J. de Mast

A rational reconstruction of Six Sigma's Breakthrough Cookbook

Purpose – The purpose of this paper is to develop a consistent and crystallized exposition of Six‐Sigmas methodology for improvement projects, which could serve as a basis for subsequent scientific research of the method.Design/methodology/approach – The paper shows that reformulation of imprecise and unscientific formulations of knowledge is called rational reconstruction. Starting from accounts given in the Six‐Sigma literature, a descriptive reconstruction of the main elements of the Six‐Sigma method is made: its business context, strategy, tools and techniques, and concepts and classifications.Findings – The paper finds that, although, on the face of it, it may seem that accounts given in literature diverge, analysis shows that variations are superficial rather than essential. The analyses result in precisely formulated accounts of Six‐Sigmas method (DMAIC phases, steps, and tools), its business context, and its terminology. Essential anomalies are discussed. Six‐Sigmas claims of being data‐driven ...

A methodological comparison of three strategies for quality improvement

Quality improvement is understood by Juran to be the systematic pursuit of improvement opportunities in production processes. Several methodologies are proposed in literature for quality improvement projects. Three of these methodologies – Taguchis methods, the Shainin system and the Six Sigma programme – are compared. The comparison is facilitated by a methodological framework for quality improvement. The methodological weaknesses and strong points of each strategy are highlighted. The analysis shows that the Shainin system focuses mainly on the identification of the root cause of problems. Both Taguchis methods and the Six Sigma programme exploit statistical modelling techniques. The Six Sigma programme is the most complete strategy of the three.

Improving processes in financial service organizations: where to begin?

Purpose – The purpose of this paper is to create actionable knowledge, thereby supporting and stimulating practitioners to improve processes in the financial services sector.Design/methodology/approach – This paper is based on a case base of improvement projects in financial service organizations. The data consist of 181 improvement projects of processes in 14 financial service organizations executed between 2004 and 2010. Following the case‐based reasoning approach, based on retrospective analysis of the documentation of these improvement projects, this paper aims to structure this knowledge in a way that supports practitioners in defining improvement projects in their own organizations.Findings – Identification of eight generic project definition templates, along with their critical to quality flowdowns and operational definitions. An overview of the distribution of improvement projects of each generic template over different departments and the average benefit per project for each department. The gener...

Some common errors of experimental design, interpretation and inference in agreement studies.

We signal and discuss common methodological errors in agreement studies and the use of kappa indices, as found in publications in the medical and behavioural sciences. Our analysis is based on a proposed statistical model that is in line with the typical models employed in metrology and measurement theory. A first cluster of errors is related to nonrandom sampling, which results in a potentially substantial bias in the estimated agreement. Second, when class prevalences are strongly nonuniform, the use of the kappa index becomes precarious, as its large partial derivatives result in typically large standard errors of the estimates. In addition, the index reflects rather one-sidedly in such cases the consistency of the most prevalent class, or the class prevalences themselves. A final cluster of errors concerns interpretation pitfalls, which may lead to incorrect conclusions based on agreement studies. These interpretation issues are clarified on the basis of the proposed statistical modelling. The signalled errors are illustrated from actual studies published in prestigious journals. The analysis results in a number of guidelines and recommendations for agreement studies, including the recommendation to use alternatives to the kappa index in certain situations.

Inter-industry generic Lean Six Sigma project definitions

Purpose The objective of this research is to provide practitioners with inter-industry applicable rules and guidelines for the Lean Six Sigma (LSS) project definition phase. This research resulted in 13 inter-industry generic project definitions that are divided by four performance dimensions: quality, dependability, speed and cost efficiency. Design/methodology/approach A total of 312 previously executed LSS improvement projects in a broad variety of industries at black belt or master black belt level are analyzed. All these projects have followed the LSS methodology and are characterized by the use of critical to quality (CTQ) measurements and the structured improvement method of define, measure, analyse, improve and control for operations improvement. Findings This research resulted in 13 inter-industry generic project definitions that are divided by four performance dimensions: quality, dependability, speed and cost efficiency. Three factors that have stood out in this research are; the difficulty to capture the performance dimension flexibility in LSS project definitions, the strong focus on internal organizational benefits in defining CTQs for LSS project definitions and the unclear alignment of LSS project definitions to existing strategic objectives of the company. Originality/value This research established useable generic LSS project definitions including generic CTQ’s measures, applicable to multiple industries. These generic LSS project definitions provide useful guidance in the initial LSS project phase, helping to decompose strategic focal points into clear and measurable project objectives.

