2686099

The Impact of Six Sigma Improvement-A Glimpse into the Future of StatisticsAuthor(s): Gerald J. Hahn, William J. Hill, Roger W. Hoerl and Stephen A. ZinkgrafSource: The American Statistician, Vol. 53, No. 3 (Aug., 1999), pp. 208-215Published by: American Statistical AssociationStable URL: http://www.jstor.org/stable/2686099 .Accessed: 09/09/2014 16:10

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The Impact of Six Sigma Improvement-A Glimpse Into the Future of Statistics

Gerald J. HAHN, William J. HILL, Roger W. HOERL, and Stephen A. ZINKGRAF

Six Sigma improvements-a highly disciplined and statis- tically based approach for removing defects from products, processes, and transactions, involving everybody in the corporation-has been adopted as a major initiative by some of our leading companies. This is fundamentally changing the paradigm of how statistics is applied in business and industry, and has had a career-changing impact on those statisticians who have been involved. We describe the Six Sigma initiative and its evolution, the enthusiastic and visionary support by the CEOs at some major corporations that have embraced it, its successes to date, and the impact on statistics and statisticians. We then turn to a major theme-what statisticians must do to be maximally effective in this exciting new environment. These changes will not be limited to the companies that have adopted Six Sigma, or, for that matter, industry, but are all-pervasive. We discuss the dramatic longer term implications on our profession.

KEY WORDS: Change; Leadership; New paradigm; Pro- cess improvement; Quality.

1. INTRODUCTION-WHY THIS ARTICLE? The Six Sigma improvement initiative (Smith 1991;

Harry 1994) is having a major impact on the culture, operation, and profitability of some of our nation's largest companies, including Asea Brown Bavari (ABB), AlliedSignal, GE, Lockheed-Martin, Motorola, Polaroid, and Texas In- struments. The results have been impressive. The Six Sigma initiative was at least one key factor in Motorola winning the coveted 1988 Malcolm Baldrige Award for Quality, and produced reported savings of over $940 million in three years. Additionally, AlliedSignal reported an estimated savings of $1.5 billion from its Six Sigma initiative (per the company's 1997 annual report). Finally, in its 1998 An- nual Report, GE Chairman Jack Welch and his associates state that "We plunged into Six Sigma with a company-

consuming vengeance just over three years ago. We have invested more than a billion dollars in this effort, and the financial returns have now entered the exponential phase- more than three quarters of a billion dollars in savings beyond our investment in 1998, with a billion and a half in sight for 1999 ... Six Sigma has been an unqualified success."

With a name like Six Sigma, statisticians should clearly be interested! In fact, the Six Sigma initiative is highly sta- tistically based-even though, as we shall see, it is not being led principally by statisticians. Moreover, its emergence is having a profound impact on our careers. And, most importantly, it is a precursor to the way statisticians will be working in the future, even in operations that are not ex- plicitly involved in Six Sigma, or even in building a product. We describe the Six Sigma initiative, its impact, and the ways in which statisticians can be most responsive both now and in the future. These comments are based upon our combined experience at four companies (AlliedSignal, GE, Motorola, and Polaroid) heavily committed to making Six Sigma a reality.

2. WHAT IS SIX SIGMA QUALITY AND WHAT IS IT ACHIEVING?

2.1 The Basic Concept of Six Sigma The Financial Times (Oct. 10, 1997) defines the Six

Sigma initiative as "a programme aimed at the near- elimination of defects from every product, process and transaction." This concept was introduced at and popular- ized by Motorola in 1987 (Harry 1994) in their quest to reduce defects of manufactured electronics products. A brief history of the evolution of Six Sigma can be found in Harry (1998). When used as a metric, Six Sigma technically means having no more than 3.4 defects per million opportunities, in any process, product, or service. Statisticians may no- tice that having specification limits six standard deviations away from the average of an assumed normal distribution will not result in 3.4 defects per million. The number is ar- rived at by assuming that, in addition to random variability, the process average drifts over the long term by 1.5 standard deviations, despite our best efforts to control it. This results in a one-sided integration under the normal curve beyond 4.5 standard deviations-an area of about 3.4/1,000,000.

More important than the technical definition is the concept of Six Sigma as a disciplined, quantitative approach for improvement-based on defined metrics-in manufacturing, service, or financial processes. This drives the process of selecting projects based on their potential to improve performance metrics, and identifying and training the right people to get the business results. Projects follow a dis-

Gerald J. Hahn is Manager and Coolidge Fellow, Applied Statistics Pro- gram, GE Corporate Research and Development, P.O. Box 8, Schenectady, NY 12301 (Email: [email protected]). William J. Hill is AlliedSignal Fel- low and Director, Six Sigma Master Black Belt Program, Buffalo Research Laboratory, AlliedSignal, 20 Peabody Street, Buffalo, NY 14210. Roger W. Hoerl is Quality Leader, GE Corporate Audit Staff, 3135 Easton Turn- pike, Fairfield, CT 06431. Stephen A. Zinkgraf is Chief Executive Officer, Sigma Breakthrough Technologies, Inc.TM, 400 W. Hopkins Street, San Marcos, TX 78666. The authors thank the editor, associate editor, and two referees for their numerous helpful suggestions that have improved this article, and many colleagues who have impacted our thinking.

