, 2002; 507–520.

Eid M, Moghrabi C, Eldin H. A simulation approach to evaluating quality/cost decision scenarios. Computersand Industrial Engineering 1997; 33(1–2):105–108.

Frantz K. Apply quality to motion control. Control Engineering 2001; 48(10):8–10.

Feng C, Kusiak A. Robust tolerance design with the integer programming approach. Journal of ManufacturingScience and Engineering Transactions of the ASME 1997; 119(4A):603–610.

Ferrin D, Muthler D, Miller M. Six Sigma and simulation, so what’s the correlation? Proceedings of the 2002Winter Simulation Conference. IEEE Press: Piscataway, NJ, 2002; 1439–1443.

Finn G, Six-Sigma quality for virtual products, Manufacturing Engineering 1999; 123(6):20.

Fontenot G, Behara R, Gresham A. Six Sigma in customer satisfaction. Quality Progress 1994; 27(12):73–76.

Fuller H. Observations about the success and evaluation of Six Sigma at Seagate. Quality Engineering 2000;12(3):311–315.

Gano D. Effective problem solving: A new way of thinking. Annual Quality Congress Transactions, 2001.American Society for Quality Control: Milwaukee, WI, 2001; 110–122.

Gautreau N, Yacout S, Hall R. Simulation of partially observed Markov decision process and dynamic qualityimprovement. Computers and Industrial Engineering 1997; 32(4):691–700.

Gill M. Stalking Six Sigma. Business Month 1990; (January):42–46.

Gnibus R. Six Sigma’s missing link—understanding the quality tool needed to calculate sigma ratings. QualityProgress 2000; 33(11):77.

Goh T. A pragmatic approach to experimental design in industry. Journal of Applied Statistics 2001;28(3–4):391–398.

Goh T. The role of statistical design of experiments in Six Sigma: Perspectives of a practitioner. QualityEngineering 2002a; 14(4):661–673.

Goh T. A strategic assessment of Six Sigma. Quality and Reliability Engineering International 2002b;18(5):403–410.

Goh T, Xie M. Statistical control of a Six Sigma process. Quality Engineering 2003a; 15(4):587–592.

Goh T, Low P, Tsui K, Xie M. Impact of Six Sigma implementation on stock price performance. Total QualityManagement and Business Excellence 2003b; 14(7):753–763.

Gordon D. Quality management systems vs. quality improvement. Quality Progress 2002; 35(11):86.

Grandzol J, Gershon M. A survey instrument for standardizing TQM modeling research. International Journalof Quality Science 1998; 3(1):80–105.

Greek D. Inefficiency won’t wash. Professional Engineering 2000; 13(11):45.

Gross J. A road map to Six Sigma quality. Quality Progress 2001; 34(11):24–29.

Hahn G, Hoerl R. Key challenges for statisticians in business and industry. Quality Progress 1998;31(8):195–200.

Hahn G, Hill W, Hoerl R, Zinkgraf S. The impact of Six Sigma improvement—a glimpse into the future ofstatistics. The American Statistician 1999; 53(3):208–215.

Hahn G, Doganaksoy N, Hoerl R. The evolution of Six Sigma. Quality Engineering 2000; 12(3):317–326.



Hahn G, Doganaksoy N, Stanard C. Statistical tools for Six Sigma—what to emphasize and de-emphasize intraining. Quality Progress 2001; 34(9):78–82.

Hahn G. Deming and the proactive statistician. The American Statistician 2002; 56(4):290–298.

Hammer M. Process management and the future of Six Sigma. IEEE Engineering Management Review 2002;30(4):56–63.

Harrold D. Designing for Six Sigma capability. Control Engineering 1999; 46(1):62–70.

Harrold D, Bartos F. Optimize existing processes to achieve Six Sigma capability. Control Engineering 1999;46(3):87–103.

Harry M. Six Sigma: A breakthrough strategy for profitability. Quality Progress 1998a; 31(5):60–62.

Harry M. Six Sigma article inaccurate—author’s reply. Quality Progress 1998b; 31(8):10.

Harry M. A new definition aims to connect quality with financial performance. Quality Progress 2000a;33(1):64–66.

Harry M. Six Sigma leads enterprises to coordinate efforts. Quality Progress 2000b; 33(3):70–72.

Harry M. Six Sigma focuses on improvement rates. Quality Progress 2000c; 33(6):76–80.

