In This Chapter
► Understanding the fundamental principles that underlie Six Sigma ^ Mastering the basic equation of Six Sigma: Y = f(X) + I:
^ Knowing that all outcomes are determined by inputs and how they are processed ^ Recognizing that effective control requires understanding and managing variation
Seeing that you have to measure a process before you can manage it ^ Becoming aware of the power of "leverage"
Eneath all the statistical analyses, the equations of probability, the
^/charts and the experiments; below all the projects and plans, the tools and technologies; and beyond the colored belts, the catchy phrases, and
Dizzying arrays of terms lie several fundamental principles that beget the
Whole Six Sigma methodology.
Like all grand constructions, Six Sigma sits upon a solid foundation. In this
Chapter, you discover five basic principles. And in doing so, you will begin to
Think the Six Sigma Way.
All of Six Sigma begins with one general-purpose equation that shouldn’t intimidate even the least mathematically inclined, because of its elegant simplicity. This equation is Y = f(X) + z, Where

F*Y Is the outcome, the result you desire or need.
VX Represents the inputs, factors, or pieces that are needed to create the outcome. You can have several Xs.
VF Is the Function, The way or process by which the inputs are transformed
Into the outcome.
V IЈ is the presence of error, the uncertainty in depending upon the Xs and the transformation function to actually create the desired outcome.
This expression is called the Breakthrough equation. See Figure 2-1.
In other words, a certain set of inputs is transformed by some function (or process) into an output. The YResults from, or is a function of, the Xs. To determine a desired outcome, you apply a transformation process or function F On the inputs.
You make a loaf of bread by taking flour, yeast, salt, and the other ingredients and transforming them through mixing and baking into a desired outcome. The ingredients are the Xs, the mixing and the baking are the transformation process function F, And the resulting yummy loaf of bread is the Y.
Sound simple enough? Almost. In the real world, no matter how hard you try, there is also a degree of uncertainty or variation in the outcome. There is always some degree of uncertainty as to how well your actions produce the
Desired result.
Consider the loaf of bread. What if you used too little yeast, or if the oven wasn’t quite hot enough? Suppose you were baking ten loaves; would they all come out exactly the same? Most likely, there would be some variation from loaf to loaf. In Six Sigma, the little error that creeps in and produces this
The Principle of Determinism

F<3>
Variation is represented by the Greek letter Epsilon, or i:. Sometimes the error is your fault (you measured incorrectly), and sometimes it’s just truly random
Error, but either way, you have variation.
Everything is deterministic. All outcomes are a result of some process or
Function acting on the inputs. And no matter how hard you try, there’s always a little error.
Determine the Cause
We’re a results-oriented society: "How’d it turn out?" "What finally happened?" "What was the final score?" "How long did it take?" "What’s the bottom line?" We’re always looking at the results. We’re practically obsessed on them. After
All, that’s the whole point of all the toil and trouble in the first place, right?
But Ho© Did the results happen? Why Did they happen? What Specifically
Caused them to happen? You want to know the answers to these questions, because if good things happen, you want to know how to make them happen
Again. And if bad things happen, you just as surely want to know how to prevent them next time.
Cause and effect
All outcomes are the result of the inputs and the process that acts on them, plus the error that creates variation. A process simply operates on the inputs to create the outcomes; that is, it’s the fundamental action in changing from
One condition to another, in making any improvement, in adding value for yourself, your customers, and your company.
Inputs are transformed — by way of a process — into their outputs. This is how change happens. Therefore, when you understand the process, you can leverage the way the inputs are combined to produce the outcome.
Look behind every result (output) and examine the inputs, the process, and
The error that combined to produce it. Seek to understand what caused the
Outcome. When you know the cause, you can begin to put yourself in a position to control the outcome next time — and again and again in the future. Understanding root cause is the first step to controlling outcomes.