208 The American7 Statistician, August 1999, Vol. 53, No. 3 (? 1999 American Statistical Associationl


ciplined process of four macro phases: Measure, Analyze, Improve, and Control (MAIC). Sometimes a preliminary Define step is added; this relates to appropriate selection of projects, problem definition, and defining the metrics with their baseline and entitlement (optimal) levels. For example, at AlliedSignal metrics include Cost of Poor Quality, Rolled Throughput Yield, and Capacity Productivity, and methods for their valid estimation. The purpose of each step in the MAIC process is:

* Measure-select the appropriate responses (the "Y's") to be improved, based on customer inputs and other consid- erations (such as product yield), ensure that they are quan- tifiable, and that we can accurately measure them. Deter- mine what is unacceptable performance (i.e., a "defect"). Gather preliminary data to gauge current performance.

* Analyze-analyze the preliminary data to document current performance (baseline process capability), and to begin identifying root causes of defects (i.e., the "X's", or independent variables) and their impact, and act accord- ingly.

* Improve-determine how to intervene in the process to significantly reduce the defect levels. Several rounds of improvements may be required. Recently, special emphasis has been given to reducing variability.

* Control-once the desired improvements have been made, put a system into place to ensure the improvements are sustained, even though significant resources may no longer be focused on the problem

The major elements of Six Sigma implementation are strong leadership, initial focus on operations, clear performance metrics, aggressive project selection, and selecting and training the right people. The initiative is driven by leaders at the highest levels of the organization-such as the CEOs of GE (Jack Welch), Motorola (Bob Galvin), and AlliedSignal (Larry Bossidy)-and permeates through all levels of management and operations. This is not just corporate PR, and is, without question, the most important rea- son for success. These key leaders have created a sense of urgency for a radical culture change. But how did they become committed in the first place?

Of course, we don't know for sure, but we do know that in the early 1980s, Motorola was at risk of losing its semi-conductor business to Japanese competitors. Simulta- neously, Motorola senior management acknowledged less- than-satisfactory product quality. In response, Galvin pro- moted the development of Six Sigma to keep Motorola in business.

Larry Bossidy, upon taking over a complex and "average- quality manufacturer," created the vision of AlliedSignal as a premier company and used Six Sigma as one of the primary vehicles to achieve that positive image while at the same time creating bottom line growth and productivity improvement. In 1995, Bossidy addressed the GE Executive Council and impressed those leaders with Six Sigma and its very real and significant financial results.

While GE had implemented several major initiatives (e.g., Workout) over the years, Jack Welch realized that improved quality could provide a substantial boost to GE profitabil-

ity and that Six Sigma could take GE to the next level of performance-if the program was focused on getting tangible business results. In short, the sense of urgency has to be created by the company's senior leadership and aggres- sively deployed to the lowest level of the organization.

The main focus of Six Sigma in these companies was ini- tially on manufacturing, and specifically on cost and waste reduction, on yield improvement, and on operations where there is opportunity to improve capacity without major capital expenditure. There was also strong emphasis on understanding and satisfying customer needs. In addition, as or- ganizations realized how large the financial impact could be on nonmanufacturing processes, these have been heavily emphasized. More on this later.

Performance metrics are established that directly measure the improvement in cost, quality, yield, and capacity. Contrary to some "TQM" initiatives, financial figures are required both to select projects and to evaluate success, and performance metrics are tracked rigorously.

Projects are typically targeted for at least $50,000 annual impacts. At AlliedSignal, the initial projects generally exceeded $1 million in benefits, and many new projects are still of this magnitude. Practitioners (engineers, accoun- tants, computer scientists, and so on) are identified to work on these projects 50% to 100% of their time, with help from other team members. These people are given various names in different companies, such as "Black Belts" (GE, Mo- torola, Allied Signal), or "Variability Reduction Leaders" (Polaroid). We will refer to them as Black Belts (BBs).

