This chapter will give you a straight-out definition of Six Sigma, and will elaborate on that definition just enough to make you dangerous. We’ll also tell you where Six Sigma came from and why it exists. (Preview: to eliminate defects!) We’ll summarize the Six Sigma problem-solving methodology known around the world as DMAIC (for Define-Measure-Analyze-Improve-Control).

Also in this chapter, we’ll briefly introduce the key principles that underlie Six Sigma. You may have heard the phrase “Y is a function of x.” This is the central principle of Six Sigma: that every observed effect (Y)—be it desired or undesired—is attributable to one or more “root causes” (x). How you get from the Y to the x is the essence of Six Sigma.

In its fullest form, Six Sigma is a world-class management system for driving, achieving, and sustaining breakthrough improvements in every part of an organization. Through structured planning and systematic project execution, an organization lever-ages Six Sigma to achieve its most important business objectives.

Essentially, Six Sigma is about improving processes, or the way work gets done in an organization. This is where the rubber of performance improvement meets the road. If you operate your processes at Six Sigma quality, then you commit no more than 3.4 defects per million opportunities for defects. This means you have to operate your processes correctly the first time 99.9997 percent of the time!

If that sounds too stringent, consider the table that follows. It gives you the counterintuitive truth about how good you have to be. In today’s business world and organizational climate, 99 percent good is simply not good enough. To stay in business and be competitive, you have to be Six Sigma good.

In its totality, Six Sigma is a vision, a philosophy, a management system, a scientific methodology, and a performance-improvement toolbox.

The point of Six Sigma is to operate nearly flawless processes such that all the work performed is right the first time. Here’s the disturbing reality of how most organizations operate: they have far more defects and errors than they think. The reason is because they think their scrap rate might be, say, just 1 percent as shown by the simple Flow diagram that follows.

Proud managers look at numbers like this and say, “We’re not doing too badly; only 1 out of every 100 products or transactions we make is defective.” What they aren’t examining as closely is the cost, time, and resources involved in achieving the 99 percent yield.

Typically, organizations engage in huge amounts of rework to achieve the yield they claim. A part is the wrong size, so the final product has to be reassembled with the right part. A track house has to be painted over again because the wrong color was applied at first. A creditor needs to reapply a payment that was wrongly applied to another customer’s account.

In the end everything looks fine, but in the process you commit a cardinal business sin: you engage in rework to make it right when you could have done it right the first time. Therefore, in reality, your 99 percent yield might look like the following diagram.

In this example, you see that 20 items (or outputs) were not completed right the first time, so they had to be put through the rework process. While yield looks like 99 percent, first-time yield is only 80 percent. And the expense to the organization is enormous. Therefore, it’s all about how you get there; if you arrive at your destination through rework, you are losing money along the way. If you do it right the first time, you keep your money in your pocket where it belongs.

As with Lean, we can trace the roots of Six Sigma to the nineteenth-century craftsman, whose challenges as an individual a long time ago mirror the challenges of organizations today. The craftsman had to minimize wasted time, actions, and materials; he also had to make every product or service to a high standard of quality the first time, each time, every time.

The roots of what would later become Six Sigma were planted in 1908, when W. S. Gosset developed statistical tests to help analyze quality data obtained at Guinness Brewery. About the same time, A. K. Erlang studied telephone traffic problems for the Copenhagen Telephone Company in an effort to increase the reliability of service in an industry known for its inherent randomness. It’s likely that Erlang was the first mathematician to apply probability theory in an industrial setting, an effort that led to modern queuing and reliability theory.

With these underpinnings, Walter Shewhart worked with Western Electric (a forerunner of AT&T) in the 1930s to develop the theoretical concepts of quality control. Lean-like industrial engineering techniques did not solve quality and variation-related problems; more statistical intelligence was needed to get to their root causes. Shewhart is also known as the originator of the Plan-Do-Check-Act cycle, which is sometimes ascribed to Dr. Edwards Deming, Shewhart’s understudy.

