A conversation with Ron Snee

PurposeThe purpose of this paper is to demonstrate the vital linkage between six sigma and statistical thinking. The paper also explains the key characteristics required for statistical thinking and some of the common barriers in the implementation of the key principles of statistical thinking.Design/methodology/approachThe objectives of the paper have been achieved in several ways. The paper provides the key principles of statistical thinking and then discusses the possible reasons for lack of statistical thinking in modern organizations. The paper then illustrates the linkage between the statistical principles and six sigma. The tools and techniques of six sigma used within statistical thinking are also highlighted in the paper.FindingsThe key findings of this work include the relationship between the two key powerful methodologies: six sigma and statistical thinking, reasons for lack of applications of statistical thinking in organizations, the future role of managers and engineers in companies with regard to statistical thinking era and the commonalities in the application of tools and techniques between these two methodologies.Research limitations/implicationsThe paper needs more justification through surveys and case examples and this will be the future step of this study. In fact, one of the co‐authors is currently conducting a survey in the UK organizations to investigate the relationship between statistical thinking and six sigma.Practical implicationsThe paper is very practical in nature and it does yield a great value to those people who are currently embarking on six sigma program, especially at senior manager and executive levels.Originality/valueVery little is published in the field of statistical thinking in the UK academic world. In fact, there is a cognitive gap in this field and this paper certainly forms a good platform for further research that will enable to bridge the gap.

Medical Director, CPGs Spell QI Success

Paper aims Identify the use of statistical thinking and techniques and their main difficulties and benefits in the Brazilian State of Sao Paulo industries. Originality There are few empirical studies on the application of the statistical thinking and techniques which study their difficulties of implementation and their benefits in manufacturing companies. Research method A web survey of a sample of 243 manufacturing companies. Main findings The companies, in general, use some statistical principles and basic techniques for process control and improvement, however, companies that use principles and techniques more consistently have greater operational and team benefits. The main difficulties are associated to lack of culture and knowledge. Implications for theory and practice The statistical application enables effective processes improvements and it is associated with motivation for further improvements, consolidation of improvement programs and culture of quality. This finding suggests managerial implications such as to plan actions to deploy and disseminate the culture of statistical thinking in an evolutionary way, training and support for use and to overcome barriers.

Click to increase image sizeClick to decrease image size ABOUT THE AUTHORRonald D. Snee is Founder and President of Snee Associates, LLC. Prior to pursuing a consulting career he worked at DuPont for 24 years. Ron also serves as Adjunct Professor in the Pharmaceutical Graduate programs at Temple and Rutgers Universities. He received his BA from Washington and Jefferson College and MS and PhD degrees from Rutgers University. He is an academician in the International Academy for Quality and Fellow of the American Society of Quality, American Statistical Association, and American Association for the Advancement of Science. Ron has been awarded ASQ's Shewhart and Grant Medals, and ASA's Deming Lecture and Dixon Consulting Awards. He has co-authored 5 books and written more than 255 publications on performance improvement, quality, management and statistical studies.

Roman W. Wong (rvvong@washjeff.edu) received a Ph.D. in mathematics from Rutgers University. He taught at Syracuse University for one year and is now a professor at Washington and Jefferson College. He is currently interested in stochastic process and linear transformations. His hobbies include reading murder mysteries, playing USTA team tennis, and motivating his two teenage sons to be more interested in mathematics.

Kristin Shrader-Frechette: Confronting environmental injustice.

During a sabbatical in the fall semester of 1991, Hogg took a ‘quality journey’, visiting 20 companies and 5 universities and attending several meetings related to quality improvement. He did this to determine what produces quality products and services and the role statisticians can play in the quality movement. Moreover, he found out how programmes in statistics can be improved to benefit students, particularly those who wish to obtain positions in business, industry and government. The journey confirmed practices that are generally accepted in quality improvement: involving top management, paying attention to customers' wants, training and empowering employees, and forming partnerships with suppliers. Statisticians must continue to stress the importance of making data-based decisions and teaching ‘statistical thinking’ and other important ideas of quality improvement.

The application of statistical tools has a long history of successful use at the operational level of both research and manufacturing organisations. The development of the Total Quality Management (TQM) concept brought with it a renewed emphasis on statistical thinking and its role in the overall management of organisations. Statistical thinking encompasses three important components: knowledge of systems; understanding variation; and use of data to guide decisions. Managers must strive to understand, acquire, and internalise an organisational system based on statistical thinking in order to transform their organisations. This paper provides a conceptual framework to understand and reduce variation in product quality through the use of system thinking and statistical methods.