The BBs take four to five weeks of intensive, highly quantitative training, roughly corresponding to the four macro steps of the Six Sigma methodology. They are asked to bring their laptop computers for in-class deployment, often using Excel, Minitab, or the simulation package Crys- tal Ball. Each week of training is typically separated by three or four weeks for application of the learnings/tools to the BBs' projects-see Figure 1 for a typical course out- line. Soft tools such as communication and team leadership skills are generally part of the curriculum. The "price of admission" is a significant project impacting the business' bottom line. These projects typically are derived from the business unit's strategic plans and goals.

The initial training courses in the company are usually conducted by an external expert (e.g., at GE by the Six Sigma Academy's Mikel Harry). The students typically are future BBs, managerial "Champions," and carefully se- lected Master Black Belts (MBBs), including some statisticians. The MBBs then take over the responsibility for training the Black Belts in their own business operations, for providing overall leadership, and for serving as change agents. Six Sigma "Champions" provide managerial support in terms of project selection and evaluation, selection of MBBs and BBs, and removal of barriers to success.

Upon completion of the first project, preferably within four months, the BB moves on to a new project repeating the deployment of the tools in the MAIC sequence. Good practice requires the BBs to formally and regularly report out on these projects to management. Also, project reviews are part of the training.

The American Statisticiacn, August 1999, Vol. 53, No. 3 209


Weeki * Week3 - Six Sigma Overview & the MAIC - ANOVA

Roadmap - DOE (Design of Experiments) - Process Mapping * Factorial Experiments - QFD (Quality Function Deployment) * Fractional Factorials - FMEA (Failure Mode and Effects * Balanced Block Designs

Analysis) * Response Surface Designs - Organizational Effectiveness Concepts - Multiple Regression - Basic Stats Using Minitab - Facilitation Tools - Process Capability * Week 4 - Measurement Systems Analysis - Control Plans

Week 2 - Mistake-Proofing - Review of Key Week 1 Topics - Team Development - Statistical Thinking - Parallel Special Discrete, - Hypothesis Testing and Confidence Continuous Process,

Intervals (F, t, etc.) Administration and Design Tracks - Correlation - Final Exercise - Multi-varn Analysis and Regression - Team Assessment Notes:

1)Project reviews are done each day in weeks 1-4 2. Hands-on exercises on most days 3. Three weeks of applied time between sessions

Figure 1. Six Sigma -Typical BB Training Curriculum.

Green Belts (GBs) receive reduced training-six to ten days, for example. Like the BBs, they enter the training with a chartered project important to their operation's success. Unlike the BBs, however, the GBs do not spend the prepon- derance of their time on Six Sigma projects. Many hourly workers in financial operations, factories, and so on, have also been trained, using the titles of Yellow Belts (YBs). They may receive a total of-four days of introductory training on the MAIC tools to assist on BB or GB teams, or con- duct their own projects. These training efforts are intended to get everyone involved in Six Sigma, with the objective of having every employee make improvements in their work processes a normal, everyday part of the job.

While the tools are not new, the Six Sigma approach adds considerable value to the use of existing tools. Its advan- tages include:

* Providing an overall "roadmap;" that is, a multistep approach to integrating the tools appropriately (MAIC). Many have commented that their college statistics courses left them confused about why they needed to learn various statistical tools, and how they fit together. The Six Sigma roadmap helps to make this clear, and has enabled practitioners to readily apply the tools to real problems, using readily accessible software.

* Stressing the need to understand and reduce variation, versus only estimating it.

* Emphasizing a data-based approach to management, versus gut feel or intuition. Six Sigma requires that every-

thing be quantified, even "intangibles" such as customer opinions.

* Developing standardized vocabulary, metrics, and tools throughout highly diverse companies.

2.2 The Evolution of Six Sigma Quality Beyond Manufacturing and Quality

Although most of the initial emphasis of Six Sigma was for quality improvement in manufacturing, it is now being applied in key areas beyond manufacturing, and beyond what would traditionally be considered "quality." Emphasis in these areas has, in fact, recently accelerated with the aim of ensuring that customers also reap the benefit of Six Sigma. For example, AlliedSignal has developed its commercialization thrust around Six Sigma concepts, voice of the customer, value chain analysis, and customer satisfac- tion. The focus is on getting good data on customer requirements, and on reducing failures and variation in product design, scale-up, and commercialization. AlliedSignal also has significant Six Sigma initiatives ongoing in financial and business services.

Similarly, at General Electric there has been a major focus on what is called Commercial Six Sigma. This has emphasized both the needs of GE's service businesses (GE In- formation Services, NBC, and the massive GE Capital Ser- vices), and the nonmanufacturing operations of GE's manufacturing businesses, such as software systems and development, billing, human resources, and so on. In addition, with the recognition that long-term success is highly dependent

210 General


on product and service design, there has been a significant drive in GE for Design for Six Sigma (DFSS), and, most recently, for Design for Reliability (DFR) as a key element of the Six Sigma initiative.