Harry M. Abatement of business risk is key to Six Sigma. Quality Progress 2000d; 33(7):72.

Harry M. The quality twilight zone. Quality Progress 2000e; 33(2):68.

Harry M. Quality leads, answers follow. Quality Progress 2000f; 33(5):82.

Henretta K, Walker J, Bellafiore L. Applying Six Sigma to chromatography—tutorial: Cutting costs throughprocess improvements. Genetic Engineering News 2003; 23(1):54–56.

Hild C, Sanders D, Cooper T. Six Sigma on continuous processes: How and why it differs. Quality Engineering2000; 13(1):1–9.

Hill W. Discussion—Six Sigma Black Belts: What do they need to know? Journal of Quality Technology 2001;33(4):421–423.

Hoerl R. Six Sigma and the future of the quality profession. Quality Progress 1998; 31(6):35–42.

Hoerl R. Six Sigma Black Belts: What do they need to know? Journal of Quality Technology 2001a;33(4):391–406.

Hoerl R. Response—Six Sigma Black Belts: What do they need to know? Journal of Quality Technology 2001b;33(4):432–435.

Horst R. Safety and Six Sigma. Manufacturing Engineering 1999; 122(2):14.

Howell D. The power of six. Professional Engineering 2000; 13(14):34–35.

Howell D. At sixes and sevens. Professional Engineering 2001; (May):27.

Hunter D. Six Sigma steps. Chemical Week 1999; 161(33):3.

Hunter D, Schmitt B. Six Sigma: Benefits and approaches. CW Conference Proceedings, Chemical Week 1999;1661(37):35–36.

Hutchins G. The branding of Six Sigma. Quality Progress 2000; 33(9):120–121.

Ingle S, Roe W. Six Sigma Black Belt implementation. The TQM Magazine 2001; 13(4):273–280.

Johnson A. Six Sigma in R&D. Research-Technology Management 2002; 45(2):12–16.

Johnson A, Swisher B. Now Six Sigma improves R&D. Research-Technology Management 2003; 46(2):12–15.

Johnstone P, Dernbach A. Six Sigma quality and delivery of radiation therapy. Cancer Journal 2002; 8(6):44.

Johnstone P, Hendrickson J, Dernbach A, Secord A, Parker J, Favata M, Puckett M. Ancillary services in healthcare industry: Is Six Sigma reasonable? Quality Management in Health Care 2003; 12(1):53.

Kandebo S. Lean, Six Sigma yield dividends for C-130J. Aviation Week and Space Technology 1999;151(2):59–61.



Kane L. The quest for Six Sigma. Hydrocarbon Processing 1998; 77(2):15.

Kazmer D, Hatch D, Zhu L. Investigation of variation and uncertainty in Six Sigma. Proceedings of the ASMEDesign Engineering Technical Conference, 2002, vol. 3. ASME International: New York, 2002; 21–29.

Kazmierczak S. Laboratory quality control: Using patient data to assess analytical performance. ClinicalChemistry and Laboratory Medicine 2003; 41(5):617–627.

Kendall J, Fulenwider D. Six Sigma, e-commerce pose new challenges. Quality Progress 2000; 33(7):31–37.

Kenett R, Coleman S, Stewardson D. Statistical efficiency: The practical perspective. Quality and ReliabilityEngineering International 2003; 19(4):265–272.

Knowles G, Vickers G, Anthony J. Implementing evaluation of the measurement process in an automotivemanufacturer: A case study. Quality and Reliability Engineering International 2003; 19(5):397–410.

Koch P. Probabilistic design: Optimizing for Six Sigma quality. Proceeds from 43rd AIAA/ASME/ASCE/AHSStructures, Structural Dynamics, and Materials Conference, 4th AIAA Non-Deterministic ApproachesForum, Denver, CO, 2002. American Institute of Aeronautics and Astronautics: Reston, VA, 2002; paperAIAA-2002-1471.

Koonce D, et al. A hierarchical cost estimation tool. Computers in Industry 2003; 50(3):293–302.

Krouwer J. Using a learning curve approach to reduce laboratory errors. Accreditation and Quality Assurance2002; 7(11):461–467.

Kunes R. Six Sigma article is misleading. Quality Progress 2002; 35(3):8.

Landin A, Nilsson C. Do quality systems really make a difference? Building Research and Information 2001;29(1):12–20.