As the story goes, Deming made the connection between quality and cost. If you find a way to prevent defects, and do everything right the first time, you won’t have any need to perform rework. Therefore, as quality goes up, the cost of doing business goes down. Deming’s words were echoed in the late 1970s by a guy named Philip Crosby, who popularized the notion that “quality is free.”

War and devastation bring us to Japan, where Deming did most of his initial quality proselytizing with another American, Dr. Joseph Juran. Both helped Japan rebuild its economy after World War II, consulting with numerous Japanese companies in the development of statistical quality control techniques, which later spread into the system known as Total Quality Control (TQC).

As the global economy grew, organizations grew in size and complexity. Many administrative, management, and enabling functions grew around the core function of a company to make this or that product. The thinking of efficiency and quality, therefore, began to spread from the manufacturing function to virtually all functions—procurement, billing, customer service, shipping, and so on. Quality is not just one person’s or one department’s job. Rather, quality is everyone’s job!

This is when quality circles and suggestion programs abounded in Japanese companies: no mind should be wasted, and everyone’s ideas are necessary. Furthermore, everyone should continuously engage in finding better ways to create value and improve performance. By necessity, quality became everyone’s job, not just the job of a few … especially in Japan, at a time when there was precious little money to invest in new equipment and technology.

The rest of the story might be familiar if you’re old enough to remember. By the late 1970s, America had lost its quality edge in cars, TVs, and other electronics—and they were suffering significant market share losses. Japanese plants were far more productive and superior to American plants, according to a 1980 NBC television program, If Japan Can Why Can’t We?

In response to all this, American companies took up the quality cause. They made Deming and Juran heroes, and institutionalized the Japanese-flavored TQC into its American counterpart, Total Quality Management (TQM). They developed a special government award, the Baldrige Award, to give companies that best embodied the ideal practice of TQM. They organized all the many elements and tools of quality improvement into a teachable, learnable, and doable system—and a booming field of quality professionals was born.

The co-founder of Six Sigma, Dr. Mikel Harry, has often said that Six Sigma shifts the focus from the business of quality to the quality of business. What he means is that for many years the practices of quality improvement floated loosely around a company, driven by the quality department. And as much as the experts said that quality improvement has to be driven and supported by top executives, it generally wasn’t. Enter Jack Welch, the iconic CEO who led General Electric through 2 decades of incredible growth and consistent returns for shareholders. In the late 1980s, Welch had a discussion with former AlliedSignal CEO Larry Bossidy, who said that Six Sigma could transform not only a process or product, but a company. In other words, GE could use Six Sigma as AlliedSignal was already doing: to improve the financial health and viability of the corporation through real and lasting operational improvements.

Welch took note and hired Mikel Harry to train hundreds of his managers and specialists to become Six Sigma Black Belts, Master Black Belts, and Champions. Welch installed a deployment infrastructure so he could fan the Six Sigma methodology out as widely as possible across GE’s many departments and functions. In short, Welch elevated the idea and practice of quality from the engineering hallways of the corporation into the boardroom.

Lest we not be clear, the first practical application of Six Sigma on a pervasive basis occurred at Motorola, where Dr. Harry and the co-inventor of Six Sigma, Bill Smith, worked as engineers. Bob Galvin, then CEO of Motorola, paved the way for Bossidy and Welch in that he proved how powerful Six Sigma was in solving difficult performance problems. He also used Six Sigma at Motorola to achieve unprecedented quality levels for key products. One such product was the Motorola Bandit pager, which failed so rarely that Motorola simply replaced rather than repaired them when they did fail.

In Six Sigma, then, we find the most mature system for ensuring quality at the organizational level, the operational level, the process level, and the individual product level. This is what gives Six Sigma its status as a world-class approach to quality improvement: its scientific validity, its focus on the execution of many different projects in all functions, its requirement to achieve financial return, and its robust nature as a management system.