Each of these extensions has built on the foundation pro- vided by the basic Six Sigma concepts and training, including strong emphasis on successful projects and quantifica- tion of results. Thus, we have seen Six Sigma generalized for use beyond the traditional manufacturing arena to engineering, reliability, financial, and human resource processes. Moreover, we see opportunities for even broader areas such as banking.

2.3 What Have Been The Results? Six Sigma is working extremely well and building further

momentum. Some key reasons are: Quantified financial impact. We have already mentioned

some reported payoffs of the Six Sigma initiative at various companies. These have been the accumulation of numerous individual projects. For example, one team of three BBs at an AlliedSignal site returned more than $25 million in cost savings and capacity improvement on one project alone. GE's 1997 annual report lists numerous specific savings: $70 million in productivity gains for aircraft engines; $44 million in savings for appliances; $60 million in new in- surance written from one customer; $47 million benefits in lighting products; $42 million in productivity improvements for medical systems; $137 million for engineered plastics; and so on. In his address at the GE Company 1997 annual meeting, Jack Welch added that "Six Sigma has gone in less than two years from being an alien concept, full of complex calculations and unfamiliar jargon, to a consuming passion sweeping across the company." USA Today (July 21, 1998) provides further examples, including ones from Raytheon and Lockheed-Martin.

Continued top management support and enthusiasm. The support by top company leaders for the Six Sigma initiative remains unabated. According to GE's 1997 annual report, "Six Sigma training is now an ironclad prerequi- site for promotion to any professional or managerial posi- tion in the company-and a requirement for any award of stock options. Senior executive compensation is now heavily weighted toward Six Sigma commitment and success." In addition, all professional, supervisory and managerial employees must, as a minimum, be green-belt trained and have done a project by the end of 1998-and this, indeed, has happened. Thus, Jack Welch has made it clear, by word and deed, that if you are not enthusiastic about Six Sigma, GE is simply not the right company for you. All of this does wonders in driving involvement and enthusiasm at all levels of the organization.

The emphasis on a quantitative and disciplined approach to process improvement. Using the MAIC approach consistently across diverse businesses and processes has al- lowed these businesses to leverage the power of the tools to achieve significant tangible business results. Six Sigma has become a common language for different business units to talk to one another, share successes and failures, and learn

from one another. Senior leaders recognize that obtaining better results requires improving the process that generates the results-and that improving the process requires a disciplined, data-based approach, rather than the more traditional "ready, fire, aim" method. This leads directly to more effective use of statistical tools. Similarly, vague statements, such as "We think we have that under control," or "Perfor- mance seems to be improving," are no longer accepted. The typical response now is: "Show me the data!"

The value placed on understanding and satisfying cus- tonmer needs. While most companies claim this as a value already, it has been amazing to us how little we really knew about our customers prior to Six Sigma. With the emphasis on metrics, customer needs and our current performance in meeting them must be documented. Formal customer interactions and evaluations have replaced anecdotes. For example, quantitative measures, such as Customer Sigma and Product Sigma are internally reported quarterly for most AlliedSignal products to assess how well defects are being reduced in manufacturing and in delivered products. Six Sigma quality or 3.4 defects per million opportunities is the goal.

Combining the right projects, the right people, and the right tools. Statistical tools are clearly powerful, but only if used by the right people on the right projects in the right manner. When companies have stressed tools per se, the results have, sometimes, been unimportant, perhaps even trivial, applications. By trivial, we mean that the tools did not lead to major business results. We have seen tremendous synergy in carefully selecting the most important projects, and getting the most talented people properly trained to apply the appropriate statistical (and nonstatistical) tools to these projects. A key lesson learned is that training is en- hanced tremendously by directly and immediately applying the material learned to a real project-as required for Six Sigma training. Also, management has insisted that can- didates for significant Six Sigma roles, such as BBs and MBBs be the most highly regarded people in the business, not simply "warm bodies" who were available. Thus, project and people selection have been just as important to achieving results as use of the proper tools.

To put matters into perspective, USA Today (July 21, 1998) in an article titled "Firms aim for Six Sigma Effi- ciency," stated "Today, depending on whom you listen to, Six Sigma either is a revolution slashing trillions of dollars from corporate inefficiency, or it's the most maddening management fad yet devised to keep front-line workers too busy collecting data to do their jobs." The article then provides evidence on both sides of the argument. Although we cannot predict specifically what direction the Six Sigma initiative will take in the future, we are convinced that the disciplined, data-oriented approach that is the foundation of Six Sigma will survive and flourish.