Here’s a simple example: The guy gets the girl. That’s the outcome. How did that outcome happen? Well, as all razor companies would have you know, it is because he has a smooth, sexy face. What caused that? It’s the result of his
Shaving process and choice of ingredients. What caused that? Combining hot

Water, shaving cream, a mirror, a particular razor, and a steady hand to get a close shave. If anything’s wrong with the outcome — if the girl doesn’t like the guy’s scruffy face — you examine the ingredients and shaving process to determine the root cause of the problem.
Regardless of complexity, literally every result has one or more causes. The
More you can single out these causes and understand them, the better your
Opportunity to change it for the better. In Six Sigma speak, you’d say that knowing the Xs, The function F, And the uncertainty T: Means you know what caused the outcome Y. Cause and effect.
There is a better ©a§
Many companies and organizations want to improve their performance. They recognize intuitively that their performance results are the outcome of all their business and work processes. These processes are quite literally "the way" business is done. So, to improve the outcomes, the company has to change the way it does business. They want to change the processes — the function F— And combine the business inputs in a way that produces a better outcome. It’s not a wishful notion and not a trick for getting everyone to work harder. The call to change the way you do business is a legitimate search for a better way, because there is a better way. There’s always a better way.
^BC» Determinism Is the principle that you can create a desired outcome by config-Y~f\ Uring and controlling the inputs in a specific manner. In Six Sigma, you ana-( 1M1 ) lyze the inputs, the process, and the variation, and then implement the best possible combination to achieve your objective. By doing so, you’re exercising
Direct control over your environment, rather than allowing your environment to control you. You are deterministic, not reactionary, in your thinking.
Beu/are superstitious delusions (that is, correlation doesn’t impl§ causation)
The more you understand the cause-and-effect relationship between inputs and outcomes, the better you can predict, determine, and control the results. Conversely, the less you understand the relationships between inputs and actions, the more difficult it is to determine and control the results.
Don’t confuse coincidence, or Correlation, With cause and effect. Just because
Two events happen together does not mean that one has caused the other. The Latin term for such an error is called Non causa pro causa, Which means,
"non-cause for the cause." People often assume that events which are closely connected — either spatially or temporally — are somehow also connected
Causally.
Determinism is proactive
Determinism is about taking control. It’s the opposite of believing that events unfold by chance, apart from one’s influence. Surely, you
Don’t control all the variables and processes
That affect our lives. But just as surely, success is not just random luck.
If you think there really isn’t much you can do to impact the world, or even your local surroundings, you’re wrong. You can influence most everything around you in some way. But
Determinism is proactive. You must try to initiate change and believe the forces within your control are much greater than the ones outside your
Control.
Let go of excuses about why you can’t do something —justifying a tendency to be mediocre — and realize that you can do much more than you may think! Proactivity — the willingness to do something — will fuel your determinism.
Consider this exchange between Homer and Lisa Simpson:
Homer: Not a bear in sight. The "Bear Patrol" must be working like a charm!
Lisa: That’s specious reasoning, Dad.
Homer: Thank you, dear.
Lisa: By your logic, I could claim that this rock keeps tigers away. Homer: Oh, how does it work?
Lisa: It doesn’t work. Homer: Uh-huh.
Lisa: It’s just a stupid rock. But I don’t see any tigers around, do you? Homer: Lisa, I want to buy your rock.
These confusions of cause versus correlation are also known as Superstitious
Delusions. This is the football coach who always wears red socks, because he
Once won a very important game when he was wearing them. Did the socks cause his team to win? Did clothing determine the outcome of the game, or was it some other input or set of inputs?
Businesses are known to confuse correlation with causation. What about the
Company that ramps up capacity after a great month or quarter of sales, because they think this indicates an economic expansion? Only later do they
Discover that no expansion was forthcoming and that, instead, increased sales were correlated to a different factor.
Even if two variables are legitimately correlated, there is not necessarily any particular causal relationship between them. One may fluctuate in relation to the other due solely to chance (this is called Coincidence) Or, as is often the case, each is strongly affected by one or more other outside (or Confounding) Variables that you hadn’t thought of.