We hope what you’ve read so far in this chapter doesn’t seem like Greek to you, even though sigma is a Greek letter! Statistics really aren’t as intimidating as they seem, and it’s not as hard as you might think to become astute about Six Sigma. Of course, Parts 2 and 3 of this book will take you a little deeper into some of Six Sigma’s methods as they are integrated with those of Lean.

Here in this section, you can keep having fun just learning about Six Sigma without the hard work of actually doing a Six Sigma or Lean Six Sigma project. And before you do, anyway, it might be very helpful to understand some of the Six Sigma concepts that underlie the application of its tools. These concepts follow, so know them well because they’ll put you in the right mind-set for success.

In your travels around your company, you may hear the phrase “Y is a function of x” coming from Black Belts, Green Belts, and others. If you only memorize one Six Sigma equation, memorize this one. It will not only change your view of business, but it will change your view of life.

Y is the outcome you desire. The xs are the causal factors, or inputs, that are critical to producing the outcome. The f is the function performed on the inputs to produce the outputs. Y is a function of x.

A good cup of coffee (Y) is a function (f) of properly managing the quality and interaction of several critical xs: beans, coffeemaker, water. A low golf score is a function of the golfer’s swing, strategy, mood, course conditions, weather, caddy, and so on.

The most important thing to know about Y = f(x) is that few rather than many xs carry leverage in producing the desired outcome. This is true for any product or system, no matter how complex. Of the thousands of variables affecting any system, only a few really matter.

Say you are applying for a job but you can’t get an interview. Or you get interviews but never get offered a job. What is the issue? Y is a function of x. You might conveniently make yourself think “they just don’t see how good I am.” So they are the cause, the x.

Upon deeper introspection, maybe your resumé has errors, or maybe you talk loudly and come across a little “rough.” Maybe your expectations are too high and you are applying for jobs that really require more education or experience. In any case, you better figure out what the real connections are between your Ys and xs or you’ll never get what you want.

The other part of thinking in terms of Y = f(x) is that it drives you down the chain of causation as far as possible, to the place where the real leverage resides. Say you want money. How do you get money? You need a good job; you need an entrepreneurial business. How do you get these? You need some education or some experience, some training; you need a mentor. Which training or mentor do you choose and why?

Go all the way back. Ask why five times. Do not settle for surface-level thinking. Go back to the bottom-most source of leverage and work there. In this sense Y = f(x) is much like the paradox of the self: the more you focus on a problem, or on fixing a problem, the worse it can get.

Sometimes you have to take your eye off the goal and focus on the process—the necessary xs that will naturally produce the goal like it is ripe fruit falling off a tree. This is the root cause. If you want money, you might want to start improving your personality, because then more people will like you. Then you might want to get to know as many people as possible—to increase your odds of becoming favored by someone who can give you a great job, or connect you to one.

If you’re already a people person, the best thing for you might be to hit the books for a while. Whatever you do, if you want something, or your organization needs to do something better, it is advisable to meditate on Y = f(x). The more thought you put into it, the more likely you are to discover the leverage you need to make your dream come true.

This is the very concept that grounds Six Sigma to reality. It’s all too easy for any business executive or even a design engineer to make decisions based on “experience” or “gut feel” because they’re just so darn good at what they do. Six Sigma is not biased toward this type of thinking. Six Sigma says that if you don’t have the data, you don’t have squat.

And by the way, if you do have data, it better be valid. So your random sample better be taken with adherence to proper statistical standards; your measurement gauges better be accurate (remember root cause?) If your data is inaccurate because it was produced by faulty measurement gauges, then your analysis of that data will yield conclusions that are useless. All your effort will still not stop that stubborn defect from recurring over and over again.

If operational excellence is your goal, then you have to embrace data like it’s your best friend. Your very best friend. That means you’ll certainly know it well enough to know where it is, what it represents, and what it means. And you’ll certainly know if and when it is lying to you!

