Application 1 – Analysis and Synthesis of Prior Research

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A modern framework for achieving enterprise excellence

Douglas C. Montgomery School of Computing, Informatics and Decision Systems Engineering,

Arizona State University, Tempe, Arizona, USA

Abstract

Purpose – This paper aims to present a quality and business improvement framework based on Six Sigma/design, measure, analyze, improve, and control (DMAIC); design for Six Sigma (DFSS); and Lean principles. An historical perspective on these methods is also provided.

Design/methodology/approach – Through a discussion of the fundamental components of Six Sigma, DFSS and Lean a deployment strategy is developed. This approach is consistent with Deming’s philosophy of quality improvement and is based on his system of profound knowledge.

Findings – A quality/process improvement effort that utilizes Six Sigma and DMAIC, DFSS, and Lean can form two of the three basic building blocks of a company-wide quality management system.

Research limitations/implications – Some implications for education of future business leaders in a variety of disciplines including engineering, statistics and business administration are discussed. More widespread education about DMAIC and the associated problem-solving tools is needed.

Originality/value – This paper provides practical guidance for implementing quality and productivity improvement across a wide range of industrial and business endeavors.

Keywords Six sigma, Process management, Quality management, Lean production

Paper type Research paper

Introduction Quality always has been an integral part of virtually all products and services. However, our awareness of its importance from a business standpoint and the introduction of formal methods for quality control and improvement have been an evolutionary development. Our objective in this paper is to briefly summarize some important milestones in this historical development, to propose a framework for implementing quality improvement in the modern business and industrial world, and to discuss the potential impact that this has on managers, practicing engineers, scientists and statisticians, and academia particularly from the perspective of how these individuals are trained and educated. I argue that the framework for improvement that I propose is consistent with and is actually a system for implementing Deming’s management philosophy.

The next section of this paper provides the historical background. Then some aspects of quality philosophy and its role in the modern business improvement framework are described, followed by a discussion of the implementation of an improvement strategy. The paper concludes with a discussion of the impact that adopting this approach for business improvement could have on education and training of a wide spectrum of quality and management professionals.

Some historical background on quality improvement At the beginning of the twentieth century, science and most of business and industry was based on determinism, that is, a widespread belief that physical laws and

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International Journal of Lean Six Sigma Vol. 1 No. 1, 2010 pp. 56-65 q Emerald Group Publishing Limited 2040-4166 DOI 10.1108/20401461011033167

(in general) economic models of real world phenomenon were actually the true reality, and that all we needed to be able to perfectly predict future events was enough data so that the physical constants in these models could be estimated precisely enough. With enough data and the appropriate equations, we could foretell future events with near-perfect certainty. Some big successes in the nineteenth century drove this feeling. For example, in the 1840s Newton’s methods were used to predict the existence of Neptune. This did much to enhance the belief in determinism. Science, business, and industry dealt with things that we held to be constant. We just needed enough data to be able to describe phenomena exactly.

Variability and the statistical view of the world were not widespread and indeed even unappreciated, except in some aspects of genetics and agriculture. Ernst Rutherford (1871-1937) the winner of the 1908 Nobel Prize in Physics said “If your experiment needs statistics, you ought to have done a better experiment.” Albert Einstein (1879-1955) the winner of the 1921 Nobel Prize in Physics famously said that “God doesn’t play dice” in his discussion of quantum theory. However, by the early years of the century science was making tremendous advances in the ability to make measurements of increasing precision. It was quickly discovered that the more “precise” the measurements the more “error” was observed. Albert A. Michelson (1852-1931), winner of the 1907 Nobel Prize in Physics, and the inventor of the interferometer that was used in experiments (with E.W. Morley) to measure the velocity of light discovered that his measurements varied widely. Control charts applied to his data exhibit a lack of stability that today we would call an out-of-control process.

Variability in the response was being recognized as an “evil” that must somehow be overcome (or at least reckoned with!). In the academic world, courses on statistical mechanics began to appear in physics, engineering curricula. The development of interchangeable parts as a manufacturing technique led to the origins of mass production in the late 1899s. Frederick W. Taylor introduced some of the principles of scientific management as these mass production industries began to develop. Taylor pioneered dividing work into tasks so that a product could be manufactured and assembled more easily. His work led to substantial improvements in productivity. Also, because of standardized production and assembly methods, the quality of manufactured goods was positively impacted as well. However, along with the standardization of work methods came the concept of work standards – a standard time to accomplish the work, or a specified number of units that must be produced per period. Frank Gilbreth and others extended this concept to the study of motion and work design. The outgrowth of this was the motion-time-measurement system used to set work standards. Much of this had a positive impact on productivity, but it often deemphasized the quality aspect of work. Furthermore, if carried to extremes, work standards can potentially halt innovation and continuous improvement, which are a critical aspect of all work activities.