A causal connection probably does exist if you can establish all three of the following:
There is a reasonable explanation for cause and effect
^ The connection happens under different environmental conditions
You’ve ruled out potential outside confounding variables
One way to determine these conditions is through a designed experiment where groups strongly similar to one another in terms of the most important
Variables are exposed to different conditions and then analyzed to see whether the variable of interest performs differently. One or more groups is also held constant and not subjected to treatment(s) as a "control" group(s). You can
Find out more about this in Chapters 7 and 8.
Variation Happens
You’re playing a great round of golf. Everything’s dropping. All you have to dois win the last hole, and you’re going to beat all your buddies for the first
Time ever. You step up on the 18th tee, cast an eye down the fairway, draw
Your club back, uncork your winning swing, and. . . splat! Right in the drink! You lose. What happened?
Variation happened. Error happened. Whatever you did seventeen times in a row, you didn’t do it the last time. Dang! Consistency is a bugger. How do the
Pros do it?
Professional results, in anything, demand consistency. That means you get
The variation in your inputs — the Xs — and the uncertainty in the transfer function — that little T: — Under control.
In general, variation is undesirable, because it creates uncertainty in our ability to produce a desired outcome. In the world of business and organizational life, the goal is to produce a work product or deliver a service in a predictable manner. Variation in your results — whether in time, specification, quality, cost, or something else — is going to happen; it’s inevitable.
Some variation — within limits — might be okay. A little too much variation
Here or there, and you might have some repairs or rework on your hands. And too much altogether, and you’re either out of a job or out of business.

Stf^tyfc The characterization, measurement, analysis, and control of variation is a central theme of Six Sigma. Reduction of unwanted variation is the key to Fijt|) ) Achieving Six Sigma improvements. To jump right into the statistics of variation, go to Chapter 5.
What is Variation?
Very simply, Variation Is deviation from expectation. If you toss a coin, what’s your the chance of it landing on heads? Fifty percent. Therefore, if you toss a coin ten times, you expect to get five heads and five tails. This is your Expectation. Take out a coin and toss it ten times. What happened? Did you meet your expectation?
Try it again. What happened the second time? Try performing successive sets of ten coin tosses. Every time you repeat your ten coin tosses, the output — the number of heads and tails — varies. The extent to which your experience
Deviates from expectation is the extent to which variation has occurred.
When you closely measure any output Y, You find that it varies — always. Every output varies. This is important to understand: Again, every output varies. Each time you park your car, it doesn’t fit exactly in the same place between the parking lines. Every single product a company makes varies from every other single instance of the same product on every dimension, such as weight, size, durability, and so on. Every time you call a company for
Help as a customer, you get a different level of service and you leave the call
With a varying degree of satisfaction. Here’s one you can probably relate to:
Each and every person arrives to work at varying times each day. Get (the) mean
If you measure the occurrence of something many times, it’s going to vary
Around some average — or Mean — value. The mean is the central tendency of your process. Flip that coin enough times, and you’ll see that the mean will
Tend toward 50 percent heads, and 50 percent tails. Variation obsession
Anytime you measure the value of a given occurrence or event, it’s going to
Vary from the mean. A player’s batting average may be.302 for the season, but Friday night he went 2-for-5 and batted.400, nearly 100 points above his average. And then Saturday he went 0-for-4. Why? The school bus usually arrives at the stop at 7:17, but today it came at 7:22; that’s five minutes later than normal. Yesterday it came early at 7:15, and the kids almost missed it. Why?
These are examples of variation — the variation of occurrence versus the mean. The size, trends, nature, causes, effects, and control of this variation
Are the undying obsession of Six Sigma. Nothing is more examined, or more addressed in Six Sigma than this.
Variation is a
Many parts have to fit together to make a product, like a cellphone. When engineers design the parts, they account for the fact that all parts will display some amount of variation as they are produced. Variation is the degree to which a part, product, service, or transaction differs from all others in the same class or category.
In the case of a phone, each class of parts, like the plastic casing, vary in size, weight, and even color. Just as the phone cases vary, so does the clear plastic display that covers the liquid crystal
Serious thing
Display. Then you have the many hinges, buttons, antenna, internal components, and so on. All these parts have to snap and fit together well if the phone is to perform its function to your satisfaction. In other words, you can only tolerate a certain amount of variation. A little too much variation and the phone won’t work properly. A little
More variation and it won’t work at all.