2.4 Six Sigma, Statistics, and Statisticians Statistics provides a major backbone for the Six Sigma

initiative. It is most gratifying, and sometimes amazing, to hear engineers or MBAs talk about what data is needed to "identify the key X's that impact the Y's." A decade ago,

The Americani Statistician, August 1999, Vol. 53, No. 3 211


people in our companies identified DOE as the Department of Energy; today everybody equates it with the "design of experiments." In fact, it is common practice to boast to management that this or that result was obtained via a DOE. Statistical and related methods are being used extensively by Six Sigma companies, and by literally thousands of practitioners systematically using statistics on focused projects yielding significant financial results.

One might be tempted to conclude that the Six Sigma initiative is the brainchild of statisticians, that statisticians played the key role in its introduction, and in general, that statisticians are living in a state of exalted reverence. Al- though we might like to believe these to be the facts, they are not. In general, the originators and drivers of the Six Sigma initiative have been predominantly engineers and managers-and not statisticians. One reasons for this is because statisticians tended to be more techniques-oriented than business vision-oriented. Another is that statisticians, as principally staff resources, were not expected to generate culture change.

The overwhelming majority of Six Sigma trainers and leaders today are not statisticians, and statisticians are not directly consulted about the proper use of statistical methods on most projects. Thus, we conjecture that while the use of statistical methods may have increased substantially in our companies since the advent of Six Sigma, the number of professional statisticians has not changed dramati- cally (although their forces have been augmented by external consultants). This is not to say that statisticians have not been actively engaged in Six Sigma projects, but only that the massive number of ongoing projects does not permit the routine involvement of professional statisticians. AlliedSig- nal may be an exception relative to training, since in one of the three major business groupings statisticians have been among the lead trainers of MBBs, BBs, and GBs. In short, even though this may be the next generation or, possibly even a "golden age of statistics" in business and industry, it is not necessarily a golden age for applied statisticians, at least not in terms of massive utilization or hiring.

3. STATISTICIANS' RESPONSE TO SIX SIGMA The Six Sigma initiative is significantly impacting the

lives of statisticians in the companies involved. In this section we provide recommendations, based on our experiences, on how statisticians can be most effective in the new environment. Moreover, most applied statisticians can ex- pect similar changes, if they are not yet encountering them. We consider these larger implications in the next section.

3.1 Do Not Get Bogged Down on Technical Details A traditional statistician's initial response to Six Sigma

may be to focus on technical details. For example, where is the proof that all processes shift exactly 1.5 standard deviations from the mean-as factored into the 3.4 defects per million opportunities calculation? (Of course, there isn't one; it is a rule of thumb based upon Motorola's experience.) And exactly what do 3.4 parts defective per million opportunities mean if the sample size of opportunities is less than a million? And what is an "opportunity"

anyhow? Also, shouldn't we be concerned about the com- plexity and mission of the system, and shouldn't we have different requirements for, say, a dishwasher from an aircraft engine? (Of course, we should.) Shouldn't some Y's be weighted heavier than others, even within the same process? We find that such discussions rarely arise in real applications. In practice, few processes actually operate at or near Six Sigma-even though we might like them to. In- stead, the new statistician will serve as a leader to drive the real and more important focus of Six Sigma-that of ele- vating the importance of satisfying customers' needs and using a highly disciplined, quantitative approach to gaining improvements. 3.2 Participate in the Changes

The Six Sigma effort in our companies has certainly been a boost to professional statisticians. However, as previously noted, most of the statistical work is being done without statisticians. This has been made possible by the widespread availability of easy-to-use statistical software. In fact, when one considers the magnitude of the Six Sigma effort in our corporations, one soon concludes that it is simply not fea- sible for professional statisticians to do everything. There just are not enough of us to go around! In addition, this would defeat the whole purpose of trying to get everyone involved. We recognize that the consequence might not be an optimum analysis in most instances. However, statisticians need to recognize that an optimum-as opposed to a reasonable-analysis is often just not that vital, or even cost-effective. A good general strategy often is to identify and help train knowledgeable engineers or business persons to become the "local statistical gurus." Ideally, these people would respond to most issues on site, but consult with professional statisticians when needed.

We have also found that after Six Sigma is going strong, statisticians are highly sought after and valued. Statisticians who are willing to take a leadership role are often requested by the business to aid in tough and vital projects. In several cases at AlliedSignal and GE, professional statisticians in a central group were asked to relocate to the business for three to six months to lead important improvement projects. In essence, statisticians are now "pulled" in by the business, instead of the traditional push of the past. Rather than statisticians scheduling statistical training, and trying to convince people to attend, businesses are scheduling the training. Statisticians who previously complained about lack of support are now overwhelmed with demand (a good problem to have!)-though, often, in a less hands-on capacity. The key point, therefore, is not that Six Sigma is passing us by, but rather that it has changed the paradigm of how we add value. From our experience, statisticians with a strong sense of how a business is run and a firm grasp on engineering concepts will be the most successful. In a leadership role, statisticians will find that a background in business concepts will provide insights into leveraging Six Sigma efforts into big business results.