Discussion: [of 'An overview of the Shainin System™ for quality improvement']

ABSTRACT The article by Steiner, MacKay and Ramberg offers a sound and an accurate description of the Shainin System for quality improvement. Therefore, our comments will not address their representation of the Shainin System, but concern the Shainin System itself.The article by Steiner, MacKay and Ramberg [An overview of the Shainin System for quality improvement] offers a sound and an accurate description of the Shainin System for quality improvement. Therefore, our comments will not address their representation of the Shainin System, but concern the Shainin System itself.

Quantifying the Random Component of Measurement Error of Nominal Measurements Without a Gold Standard

It is well known that measurement error of numerical measurements can be divided into a systematic and a random component and that only the latter component is estimable if there is no gold standard or reference standard available. In this paper, we consider measurement error of nominal measurements. We motivate that, on a nominal measurement scale too, measurement error has a systematic and a random component and only the random component is estimable without gold standard. Especially in literature about binary measurement error, it is common to quantify measurement error by ‘false classification probabilities’: the probabilities that measurement outcomes are unequal to the correct outcomes. These probabilities can be split up in a systematic and a random component. We quantify the random component by ‘inconsistent classification probabilities’ (ICPs): the probabilities that a measurement outcome is unequal to the modal (instead of correct) outcome. Systematic measurement error then is the event that this modal outcome is unequal to the correct outcome. We introduce an estimator for the ICPs and evaluate its properties in a simulation study. We end with a case study that demonstrates not only the determination and use of the ICPs but also demonstrates how the proposed modeling can be used for formal hypothesis testing. Things to test include differences between appraisers and random classification by a single appraiser. Copyright

Discussion of “Principles of Exploratory Data Analysis in Problem Solving: What Can We Learn From a Well-Known Case?”—Rejoinder

Institute for Business and Industrial Statistics (IBIS UvA), University of Amsterdam, Amsterdam, The Netherlands We would like to thank the editor for organizing this discussion of our paper, and we appreciate the points brought forward by the discussants. Professor Vining’s example of the chemical company illustrates the problem that we aimed to address with our paper. The professionals responsible for designing this company’s problem solving methodology were tool experts—they were, probably competent, experts on (mostly confirmatory) tools for data analysis, but they were, apparently, lacking in understanding of the process of inquiry. For us, being an expert on the tools of data analysis is not enough for being called a statistician, as the latter for us also involves understanding of the process of inquiry, and the roles of exploratory investigations and confirmatory investigations in it. We follow professor Vining here in acknowledging professor George Box as our profession’s inspiring tutor on this thought. Dr. Simpson makes a point in place, warning the reader that in the actual process of problem solving, EDA and confirmatory data analysis (CDA) cannot be clearly distinguished—activities, techniques and pursuits related to these two are often thoroughly intertwined. Dr. Simpson’s assertion that problem solving cannot be clearly distinguished into studies requiring purely EDA and studies requiring purely CDA, is illustrated by the case of John Snow. The reader may have assumed Snow’s Grand Experiment (mainly CDA in intention and set-up) to be after the Soho episode which identified the pump, and more generally, the water system, as instrumental in the epidemic; but in fact, these two investigations were concurrent, with John Snow frequently traveling between the south and north shore of the River Thames. The distinction between an EDA phase which identifies the hypotheses, and a CDA phase in which they are tested, is often a simplifying reconstruction afterwards. For us, the reason for contrasting the concepts of EDA and CDA, is that both represent distinctive functions in the problem solving process (sometimes characterized as discovery and justification). Each function Address correspondence to Jeroen de Mast, Institute for Business and Industrial Statistics (IBIS UvA), University of Amsterdam, Plantage Muidergracht 12, Amsterdam, 1018 TV, The Netherlands. E-mail: j.demast@uva.nl Quality Engineering, 21:382–383, 2009 Copyright # Taylor & Francis Group, LLC ISSN: 0898-2112 print=1532-4222 online DOI: 10.1080/08982110903291757