Lee K. Critical success factors of Six Sigma implementation and the impact on operations performance. PhDDissertation, Cleveland State University, Cleveland, OH, 2002.

Leffew K, Yerrapragada S, Deshpande P. 6 Sigma and solid-state polymerization. Chemical EngineeringCommunications 2001; 188:109–114.

Linderman K, Schroeder R, Zaheer S, Choo A. Six Sigma: A goal-theoretic perspective. Journal of OperationsManagement 2003; 21(2):193–203.

Lucas J. The essential Six Sigma—How successful Six Sigma implementation can improve the bottom line.Quality Progress 2002a; 1(35):27–31.

Lucas J. Response to Six Sigma article is misleading. Quality Progress 2002b; 35(3):8–9.

Mader D. Design for Six Sigma. Quality Progress 2002; 7(35):82.

Maguire M. Six Sigma saga. Quality Progress 1999a; 32(10):6.

Magure M. Cowboy quality: Mike Harry’s riding tall in the saddle as Six Sigma makes its mark. QualityProgress 1999b; 32(10):27–34.

Mandal P, Howell A, Sohal A. A systemic approach to quality improvements: The interactions between thetechnical, human and quality systems. Total Quality Management 1998; 9(1):79–100.

Mason R, Young J. Interpretive features of a T(2) chart in multivariate SPC. Quality Progress 2000;33(4):84–89.

McCarthy B, Stauffer R. Enhancing Six Sigma through simulation with IGRAFX process for Six Sigma.Proceedings of the 2001 Winter Simulation Conference. IEEE Press: Piscataway, NJ, 2001; 1241–1247.

McFadden F. Six Sigma quality programs. Quality Progress 1993; 26(6):37–42.

Montgomery D. The present state of industrial statistics. Quality and Reliability Engineering International2000; 16(4):253–254.

Montgomery D. Beyond Six Sigma. Quality and Reliability Engineering International 2001; 17(4):iii–iv.



Montgomery D. Changing roles for the industrial statistician. Quality and Reliability Engineering International2002; 18(5):iii.

Montgomery D, Lawson C, Molnau W, Elias R. Six Sigma Black Belts: What do they need to know? Journalof Quality Technology 2001; 33(4):407–409.

Motyka M. Six Sigma, QS-9000 article has one minor flaw. Quality Progress 2000; 33(8):8.

Mukesh D. Putting Six Sigma processes to work. Chemical Engineering 2003; 110(12):62.

Munro R. Linking Six Sigma with QS-9000. Quality Progress 2000; 33(5):47–53.

Murugappan M, Keeni G. Blending CMM and Six Sigma to meet business goals. IEEE Software 2003; 2:42–48.

Nave D. How to compare Six Sigma, lean and the theory of constraints—a framework for choosing what’s bestfor your organization. Quality Progress 2002; 35(3):73–78.

Neuscheler F, Norris R. Capturing financial benefits from Six Sigma—five lessons learned will resonate withtop management. Quality Progress 2001; 34(5):39–44.

Nevalainen D, et al. Evaluating laboratory performance on quality indicators with the Six Sigma scale. Archivesof Pathology and Laboratory Medicine 2000a; 124(4):516–519.

Nevalainen D, Berte L. Replies to evaluating laboratory performance with the Six Sigma scale. Archives ofPathology and Laboratory Medicine 2000b; 124(12):1748.

Nielsen K, Orshal J. Companies—Dow accelerates Six Sigma effort; Reports on ‘social responsibility’.Chemical Week 1999; 161(37):9.

Noble T. Six Sigma boosts the bottom line. Chemical Engineering Progress 2001; 97(4):9–11.

Oakland J. Total Quality Management. Butterworth-Heinemann: London, 1989.

Olexa R. Driving quality with Six Sigma. Manufacturing Engineering 2003; 130(2):61.

Pande P, Neuman R, Cavanaugh R. The Six Sigma Way: How GE, Motorola, and Other Top Companies areHoning Their Performance. McGraw-Hill: New York, 2000.

Pearson T. Measure for Six Sigma success. Quality Progress 2001; 34(2):35–40.

Plotkin C et al. Panel: Advisory board—what are the successful companies doing? Proceedings of the AnnualReliability and Maintainability Symposium, 1999. IEEE Press: Piscataway, NJ, 1999; 219–223.

Pyzdek T. Why Six Sigma is not TQM. Quality Digest 2001a; (February):26.