3.3 Contribute to the Effort Relieved of many of the day-to-day tasks of routine data

analysis, statisticians become available to make key contri-

212 General


butions in those areas where their talents are most uniquely required. We comment on some of the broader elements in the next section. In those companies where Six Sigma is already a way of life, we urge statisticians to:

Help lead and improve the training. First, statisticians have a major role to play in "training the trainers." Statis- ticians may train Master Black Belts in useful advanced methods so that MBBs are better prepared when they train BBs. In fact, at both AlliedSignal and GE, some statisticians are themselves MBBs, and are actively engaged in training. Second, a major aspect of improved training is to identify and add important topics that are currently ex- cluded from the Six Sigma quality training curriculum. Ex- amples include greater emphasis on graphical tools, reliability improvement tools, data quality issues, advanced DOE, and requirements for valid data-based investigations (including Deming's differentiation between analytic and enu- merative studies). At the same time, we should push for de-emphasizing some topics, such as statistical significance tests-an unfortunate carry-over from the traditional ele- mentary statistics course. We would suggest a greater focus on confidence intervals-these achieve the aim of formal hypothesis testing, often provide additional useful information, and are not as easily misinterpreted.

Develop user-robust tools. As data analyses are conducted more and more by nonstatisticians, it becomes our responsibility to provide "user-friendly" methods and software. This includes developing methods that are reasonably easy to use and to understand-for example, simulation-based approaches-in a form that minimizes the likelihood of misuse. Also, we need to document these tools in a manner that makes them both readily understandable and easy to implement, including providing guidance to help explain the outputs. While most commercial statistical packages are becoming easier to use, they are all too often very easy to misuse, as well. For example, some popular packages will calculate and plot the interactions from a Resolution III design, without warning that these are in fact confounded with the main effects!

Become personally involved in the more complex and more important problems. Although statisticians are being replaced by Black Belts and Green Belts in many day-today Six Sigma projects, we have an important role to play in two areas. The first is in problems that are technically complex. Initial waves of Six Sigma projects rightfully tend to be directed at reaping the proverbial "low-hanging fruit"- often requiring relatively straightforward methods. Thus, the initial projects may aim to reduce scrap and rework in factories, or outright blunders in accounting operations. Next, the focus switches to such things as increasing capacity, minimizing "dead on arrival" and "infant mortality" field failures for products, or making better decisions on credit applications. Eventually, the emphasis must move to more proactive projects, such as enhancing overall product life, or optimizing inventory management. As our Y's (using Six Sigma terminology) move out in time, both the needed data and the required assessments become more involved and will require more complex assessments-such

as, for example, the analysis of censored data. Professional statisticians can provide important guidance here, although some of the work may be "outsourced" to a statistician at a local university. Our goal should be to help build knowledge so that the need for our future involvement is steadily reduced. The second case is that of problems that are of special criticality to the company. In that case, there is often a reluctance to outsource, and an in-house statistician with the necessary business, domain, and operational knowledge can expedite the drive to a speedy and correct solution.

Look toward the future and be a force toward proactive- ness. As we become less involved in "day-to-day" crises, we need to play a more substantial role in helping develop a framework that will proactively avoid crises in the future. One critical area is that of helping build quality and reliability into products and services up front (see Hahn and Hoerl 1998). To do this, we must seek new tools and techniques that work in such applications, and be willing to promote and pilot these on important projects. These should then be integrated into the Six Sigma process and training. Success in this and other areas will also require the development of appropriate metrics for future cost avoidance. Another opportunity is technical guidance in ensuring the establishment of comprehensive and easy-to-use' databases that provide early warning of problems and help us understand their root causes. Companies implementing Six Sigma have found that the inability of existing databases to track and analyze the performance metrics is often a major weak- ness. Frequently, such databases become critical to the rapid identification of potential crises, and enable the organization to move out of the "fire-fighting" mode. Theoretically, the databases should be proactively upgraded, but this issue has generally been addressed only when the inadequacies of the current databases prevent progress. Because of statisticians' understanding of the difference between data quantity and data quality, we can provide significant value in the design of databases and measurement systems for new processes, in addition to the retrofit of existing ones.

4. LONGER TERM IMPLICATIONS OF SIX SIGMA: THE FUTURE OF STATISTICS AND

STATISTICIANS

4.1 A New Paradigm The implementation of Six Sigma in our companies is

fundamentally changing the paradigm of how statistics is applied in business and industry. We believe these changes point to the future direction of the entire field of statistics, and will impact the environment for statistics not only in businesses that practice Six Sigma, but in all businesses. In addition, we believe the ramifications will not be limited to manufacturing businesses, but will impact many other en- terprises, such as banking, healthcare, academia, and gov- ernment.