And we all know who’s going to end up with the bad phone, right?
Where does Variation come from?
Why does Every Output vary? Because all the input Xs vary, and because the transformation function F Also varies. So how do they vary? And what makes
Them vary?
All variation is caused by something. Remember cause and effect: If you’re
Going to control the outcome, you have to control the cause. Therefore, if you’re going to control variation, you’d better understand what’s causing it.
Causes of Variation: Common Versus special
Some variation is just natural; you can’t eliminate it. The natural forces of
Nature work to mix things up. It’s simply part of the normal course of events.
Recall the coin-toss example; the variation in the number of heads from set to
Set is perfectly normal. Or consider the variation in the time between waves at the beach. Or the variation in the height of trees in a forest. These are all
Examples of naturally-occurring variation.
Now consider a few examples in human systems. Think about the time each day when the mailman comes. Or how long it takes to process a credit card application. Or the actual number of tiny time pills in one of those cold capsules. They all vary. And the variation is a natural part of their system.
This type of variation is called Common-cause variation. You can act to reduce common cause variation, but you can’t eliminate it. It’s natural, and it’s part
Of Every System. It’s in there and it’s not going away!
The other type of variation is known as Special-cause variation. Special cause variation is completely different — it’s directly caused by something special. If the mailman usually comes at about 11:30 each day, but he gets a flat tire and doesn’t come until noon, that’s a special cause of variation. If it normally
Takes 15 minutes to process a credit card application, but the network connection went down, that’s a special cause. And if there are supposed to be
600 tiny time pills in your cold capsule, but the white acetaminophen filler
Tube jammed and you have 550, that’s special. These special causes are specific things you can identify and do something about.
With Six Sigma, you spend particular effort to identify the difference between
Common-cause and special-cause variation, because they’re so different, and because you go to special effort to understand which type is causing the variation and how it’s affecting the outcome.
We’re adrift: Short-term and long-term Variation
Another important characteristic of variation is the way in which it changes
Over time. There are short-term variations and long-term variations. The difference is important.
Here’s an example: "That mailman used to come at 11:30, give or take a few minutes, but lately he’s been coming later and later, and now it seems he’s here closer to 12:15, which is really annoying because we’re at lunch and he has to leave the packages out in the rain." In this example, the short-term variation of a few minutes was inconsequential and well within our tolerance level, but when the mean time of arrival experienced a long-term variation (perhaps caused by a seasonal shift in weather or the holidays), drifting out by 45 minutes, there was a problem.
Getting Variation right is eVergthing
In general, it’s best to work on reducing special-cause variation before trying to reduce common-cause variation. The reason is because when you have special-cause variation, the process is not stable or predictable, and you can’t be sure of what is happening. But after you’ve taken the special-cause variation out of a system or process, you can then improve its common cause variability.
For example, if a coffee house first eliminates the special employee-to -
Employee differences in making a cup of coffee, it can then effectively work on improving the inherent, common-cause quality of the coffee itself. If, however, the coffee house tries to fix the inherent quality of the coffee first, the
Special employee-to-employee differences will cloud the situation, blocking
All efforts to decipher the what’s really going on.
The goal is to control variation, understand it, and minimize its impact, while
Accepting that it is part of everyday life and a part of every organization. Just like you can understand and characterize the relationship between Xs and Y you can characterize variation and error in the ability to produce desired outcomes consistently over time. This provides the foundation and framework for implementing real changes in the way you do what you do — changes that have the greatest probability of yielding positive results.
Thou Shalt Measure
In 1891, a British scientist named William Thompson, also called Lord Kelvin, said, "When you can measure what you are speaking about, and express it in
Numbers, you know something about it. But when you cannot express it in
Numbers, your knowledge is of a meager and unsatisfactory kind." Some
Wisdom is timeless, and the principle of measurement is one of the fundamental tenets of Six Sigma.