Pyzdek T. Discussion, Six Sigma Black Belts: What do they need to know? Journal of Quality Technology2001b; 33(4):418–420.

Ramberg J. Six Sigma: Fad or fundamental? Quality Digest 2000; (May):28–32.

Rasis D, Gitlow H, Popovich E. Paper organizers international: A fictitious Six Sigma green belt case study, I.Quality Engineering 2002a; 15(1):127–146.

Rasis D, Gitlow H, Popovich E. Paper organizers international: A fictitious Six Sigma green belt case study, II.Quality Engineering 2002b; 15(2):259–274.

Rayner B. Market-driven quality: IBM’s Six Sigma crusade. Electronic Business 1990; 1(10):68–74.

Ribardo C, Allen T. An alternative desirability function for achieving Six Sigma quality. Quality and ReliabilityEngineering International 2003; 19(3):227–240.

Riley J, Justison G, Povrzenic D, Zabetakis P. Designing an integrated extracorporeal therapy service qualitysystem. Therapeutic Apheresis 2002; 6(4):282–287.

Rowlands H, Antony F. Application of design of experiments to a spot welding process. Assembly Automation2003; 23(3):273–279.

Sanders D, Hild C. A discussion of strategies for Six Sigma implementation. Quality Engineering 2000a;12(3):303–309.



Sanders D, Hild C. Six Sigma on business processes: Common organizational Issues. Quality Engineering2000b; 12(4):603–610.

Sanders D, Hild C. Common myths about Six Sigma. Quality Engineering 2001; 13(2):269–276.

Sarewitz S. Evaluating laboratory performance with the Six Sigma scale. Archives of Pathology and LaboratoryMedicine 2000; 124(12):1748.

Scalise D. Six Sigma: The west for quality. Hospitals and Health Networks 2001; 75(12):41.

Scalise D. Six Sigma in action—case studies in quality put theory into practice. Hospitals and Health Networks2003; 77(5):57.

Schmitt B. Moving ahead with Six Sigma. Proceedings from CW Conference, Chemical Week 2000;162(17):64–68.

Schmitt B. Expanding Six Sigma. Chemical Week 2001; 163(8):21–23.

Schmitt B. A slow spread for Six Sigma. Chemical Week 2002; 164(6):34–36.

Sigal R, Dessales-Martin D, Ruelle C, Kouchit N, Guillemot M, Klipfel B. Implementation of a PACS usingSix Sigma methodology. Radiology (Suppl. S) 2001; 221:527.

Smith B. Lean and Six Sigma—a one-two punch. Quality Progress 2003; 36(4):37–41.

Snee R. Why should statisticians pay attention to Six Sigma? Quality Progress 1999; 32(9):100–103.

Snee R. Impact of Six Sigma on quality engineering. Quality Engineering 2000a; 12(3):ix–xiv.

Snee R. Six Sigma improves both statistical training and processes. Quality Progress 2000b; 33(10):68–72.

Snee R. Dealing with the Achilles’ heel of Six Sigma initiatives—project selection is key to success. QualityProgress 2001a; 34(3):66.

Snee R. Discussion of Six Sigma Black Belts: What do they need to know? Journal of Quality Technology2001b; 33(4):414–417.

Snee R. The Six Sigma sweep. Quality Progress 2003; 36(9):76–78.

Sousa R, Voss C. Quality management re-visited: A reflective review and agenda for future research. Journal ofOperations Management 2002; 20:91–109.

Stamatis D. Who needs Six Sigma, anyway? Quality Digest 2000; (May):33–38.

Stein P. Measurements for business. Quality Progress 2001; 34(2):29.

Studt T. Implementing Six Sigma in R&D. R&D Magazine 2002; 44(8):21–23.

Tadikamalla P. The confusion over Six Sigma quality. Quality Progress 1994; 27(11):83–85.

Tang L, Than S, Ang B. A graphical approach to obtaining confidence limits of Cpk . Quality and ReliabilityEngineering International 1997; 13(6):337–346.

Treichler D, Carmichael R, Kusmanoff A, Lewis J, Berthiez G. Design for Six Sigma: 15 lessons learned—leading corporations find out how to avoid pitfalls. Quality Progress 2002; 35(1):33–42.

Trivedi B. Applying Six Sigma. Chemical Engineering Progress 2002; 98(7):76–81.