In the past, many applied statisticians have operated, to a large degree, as data analysts. The specific changes we see to this paradigm include the need for exerting more leadership and taking a more holistic approach (see also Hahn and Hoerl 1998). The reasons we feel that the changes apply

The American Statistician, August 1999, Vol. 53, No. 3 213


to the future of statistics, and will impact virtually all applied statisticians, are many. First, they are logical changes, and appear to work better. Second, they are consistent with the changes occurring in our environment that are beyond our control, such as the information technology revolution. Third, the methodology of Six Sigma is generic; it can be applied not only to profit-seeking companies, but also to any activity which is concerned with cost, timeliness, and quality of results, including such important application areas as healthcare and the national census. Academia will also need to adapt to these changes to stay relevant. In the case of statistics departments, this will not only include the training of statisticians, but also the courses for nonstatisticians. Fourth, and perhaps most importantly, these changes are now being driven by the leaders of some of our largest and most influential companies. Therefore, "just saying no" is not an option.

4.2 A Broader Leadership Role Statisticians still have significant roles to play; in fact,

these are more important roles than ever before. However, these roles are more as "leaders" than as "doers." For example, we can influence training curricula for thousands. We can propose the "right" software packages to management; we can mentor the Black Belts and Green Belts on their key projects; we can identify best practices to be shared-not to mention reviewing projects, identifying outside resources to bring in as needed, and getting personally involved in leading particularly difficult or important undertakings. We believe statisticians in the future will spend more time on these tasks, and less time analyzing data.

This change of focus presents a challenge to the profession. Statisticians are typically taught how to plan experiments and analyze data, but may be lean on leadership skills, and, therefore, may feel both unprepared and uncomfortable in exerting a broader role. If the preced- ing sounds familiar it should. Deming (1986) and his associates (e.g., Joiner 1985) have consistently urged us to be "statistical leaders." This, by and large, has not happened (Hahn 1995). The role that we recommend, though bear- ing important similarities to Deming's proposals, is more focused, and perhaps less ambitious. For example, we do not propose that statistical leaders be given a carte blanche to pursue whatever appears to them to be most beneficial to the company, or that they necessarily report directly to the CEO. We do propose, however, that statistical leaders work toward looking at the broader implications of problems, "think out of the box," and be concerned with much more than narrow technical details.

It is becoming more common that major corporations offer leadership and business acumen training and learn- ing experiences for their employees. Industrial statisticians should seek out such opportunities to broaden their knowledge of business success factors and to identify opportunities to have greater impact.

Of course, all of this should not imply that we need not also be highly knowledgeable in the technology of mod- ern data analysis. This is imperative, especially as our cus-

tomers become more knowledgeable. Our point is merely that in this arena our role will be less as doers, and more as expert guides.

4.3 A Holistic Approach More important than data analysis in today's environ-

ment, and harder to out-source, is the ability to take a holistic approach to problem solving, and to help others do the same. By "holistic approach" we mean a disciplined, objective approach to accurately identifying and diagnosing a problem, and developing a multistep strategy to resolve it. An excellent example, though not a Six Sigma program per se, can be found in Sematech's multistep approach to process qualification (Spencer and Tobias 1995). This goes way beyond identifying the appropriate tool to analyze a partic- ular dataset, and is the real unique value added by the Six Sigma approach. While the scientific method teaches the importance of data, it does not in itself teach scientists how to properly interpret data in light of variation. Knowledge of statistical tools is needed here. In addition, few profes- sions, with industrial engineering a possible exception, are routinely taught multistep approaches to problem solving. Unfortunately, most statisticians are not prepared for holistic problem solving in their formal training, despite the fact that they have powerful tools to do it.

4.4 Changing Management Expectations Another key change has been the end of what two of us

(Hahn and Hoerl 1998) have described as the era of "benign neglect" of statisticians by management. In some cases in the past, management has paid the bills, but did not show much interest in what statisticians actually did, and pro- vided them limited exposure. This is certainly not the situ- ation today. Most industrial statisticians are definitely "on the firing line," and those that are not are prone to down- sizing. Statisticians, like others, are directly accountable for financial results of projects, have broad visibility, and are making more presentations to management. This requires them to be outstanding communicators, and to be able to re- late statistical analyses to things managers care about most strongly, like cost and sales. It also requires them to be more selective in the projects they accept, and to better balance completeness of the statistical analysis with time constraints.