Statistical methods and their application in quality improvement have had a long history. Refer to Montgomery (2009b) for a time line and an extended discussion. Here we give a few highlights. In 1924, Walter A. Shewhart of the Bell Telephone Laboratories developed the statistical control-chart concept, which is often considered the formal beginning of statistical quality control. The control chart is the fundamental tool of statistical process control (SPC). Toward the end of the 1920s, Harold F. Dodge and Harry G. Romig, both also of Bell Telephone Laboratories, developed statistically

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based acceptance sampling as an alternative to 100 percent inspection. By the middle of the 1930s, statistical quality-control methods were in wide use at Western Electric, the manufacturing arm of the Bell System. However, the value of statistical quality control was not widely recognized by industry.

World War II saw a greatly expanded use and acceptance of statistical quality-control concepts in manufacturing industries. Wartime experience made it apparent that statistical techniques were necessary to control and improve product quality. The American Society for Quality Control was formed in 1946. The 1950s and 1960s saw the emergence of reliability engineering, the introduction of several important textbooks on statistical quality control, and the viewpoint that quality is a way of managing a business organization.

In the 1950s, designed experiments for product and process improvement were first introduced in Europe and the USA. Designed experiments were developed and utilized by Fisher in the 1920s to improve the efficiency and effectiveness of agricultural field trials. While there were some non-agricultural applications of designed experiments in the 1930s and 1940s they were not widespread, and the first industrial, era was catalyzed by the development of response surface methodology (RSM) by Box and Wilson (1951). They recognized and exploited the fact that many industrial experiments are fundamentally different from their agricultural counterparts in two ways:

(1) the response variable can usually be observed (nearly) immediately; and

(2) the experimenter can quickly learn crucial information from a small group of runs that can be used to plan the next experiment.

Box (1999) calls these two features of industrial experiments immediacy and sequentiality. Over the next 30 years, RSM and other design techniques spread throughout the chemical and the process industries, mostly in research and development work. George Box was the intellectual leader of this movement. However, the application of statistical design at the plant or manufacturing process level was still not extremely widespread. Some of the reasons for this include inadequate training in basic statistical concepts and methods for engineers and other process specialists and the lack of computing resources and user-friendly statistical software to support the application of statistically designed experiments.

The initial applications of designed experiments were in the chemical and process industries. These methods were widely exploited in these industries mainly for process development. The spread of these methods outside the chemical industry was relatively slow until the late 1970s or early 1980s, when many Western companies discovered that their Japanese competitors had been systematically using designed experiments and other statistical methods since the 1960s for process troubleshooting, new process development, evaluation of new product designs, improvement of reliability and field performance of products, and many other aspects of product design, including selection of component and system tolerances. This discovery sparked further interest in statistically designed experiments and resulted in extensive efforts to introduce the methodology in engineering and development organizations in industry, as well as in academic engineering curricula. I believe that designed experiments are the most powerful of the tools of quality improvement because they are an active tool in which changes are deliberately and systematically introduced into a system to observe their

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effect, and not based on passive observation. Refer to Montgomery (2009a) for a discussion of recent developments in designed experiments.

Since 1980 there has been extensive growth in the use of statistical methods for quality improvement. This has been motivated in the USA and Europe, at least in part, by the widespread loss of business and markets suffered by many companies that began during the 1970s. Various management systems have also emerged as frameworks in which to implement quality improvement. We now briefly discuss some key aspects of quality management.

Evolution of quality management philosophy Statistical techniques along with other problem-solving tools are the technical basis for quality control and improvement. However, to be used most effectively, these techniques must be implemented within and be part of a management system that is focused on quality improvement. The management system of an organization must be organized to properly direct the overall quality improvement philosophy and ensure its deployment in all aspects of the business. Effective quality management involves successful execution of three activities: quality planning and design, quality assurance, and quality control and improvement. I think these are the essential components of any quality management system.

Quality planning and design is a strategic activity, and it is just as vital to an organization’s long-term business success as the product development plan, the financial plan, the marketing plan, and plans for the utilization of human resources. Without a strategic quality plan, many resources of time, money, and effort will be wasted by the organization dealing with faulty designs, manufacturing defects, field failures, and customer complaints. Quality planning and design involves identifying customers, both external and those that operate internal to the business, and identifying their needs (this is sometimes called listening to the voice of the customer). Then products or services that meet or exceed customer expectations must be designed and developed. The organization must then determine how these products and services will be realized. Planning for quality improvement on a specific, systematic basis and having a structured quality-oriented approach to design is a integral part of this process.