Lord Kelvin continued by saying that your opinions and ideas may be the beginning of knowledge, but they have "scarcely, in your thoughts, advanced to the state of science." Until you include measurement and numbers in your
Knowledge, you’re bound to the world of gut-feel, guessing, and marginal improvement.
You may work very hard, and even bring significant resources to a performance problem or improvement goal, but without measuring your Ys And Xs, your ability to improve will only be, "meager and unsatisfactory."
At first, it may seem impossible to measure many of your inputs and outputs. While it may be easy at first to rationalize yourself into believing some things
Just aren’t measurable, it’s going to be much more difficult in the long term to
Try and achieve your goals without the data that can help you.
Mind your l/s and Xs
Measurement is the practice of collecting the data that relates to the inputs (Xs) and the given outcome (Y) That results from your process function F. Measurement is what enables you to gain a quantitative grasp on the characteristics of your various inputs and how they relate to your desired outcome.
Measuring the inputs is what gives you the profile of the way your process is
Playing out relative to a goal or objective. Measurement begins with the YS, and then extends to the XS to understand the causes.

For example, you’d probably love to have $1,000 in your wallet (the output Y). To measure how they’re doing on this objective, a person could open his wallet each day and count how much money is there. He’d probably discover that his performance towards this objective is well below what he wanted.
The person could then analyze the situation and discover that the amount of
Money in his wallet is a function of how much money he earns, how much is taken out in taxes, and how much he spends on necessities. These are the Xs.
The person can’t affect the amount of money in his wallet directly. He has to do something to the identified Xs to make Y Change. To affect a change in the
Output Y, The person would start to measure and control the performance of the causal XS (perhaps earn more and spend less).
Many people never get past the Y. They watch it, like the money in the wallet, hoping that it will change simply by measuring it. It’s easy to be guilty of such Y-dominated thinking and measurement. Consider the company that continues to work harder and force productivity in an attempt to improve results (the Y), Without quantitatively investigating the contributing factors to success
(the XS of scrap, excess inventory, poor quality, and so on). This approach
Has a tendency to self destruct in its blind push toward a goal.
The answer begins ©ith the data
It’s easy to understand how to measure certain input and output variables because they are, by their very nature, accommodating of such measurement.
Examples of this include number of calories, your weight, and the time it takes to complete a run or walk, or in business, the time it takes to complete
A certain job, the number of days that transpire between a customer order, the delivery of the product, and so on. These are all numerically quantifiable measures.
Such measurement-friendly events, processes, variables, and transactions are well supported by certain measurement tools, like a bathroom scale, software, a spreadsheet, a clock, and so on. Using various quantitative scales like width, length, time, rigidity, and density, it’s possible to quantify the behavior of
Many YS and XS, and their relationships.
Other YS and XS, however, are not so easy to measure, because they’re not as
Easily quantified, or because the time, cost, and effort involved in doing so is extreme. How, for example, do you measure customer satisfaction — or a customer’s opinions?
In these cases, measurement instruments have to be specifically designed,
Like a survey question that ranks responses. When such instruments are developed and employed, you can make otherwise qualitative data much more quantitative in nature.
The bottom line on measurement
Taking measurements is a matter of using information and data to quantify
The relationship between inputs, outputs, and error in a given system, process, or operating model.
Even when numbers or direct measurements aren’t available, there are ways to create them indirectly. In this sense, you take a deterministic approach to measurement: You don’t give in to the lack of data, but you find the data you
Need or create estimates in accordance with sound practice.
About 100 years ago, H. G. Wells said that, "statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write." We are now well into that day. Politicians don’t make a statement anymore
Without first assessing people’s opinions through polls. Businesses don’t make decisions without first slicing and dicing the data. Facts and numbers are everything.
Measuring your Ys and Xs is your first step toward greater efficiency and effectiveness. It’s your first step toward citizenship in the Six Sigma world of
Data-oriented thinking.
The Power of LeVerage
If you’ve ever tried to move a huge rock or boulder, or even your washing machine, you can appreciate the meaning of Leverage. While you might lift and pull on it with all your strength, the boulder won’t budge. But if you use a long metal pole and an object for a fulcrum, you maximize the force of your limited
Strength. You use leverage to move the rock and accomplish your goal.