Measuring performance and defining good quality is a relatively recent phenomenon in healthcare. Starting in the 1980s, when the publi- cation of report cards and information about outcomes of practice, healthcare has steadily improved its skill with using data to understand perfor- mance with the intention of making improvements in clinical care as well as in financial performance. Throughout the last 30 years, there have been sever- al methodologies that have been introduced to assist organizations in improv- ing quality, including Total Quality Management (TQM), Clinical Quality Improvement (CQI), and Value Stream Management (VSM). While the rest of this article will concentrate on Lean Six Sigma and how it was used to execute a hospital design project, it is vital to understand that these methodologies and their associated tools are all part of a larger toolbox, and can be integrated with other tools or methodologies based on the needs of a particular improve- ment project.In order for improvement to take root and flourish in an organization, an intense commitment on the part of senior management is required to support improve- ment activities through training, monitoring, and continued improvement activities after initial implementation. In addition to the need for management support for improvement projects, all employees must embrace some basic prin- ciples of quality and performance improvement. Good quality and productive work can't be accomplished without well-defined processes and metrics that are monitored over time. It is also impossible to have good quality without under- standing the needs of customers, both internal and external. Finally, it is vital for everyone to remember that the main source of quality defects is within the pro- cess itself or the system that is being used, not with the people who are involved in the process. It is far too easy to blame particular people or departments for poor quality, when it is often the system that is fundamentally flawed.Increasing performance can only be achieved in two ways: increase capacity or reduce variation. If the concentration is (and should be) on making the system better, the concept of variation becomes paramount. Understanding variability is the key to improving a process or a system so that goods or services produced meet the needs of customers. Variation can be described as deviation from a stan- dard process. Variation is ubiquitous, additive, and in most systems that involve humans, can't be completely eliminated. Traditional approaches seek to mitigate the effects of variation in process, sometimes putting a quick-fix into place that ends up not only not fixing the problem at hand, but often also making the entire system work less effectively and efficiently.But in discussing the best way to approach quality improvement, there are some other basic principles to consider. Poor quality leads to incremental cost to the business, for example, in terms of rework, dealing with customer complaints, loss of market share, among others. In some processes, up to half of the work done is non-value-added, that is, work that does not contribute to either the bot- tom line or to satisfying customers. An organization can't improve everything at once, so it should concentrate on the processes or systems that are most vital to core business objectives. Decisions should be made based on data that have been analyzed using sound scientific and statistical thinking. Improvement needs to be embraced at every level of the organization, or it will not be sustained.What Is Lean Six Sigma?In very broad terms, Six Sigma is a statistically based approach to process improvement. Lean is a methodology that focuses on eliminating waste-doing more with less-and has its origins in the Toyota Production System that was introduced in Japan. Lean Six Sigma combines the toolboxes of the separate methodologies of Lean and Six Sigma to provide a hybrid approach to process improvement. …

To those acquainted with the backgrounds of the leading figures in the creation of what has become known as Total Quality Management and have read any of their books on the subject, or for that matter, have studied the works of any of their leading followers, the title of this paper must seem very strange. Thus, Deming was a noted statistician in his own right, and in his renowned 1986 book “Out of the Crisis”, on p. 466, states that a plan for the organisation of quality and productivity must have: “a leader in statistical methodology with authority from top management to be a participant that in his judgement is worth his pursuit”. It would not be difficult to cite a very long list of TQM authorities making remarks that clearly affirm the central role of statistics in quality and productivity improvement, (see for example Ghoboadian & Speller (1994)). One could even note that at the present time, two the three U.K. professors of TQM are statisticians. Yet in spite of this, in a recent conversation between the author and a manager of a major U.K. company which had ISO9000 accreditation, the manager asked with seemingly some surprise — “What has a statistician got to do with TQM?”

Statistics is subject foundation requisite for students who major in finance and economics. It is also a science about data. With the development of China’s economy, and the role of statistics is gradually recognized by people, which has formed a huge challenge for higher vocational colleges in China. Under the background of big data, enterprises and institutions pay more attention to the statistical data analysis ability and mining ability of candidates when recruiting talents. But in reality, it is often difficult to recruit statistics professionals, because there are very few higher vocational colleges offering pure statistics[1]. From the perspective of discipline system, the relationship between statistics and various disciplines is cross, not a vertical discipline, but a horizontal discipline. Combining computer knowledge to form such as national economic accounting etc[2]. For current statistics teaching work of economics and management majors in higher vocational colleges, it is especially necessary to cultivate students' statistical thinking. However, at present teaching work, there is obvious lag in the teaching of statistics. Based on teaching contents, teaching methods and other aspects, this paper puts forward some suggestions on statistics teaching in higher vocational education to promote the improvement of teaching quality.

Much has been written recently concerning the future of statistics in business and industry (e.g., Technometrics 2008). In this expository article, the authors extend this discussion to focus specifically on the future of statistics and statisticians within the context of quality improvement. We argue that though the problems our profession faces are well known and have been repeatedly identified in the literature, the underlying root causes of these problems have remained lurking under the surface of the discussion. In particular, though positive change is certainly taking place, we do not believe that the statistics community has fully accepted and come to grips with the radical changes in our environment over the past 20–40 years. In short, we believe that our environment has changed radically, whereas our own changes have been incremental in nature. One such change that seems particularly difficult for us to swallow is that our society needs us to function, especially in the quality arena, more as an engineering discipline, rather than primarily as a pure science. To the extent that we can make this transition, we believe that we will maintain and grow our vitality as a discipline. To the extent that we refuse to, or are unable to, make this transition, our vitality, influence, and impact may erode, as other disciplines fill the void we leave. We choose to view this situation as half full and therefore offer specific suggestions as to how we feel the profession can make the needed changes in order to secure a brighter future in quality improvement.

Variables Associated With Nurse-Reported Quality Improvement Participation

Report of the Council for the Session 2003–2004

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