Quality assurance is the set of activities that ensures the quality levels of products and services are properly maintained and that supplier and customer quality issues are properly resolved. Documentation of the quality system is an important component. Quality system documentation involves four components: policy, procedures, work instructions and specifications, and records. Policy generally deals with what is to be done and why, while procedures focus on the methods and personnel that will implement policy. Work instructions and specifications are usually product-, department-, tool-, or machine-oriented. Records are a way of documenting the policies, procedures, and work instructions that have been followed. Records are also used to track specific units or batches of product, so that it can be determined exactly how they were produced. Records are often vital in providing data for dealing with customer complaints, corrective actions, and, if necessary, product recalls. Development, maintenance, and control of documentation are important quality assurance functions. One example of document control is ensuring that specifications and work instructions developed for operating personnel reflect the latest design and

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engineering changes. In short, a major component of quality assurance is to document what you say you are going to do and maintain records so that you can verify that you have done what you said you are going to do.

Quality control and improvement involve the set of activities used to ensure that the products and services meet requirements and are improved on a continuous basis. Since variability is often a major source of poor quality, statistical techniques, including SPC and designed experiments, are the major tools of quality control and improvement. Quality improvement is often done on a project-by-project basis and involves teams led by personnel with specialized knowledge of statistical methods and experience in applying them. Projects should be selected so that they have significant business impact and are linked with the overall business goals for quality identified during the planning process. Montgomery (2009b) and Snee and Rodebaugh (2002) discuss some important aspects of project selection.

The theory of quality management, control and improvement has been profoundly influenced primarily by the work of two people; W. Edwards Deming and Joseph M. Juran. Deming (1900-1993) was educated in engineering and physics at the University of Wyoming and Yale University. He worked for Western Electric and was influenced greatly by Walter A. Shewhart, the developer of the control chart. After leaving Western Electric, Deming held government jobs with the US Department of Agriculture and the Bureau of the Census. During World War II, Deming worked for the War Department and the Census Bureau. Following the war, he became a consultant to Japanese industries and convinced their top management of the power of statistical methods and the importance of quality as a competitive weapon. This commitment to and use of statistical methods was a key element in the expansion of Japan’s industry and economy during the 1950-1980 period. The Japanese Union of Scientists and Engineers created the Deming Prize for quality improvement in his honor. Until his death in 1994, Deming was an active consultant and speaker; he was an inspirational force for quality improvement in this country and around the world. He firmly believed that the responsibility for quality rests with management; that is, most of the opportunities for quality improvement require management action, and very few opportunities lie at the workforce or operator level. Deming was a harsh critic of many western management practices. He often spoke about the seven deadly diseases of management and what he termed “obstacles to success” including the “our problems are different” excuse and the “anyone that comes to help us must know all about our business” thinking.

The Deming philosophy is an important framework for implementing quality and productivity improvement. This philosophy is summarized in his 14 points for management (Deming, 1986; Montgomery, 2009b). Deming’s 14 points place a strong emphasis on change. Also, the role of management in guiding this change process is of dominating importance. Deming believed that the widespread deployment of statistical methods to reduce variability was a fundamental component of quality improvement. Deming often said that if he could only say one thing to management, he would tell them to focus on reducing variability.

Juran (1904-1908) is one of the founding fathers of the modern quality-control and improvement field. He worked for Walter A. Shewhart at AT&T Bell Laboratories and has been at the leading edge of quality improvement ever since. Juran was an assistant administrator of the Lend-Lease Administration during World War II and played

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an important role in simplifying the administrative processes of that agency. He was invited to speak to Japanese industry leaders as they began their industrial transformation in the early 1950s. He is the co-author (with Frank M. Gryna) of the Quality Control Handbook, a standard reference for quality methods and improvement since its initial publication in 1957.

The Juran quality management philosophy is more strategic that the Deming philosophy. It focuses on three components: planning, control, and improvement. These are known as the Juran Trilogy. Notice that the Juran trilogy encompasses many of the components of an ideal quality management system. As I have observed previously, planning involves identifying external customers and determining their needs. Then products or services that respond to these customer needs are designed and/or developed, and the processes for producing these products or services are then developed. The planning process should also involve planning for quality improvement on a regular (typically annual) basis. Control is employed by the operating components of the business to ensure that the product or service meets the requirements. SPC is one of the primary tools of control. Improvement aims to achieve performance and quality levels that are higher than current levels. Juran emphasizes that improvement must be on a project-by-project basis. These projects are typically identified at the planning stage of the trilogy. Improvement can either be continuous (or incremental) or by breakthrough. Typically, a breakthrough improvement is the result of studying the process and identifying a set of changes that result in a large, relatively rapid improvement in performance. Designed experiments are an important tool that can be used to achieve breakthrough.