4.5 The Need for Broader Statisticians Increased managerial scrutiny will require broader based

statisticians. Using a medical analogy, we need more general practitioners and fewer specialists. This is not to suggest that the profession will not need some specialized ex- perts. However, such specialists may reside permanently only within businesses where their specialization is routinely required, such as for clinical trials in the pharmaceu- tical industry. In addition, statisticians also need an appre- ciation of such related areas as construction of databases, simulation and operations research, and nontraditional mod- eling techniques such as neural nets, to name a few-to say nothing of subject matter knowledge. In summary, statisticians must adapt to the problems the organization is cur-

214 Genieral


rently facing and be flexible to respond quickly to a wide range of customers and organizational challenges. This includes taking on projects from a broad range of areas in the company to build the business acumen and other knowledge for leading change and Six Sigma efforts.

5. CONCLUDING REMARKS The Six Sigma fervor is having a major impact on some

of our largest companies, and is getting excellent results. Statisticians can take just pride in the fact that they are the originators of many of the tools of data analysis and experimental design upon which these concepts are based. The fact that the methods are being applied principally by practitioners, rather than statisticians, should be regarded as a welcome development and a significant challenge to us to expand our role. We need to broaden our perspective, and be more vital than ever before-though in a more proactive, leadership, and holistic capacity. Moreover, we feel that, although the details may vary, similar forces to those underlying the emergence of Six Sigma are in force in most areas of applied statistics. Thus, as a profession, we

need take most seriously the new paradigm and the lessons learned therefrom.

[Received January 1998. Revised September 1998.]

REFERENCES Deming, W. E. (1986), Out of the Crisis, Cambridge, MA: Massachusetts

Institute of Technology, Center for Advanced Engineering Study. Hahn, G. J. (1995), "Deming's Impact on Industrial Statistics: Some Re-

flections," The American Statistician, 49, 336-341. Hahn, G. J., and Hoerl, R. (1998), "Key Challenges for Statisticians in

Business and Industry" (with discussion), Technometrics, 40, 195-213. Harry, M. (1994), The Vision of Six Sigma. Roadmap for Breakthrough,

Phoenix, AZ: Sigma Publishing Company. (1998), "Six Sigma: A Breakthrough Strategy for Profitability,"

Quality Pr-ogress, May, 60-64. Joiner, B. (1985), "The Key Role of Statisticians in the Transformation of

North American Industry," The American Statistician, 39, 224-227. Smith, W. (1991), Presentation at the Case Studies Conference, Center

for Quality and Productivity Improvement, Madison, WI: University of Wisconsin-Madison.

Spencer, W. J., and Tobias, P. A. (1995), "Statistics in the Semiconductor Industry: A Competitive Necessity," The American Statistician, 49, 245- 249.

The American Statisticiani, August 1999, Vol. 53, No. 3 215


Article Contentsp. 208p. 209p. 210p. 211p. 212p. 213p. 214p. 215

Issue Table of ContentsThe American Statistician, Vol. 53, No. 3 (Aug., 1999), pp. 177-296Front Matter [pp. ]Statistical PracticeBayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System [pp. 177-190][Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System]: Discussion [pp. 190-196][Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System]: Discussion [pp. 196-198][Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System]: Discussion [pp. 198-200][Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System]: Reply [pp. 201-202]

Shaping Statistics for Success in the 21st Century: The Needs of Industry [pp. 203-207]The Impact of Six Sigma Improvement-A Glimpse into the Future of Statistics [pp. 208-215]Explaining the Saddlepoint Approximation [pp. 216-224]Practical Saddlepoint Approximations [pp. 225-232]Calculating Accuracy Rates from Multiple Assessors with Limited Information [pp. 233-238]Quantile Plots, Partial Orders, and Financial Risk [pp. 239-246]A Note on Confidence Interval Estimation in Measurement Error Adjustment [pp. 247-253]Effect of Prior Specification on Bayesian Design for Two-Sample Comparison of a Binary Outcome [pp. 254-256]The NBA as an Evolving Multivariate System [pp. 257-262]The Foundations of Statistics at Stanford [pp. 263-266]Teacher's CornerThe Complications of the Fourth Central Moment [pp. 267-269]Transforming Variables Using the Dirac Generalized Function [pp. 270-272]An Improved Result Relating Quadratic Forms and Chi-Square Distributions [pp. 273-275]

Statistical Computing and GraphicsDot Plots [pp. 276-281]Statistical Computing Software ReviewsComparisons of Software Packages for Generalized Linear Multilevel Models [pp. 282-290]

Reviews of Books and Teaching MaterialsReview: untitled [pp. 291]Review: untitled [pp. 291-292]Review: untitled [pp. 292]Review: untitled [pp. 292-293]Review: untitled [pp. 293-294]Review: untitled [pp. 294]

Letters to the Editor [pp. 295-296]Back Matter [pp. ]

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