Tylutki T, Fox D. Mooooving toward Six Sigma. Quality Progress 2002; 35(2):34–41.

Vandenbrande W. How to use FMEA to reduce the size of your quality toolbox. Quality Progress 1998;31(11):97–100.

Vaugham T. Defect rate estimation for ‘Six Sigma’ processes. Production and Inventory Management Journal1998; 39(4):5–9.

Velocci A. Pursuit of Six Sigma emerges as industry trend. Aviation Week and Space Technology 1998a;149(20):52–53.

Velocci A. High hopes riding on Six Sigma at Raytheon. Aviation Week and Space Technology 1998b;149(20):59–60.



Velocci A. Six Sigma takes a back seat to ‘Lean Electronics’ at Rockwell. Aviation Week and Space Technology1998c; 149(20):60–62.

Velocci A. Raytheon Six Sigma meets initial target. Aviation Week and Space Technology 2000; 152(13):59.

Velocci A. Full potential of Six Sigma eludes most companies. Aviation Week and Space Technology 2002;157(14):56–60.

Voelkel J. Something’s missing—an education in statistical methods will make employees more valuable toSix Sigma corporations. Quality Progress 2002; 35(5):98–101.

Walsh K., Fuller J., Wood A., Moore S, Seewald N, Schmitt B. Six Sigma—marshaling an attack on costs.Chemical Week 2000; 162(9):25–27.

Watson G. Toward a central tendency of Six Sigma. Quality Progress 2000; (July):16.

Watson G. Selling Six Sigma to upper management. Six Sigma Forum Magazine 2002a; 1(4):26–37.

Watson G. Breakthrough in Delivering Software Quality: Capability Maturity Model and Six Sigma (LectureNotes in Computer Science, vol. 2349). Springer: Berlin, 2002b; 36–41.

Waurzyniak P. Statistics improve quality. Manufacturing Engineering 2002; 128(2):39–44.

Weinstein L, Petrick J, Saunders P. What higher education should be teaching about quality—but is not. QualityProgress 1998; (April):91–96.

Westgard J. Evaluation of cardiac troponin assay systems and validation of QC in accordance with Six-Sigmaprinciples and ISO guidelines. Clinical Chemistry (Suppl. S) 2002; 48(6):C61 Part 2.

Wheeler J. Getting started: Six-Sigma control of chemical operations. Chemical Engineering Progress 2002a;98(6):76–81.

Wheeler J. Getting started with Six-Sigma. Chemical Engineering Progress 2002b; 98(9):8.

Wiklund H, Wiklund P. Widening the Six Sigma concept: An approach to improve organizational learning. TotalQuality Management 2002; 13(2):233–239.

Wood A. Management—making Six Sigma benefits stick. Chemical Week 2001; 163(19):40.

Wyper B, Harrison A. Deployment of Six Sigma methodology in human resource function: A case study. TotalQuality Management 2000; 11(4–6):720–727.

Yeung A, Chan L, Ledd T. An empirical taxonomy for quality management systems: A study of the Hong Kongelectronics industry. Journal of Operations Management 2003; 21(1):45–62.

Yu B, Popplewell K. Meta-models in manufacturing: A review. International Journal of Production Research1994; 32:787–796.

Zain Z, Dale B, Kehoe D. Total quality management: An examination of the writings from a U.K. perspective.The TQM Magazine 2001; 13(2):129–137.

Zwass V. Electronic commerce: Structure and issues. International Journal of Electronic Commerce 1996;1(1):3–33.

Authors’ biographies

James E. Brady is the engineering manager for LaBarge Inc. in Joplin, MO. He received his PhD in IndustrialEngineering at the Ohio State University in Columbus, OH. Formerly, he was an engineering manager anda Six Sigma coach at Lucent Technologies, Bell Laboratories. He currently guest lectures on design ofexperiments and Six Sigma topics at Missouri Southern State University in Joplin.Theodore T. Allen is an associate professor of industrial and systems engineering at the Ohio State Universityin Columbus, OH. He received his PhD in Industrial and Operations Engineering at the University of Michiganin Ann Arbor. He is a senior member of the American Society of Quality and an associate editor of the Journalof Manufacturing Systems. He has published over 25 referred papers related to design of experiments andSix Sigma. Also, he is a partner in Sagata Ltd, which is a software and consulting company.


Documents

Six Sigma Literature: A Review and Agenda for Future Research