Life is like that: you have to expend a little effort to find the leverage. When you do, it catapults you over your problems and through the obstacles that
Stand between you and your goal. In Six Sigma terms, leverage is the ability to apply the critical few Xs that have the greatest impact on your desired Y.
The vast majority of leverage, or impact power, in creating any desired outcome, comes from a surprisingly small number of contributors. This is true for the simplest of goals as well as for the most complex systems. Typically, only a few select variables determine the quality of a given outcome! It all comes down to finding those critical few that give you the leverage. These vital few will enable you to move the "boulders" in your life, your process, or
Your organization.

L,\NG/
The "Vital fe©" Versus the "triVial many"
The law of the "vital few versus the trivial many" comes from the work of early 20th century Italian sociologist and economist Vilfredo Pareto. You may also know this law as the 80-20 rule, Where 20 percent of the inputs in any
System account for 80 percent of the influence on that system.
In his dedication to exploring the nature of individual and social action, Pareto determined mathematically that, while a great number of factors are connected to a given outcome, only a few carry the weight to change that
Outcome in a significant way.
In a process, a few key variables are the cause of most performance problems
Or defects. This principle holds true even when you analyze the impact of dozens upon dozens of variables involved in complicated assemblies and sub-assemblies with hundreds of separate parts. When you look for leverage
In business, you search for the minority of variables that provide the majority
Of power in solving problems in manufacturing, assembly, distribution, procurement, accounting, finance, customer service, and so on.
While businesses often employ sophisticated statistical tools to find leverage,
You may or may not need such tools for finding leverage as individuals. The
Key is to know with certainty that, whatever your goal or situation, leverage does exists; some factors in a given situation are more powerful than others.
Leverage may not exist where you think it does; the obvious is not always the answer. Look closely, apply tests, and challenge your assumptions to find the
Sources of leverage.
Note also that the factors that represent leverage in one situation may not
Represent leverage in another similar situation. Each process or problem has its own unique dynamics and interactions.
The high road
There are more factors, contingencies, and dynamics to manage than possible when trying to break through to new levels of performance and success. The natural tendency is to try and manage and control every detail, but this is a slippery slope. The trivial many will bury you in a pile of unnecessary cost, trouble, worries, wasted energy, and valueless action.
No one, and no company, has the luxury or reason to manage all the details. Instead, the right path is to manage only those that are critical to producing the outcomes you desire.
Focus on the inputs that really matter. All the rest, leave alone unless they become significant.
After you determine that a factor is insignificant, don’t waste time and energy putting attention on it. This spreads your energy too thin and minimizes your ability to create positive change. The key is to engage in a filtering process by which you weed out the many trivial variables that compete for your time but offer no real advantage. By doing so, you disable the force of confusion and achieve clarity of focus around your efforts to resolve an issue, solve a problem, or reach a goal.
Finding the better ©ay
The way you find the critical few is to follow a structured process for defining, measuring, and analyzing all the cause-and-effect relationships. In Six Sigma, structured and powerful tools help you brainstorm the possible causes (Xs) of performance problems and operational issues. Collect performance data that reflects the behavior of the many Xs, as well as the behavior of your Y Of concern. Analytical tools enlighten you as to which XS are the critical ones, and which are the trivial.
The results of these operations tell you — and show you clearly — which Xs you need to focus on to impact your Y. They also show you which Xs are Out of control, Or behaving too erratically. Such variation is the primary cause of
Problems when it comes to performance predictability.
Having your baseline of measurements and understanding numerically
How your Xs interact and impact your Y, You can then implement countermeasures — different X-related actions that ultimately improve your Y.
Using your same data framework, you can take new measurements to test theimpact of your countermeasures. You have established a data-oriented
Baseline against which to prove that the new way of doing business is truly
Abetter way.
You have validated that the critical few Xs are truly the critical few. This is the essence of the Six Sigma principle of finding the leverage.