Dr. Harry has often said, “You only measure what you value; if you don’t measure it, you really don’t value it.” How true this is. When is the last time you checked the balance in your checkbook or bank account, and how often do you do so? The answer will tell you exactly how much you value money or not.

Numbers really do tell all, especially when it comes to businesses and organizations. Even if you are the United Way, you want to demonstrate how many children you help with the donor’s dollars. While United Way does not exist to make money, it still measures what it values. Without a strong competence in measurement, no organization can survive.

Six Sigma simply takes this undeniable fact to a deeper level. It has great value when performance problems are complex, or when smart people and common sense simply fall short of solving problems. If you take this seriously, you will experience the same discovery thousands have discovered.

You will find that data, when gathered, analyzed, and interpreted correctly, will often teach you something you didn’t know. Think of data and an obsession with measurement as your way of checking up on yourself. You don’t build a bridge without the numbers; and you don’t run a process without the numbers, either. If you do, you will crash and burn.

No, we don’t mean this in the same sense of someone getting something they don’t deserve. You may feel entitled to your job, but you always have to justify your suitability for it. In other words, you are only entitled to the very best you can possibly do. Or the very best you deserve.

Companies, too, can only expect to do their very best with what they have. Yet by no means does this mean an organization can just “try hard” and leave the rest to fate. To operate at Entitlement means that your process is producing its best possible output levels. All the conditions and possible errors or interruptions are held at bay.

Some rightly describe the idea of Entitlement as the best you can possibly operate without redesigning your process. So if one day you put 18 widgets through the process, while usually you make 15 or 16, then you’ve taught yourself something about what is possible. You’ve taught yourself that you are entitled to make 18 units a day, no less, and maybe even more.

A car has an Entitlement performance that can be achieved, depending on how the owner uses it. The car is entitled to a certain miles per gallon, tire life, top speed, life span. Your yard is entitled to be free of weeds at all times, only requiring certain functions to be performed as scheduled with no error.

But not all things operate at their Entitlement at all times, because the function varies, or, more specifically, the way the work gets performed isn’t always the same. Therefore, your goal should be to reduce the variation in your processes. Always change your oil every 3,000 miles. Every six months, give your yard a weed pre-emergent treatment, cutting them off at the source before they grow. And so on.

Entitlement is where Six Sigma can take your process the way it’s currently designed. A hacksaw, for example, is made to cut through certain metals with certain properties. You surely can’t use it to chop down a big tree—not in a timely manner, anyway.

Say you use that hacksaw to cut through a 2×4. With no interruptions or unnecessary problems, you can cut through the 2×4 in 12 minutes with zero errors. That is your Entitlement, and you know because you’ve studied and optimized the functions (using data) of using the hacksaw for cutting 2×4s.

If you change your technology and use a handsaw with a large blade, your 2×4 cutting Entitlement changes. Now you are entitled to cut through your wood in 1 minute, not 12—with no errors, of course. This is the role of Design for Six Sigma (DFSS): the application of Six Sigma tools to build in better quality and to prevent defects (time or quality related) from occurring in the first place.

Some say that the typical level of Entitlement for the typical process is around four sigma (99.379 percent good); if you want to reach six sigma (99.9997 percent good) performance, you usually have to redesign the process using DFSS or some other tool. In any case, you should always strive to operate your processes at their Entitlement level, all the time.

We would miss the mark of informing you about Six Sigma if we didn’t tell you about the DMAIC methodology. That’s the Define-Measure-Analyze-Improve-Control cycle, which is also known as the Six Sigma breakthrough strategy. This is the structured process by which you move through the stages of solving your performance problem, or making a performance improvement.

Another way of thinking about DMAIC is in terms of the scientific method, which requires data and measurement—and repeated confirmation—as the basis of knowledge. In the world of process improvement, DMAIC is like the scientific method: its structure requires that you have the data and confirmation you need to proceed with improving your processes.

Say you have to go to the doctor:

The Least You Need to Know