A modern business improvement strategy Since the 1980s various management systems have emerged as frameworks in which to implement quality improvement. Key among them is total quality management (TQM), which flourished briefly in the mid to late 1980s, and Six Sigma, which began in the late 1980s. TQM has largely disappeared as a framework for quality improvement, while Six Sigma has flourished. This is largely due to the emphasis Six Sigma places on delivering bottom-line business results in addition to quality improvements. Motorola developed the Six Sigma program in the late 1980s as a response to the demand for their products, which contained many components, and achieving very low levels of defectivity is critical to product function. See Montgomery and Woodall (2008), Folaron (2003), and Hahn et al. (2000) for the evolution of Six Sigma.

The focus of Six Sigma is reducing variability in key product quality characteristics to the level at which failure or defects are extremely unlikely. The model of a Six Sigma process that is typically adopted assumes that if the process is centered at the target and the nearest specification limit is six standard deviations from the mean, the process will operate at the 3.4 million parts per million (ppm) defective level. Montgomery and Woodall (2008) point out that many advocates of the Deming philosophy reject Six Sigma because they feel that the 3.4 ppm metric is a “goal” or “target” and that this conflicts with one of Deming’s 14 points. However, Montgomery and Woodall (2008) argue that the assumptions that lead to the 3.4 ppm metric are based on a process model that is probably wrong (as are all models) but one that is inherently useful because it focuses the organization on the critical concepts of continuous improvement and variability reduction.

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Companies deploying Six Sigma employ specially trained individuals, called green belts (GBs), black belts (BBs), and master black belts (MBBs). A project-based team approach is used in Six Sigma deployment (this is directly adapted from Juran’s philosophy). Teams used a structured problem solving approach with five steps; define. measure, analyze, improve, and control (DMAIC). See Montgomery (2009b) and Montgomery and Woodall (2008) for a discussion of the DMAIC problem-solving process. DMAIC is a direct outgrowth of the Shewhart cycle, advocated by Deming as an ideal framework for implementing continuous improvement.

BBs typically have about four weeks of specialized training, usually spread over a four-month period and combined with ongoing work on a Six Sigma project. They lead teams that are focused on projects with both quality and business (economic) impact for the organization. In most organizations, BBs train GBs and work on other activities such as new project identification.

GBs typically have less training, often one or two weeks, and either assist on major project teams or lead teams engaged in smaller, more focused projects. MBBs are often engaged in training both BBs and other MBBs. They often write and develop training materials, are heavily involved in project definition and selection, and work closely with business leaders called champions. The job of champions is to ensure that the right projects are being identified and worked on, that teams are making good progress, and to assist in making sure that the resources required for successful project completion are available. Champions are project sponsors and usually members of the senior management team. MBBs also work closely with other members of the senior leadership team. In many organizations BB and MBB positions are full-time. BBs and MBBs have specialized training and education on statistical methods and other quality and process improvement tools that equip them to function as team leaders, facilitators, and technical problem solvers. The American Society for Quality (ASQ) maintains a Six Sigma BB body of knowledge on their web site (www.asq.org/certification/sixsigma/ bok.html). There is some variability in the content of Six Sigma training from one organization to another, reflecting differences in the businesses involved, and among the courses and training offered by consultants. Some universities have begun to offer Six Sigma training, both for credit as part of regular academic degree programs and as non-credit or extension education. A number of organizations, including ASQ, some business organizations, and some consultants offer Six Sigma certification.

Design for Six Sigma (DFSS) is an extension of operational Six Sigma that takes the variability reduction and process improvement philosophy upstream from manufacturing or production into the product design process. DFSS is a structured approach efficiently commercializing technology that results in new products, services, or processes. DFSS spans the entire development process from the identification of customer needs to the final launch of the new product or service. Customer input is obtained through a voice of the customer process designed to determine what the customer really wants, to set priorities based on actual customer wants, and to determine if the business can meet those needs at a competitive price that will enable it to make a profit. Voice of the customer data is obtained in various ways, such as through customer interviews, by a direct interaction with and observation of the customer, through focus groups, by surveys, analysis of customer satisfaction data and through discrete choice experiments. The purpose is to develop a set of critical to quality requirements for the product or service.

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Traditionally, DMAIC is used to achieve operational excellence, while DFSS is focused on improving business results by increasing the sales revenue generated from new products and services and finding new application opportunities for existing ones. In many cases, an important gain from DFSS is the reduction of development lead time; that is, the cycle time to commercialize new technology and get the resulting new products to market. DFSS is directly focused on increasing value in the organization. Many of the statistical tools that are used in DMAIC are also used in DFSS. Designed experiments are particularly helpful. Experimentation with prototypes and computer models is a rapidly evolving field that is highly useful in DFSS ( Jones and Johnson, 2009).

Some organizations and use a variation of DMAIC, such as define, measure, analyze, design, and verify, for DFSS. DFSS specifically recognizes that every design decision is a business decision, and that the cost, manufacturability, and performance of the product are determined during design. Once a product is designed and released to manufacturing, it is almost impossible for the manufacturing organization to make it better. Table I illustrates that solving a problem once in engineering design is often les expensive then trying to fix it repeatedly in manufacturing. Furthermore, overall business improvement cannot be achieved by focusing on reducing variability in manufacturing alone (operational Six Sigma), and DFSS is required to focus on customer requirements while simultaneously keeping process capability in mind. Specifically, matching the capability of the production system and the requirements at each stage or level of the design process is essential. When mismatches between process capabilities and design requirements are discovered, either design changes or different production alternatives are considered to resolve the conflicts. Perry and Bacon (2007) provide a good introduction to many aspects of DFSS.

Lean manufacturing systems are designed to eliminate waste. By waste, we mean unnecessarily long cycle times, or waiting times between value-added work activities. Waste can also include rework, scrap, and excess inventory. Rework and scrap are often the result of excess variability, so there is an obvious connection between Six Sigma and Lean. George (2002) provided a good introduction to how Lean and Six Sigma work together. Important metrics in Lean are the process cycle efficiency, process cycle time, work-in-process, and throughput rate. Lean also makes use of many tools of industrial engineering and operations research. One of the most important of these is discrete-event simulation. Properly constructed and validated simulation models are often very good predictors of the performance of a new or redesigned system. Both manufacturing and service organizations can greatly benefit by using simulation models to study the performance of their processes. Lean improvement projects can be managed using DMAIC. The tools at each phase will differ somewhat

Non-recuring engineering cost Recurring test/assembly cost

Time 100 hour 15 minutes Repeats 1 10,000 Cost/hour ($) 100 50 Total ($) 10,000 125,000

Table I. The cheapest hours are

engineering hours

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from the Six Sigma tools, but the DMAIC project management framework is just as effective for Lean as it is for Six Sigma.

I advocated a deployment of Six Sigma combined with DFSS and Lean as an ideal systems framework in which to bring about Deming’s philosophy of continuous improvement. It is also consistent with Deming’s system of profound knowledge, which has the following components:

. Appreciation of a system. The system involves operations, design, and transactional components.

. Knowledge about variation. It reduces variation throughout the system.

. Theory of knowledge. It identifies key cause and effect relationships that permit system-wide optimization.

. Theory of psychology. Management, teams-workforce involvement, focus on business impact, leadership development.

Six Sigma integrated with DFSS and Lean clearly adopts a systems perspective as it is broad based and reaches into all aspects of a business. Knowledge about variation, its causes, and how to reduce it by identifying the relevant cause and effect relationships is integral to all Six Sigma thinking. The theory of psychology as it relates to management involvement, teamwork, and leadership is also essential to success. This is, as Deming advocated, a systemic end-to-end approach to quality and business improvement.

Conclusion Adopting a broad-based approach to business and quality improvement based on Six Sigma/DMAIC/DFSS/Lean systems would create many essential aspects of an idealized quality management system. Only the quality assurance aspect of the overall quality management system and some aspects of planning for quality would need supplemental tools and methods to succeed. This could have a profound effect on how engineers, statisticians and business students are educated in universities. For example, most engineering departments in US universities require a design course taken in the senior year that requires students to apply the principles and techniques that they have learned to solve a comprehensive design problem. The DMAIC problem-solving approach or DFSS could be the problem-solving framework taught in this course. Statisticians usually receive excellent education in the underlying methodology of the field. However, many statisticians have a mathematical view of problem solving, not the more pragmatic engineering approach. Few statistics programs teach anything about DMAIC and Six Sigma. Business students typically do not take any course that has a design experience, and may not be exposed to the technical tools of quality improvement. Even the quality management courses taught in many business schools usually do not focus on DMAIC as a problem-solving process. It is important for students from engineering, statistics, and business to see how the methods from their disciplines fit together in a system for creative problem solving. A joint design course for these students at the senior level based around DMAIC/DFSS would be a mechanism for improving their educational experience. This would be a challenging endeavor for faculty to develop and administer, but it could have a valuable contribution to the education of future business leaders.

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