Section 7.3. Six Sigma Structure and Design


7.3. Six Sigma Structure and Design

To understand the structure and design of Six Sigma, it's helpful to first understand the philosophy of Six Sigma. The philosophy behind Six Sigma could be summarized as "Deliver Quality." That capital Q in Quality is important. It implies a certain kind of quality, and that is what Six Sigma drives at, a very special definition of quality. Quality is not whatever happens to be the biggest, the strongest, the prettiest, the best, or the coolest. It is not what your organization says it is. It is not what your competition thinks it is. In the world of Six Sigma, quality is what your customer wants. That is all it is. The meaning of the word quality comes from that source and that source only. Nothing else matters. Everything else is irrelevant. GE calls this the Voice of the Customer (VOC).

7.3.1. The Voice of the Customer

You'll see VOC mentioned a lot in Six Sigma descriptions. Six Sigma springs from this idea. Jack Welch made this a popular mantra at GE. The Voice of the Customer means that you listen to the customers and give them what they want, what they need. Do they want light bulbs shaped like question marks? Not if we're listening. They want light bulbs you only have to change every seven years.

The concept of the Voice of the Customer makes perfect business sense. It's a perfectly logical basis for defining quality. Most consumers (customers, clients, call them what you will) want a product or a service that fills a need. But they don't want an ongoing relationship with a company. Paradoxically, free them from needing you and they will regularly return. "Brand loyalty" springs from a product (or service) that matches what a customer expects from it. I am spending time on this because many companies prefer to think that they know what their customers need. They think they have a bead on the business they are in. They know best. Maybe they've been in the business for 10 years. Maybe they have a "vision." Maybe they are driven to be the best. And so they think their customers will buy whatever they sell. But that's not the Six Sigma approach.

Six Sigma is all about making a commitment to delivering what the customers want. And if maybe the customers don't know, your job is to find out what they want. And once you know this, you then align your production processes to create what it is they want. When you produce that, you've built a quality product. That's what quality is in the realm of Six Sigma.

That's the starting point: build what your customer wants. The next steps into Six Sigma move us into the realm of quantitative analysis. Let's begin with a simple formula:

X=f (Y)

X is a function of Y.

That's a way to look at Six Sigma problems. Cause and effect. X is always a function of Y. Business is down (X) because our umbrellas ship late (Y), the polka-dot design is unattractive (Y), and they tend to leak in the rain (Y).

It's helpful to realize that we can't really control X. Business is down. That's X, and that's the result of a culmination of factors. We can't change the result by focusing on the result. It's a good thing to know, sure, but it's a summary at best. What we can control is the Y. We can pin down the folks in shipping. We can influence the designers. Six Sigma focuses in on the Y. With Six Sigma, once you've heard the Voice of the Customer, you can move forward by defining your own X=f (Y) equation.

Define X as your business goal. X may be "a light bulb you only have to change every seven years." It might be "sales of $100,000." It might be "98 percent on-time shipping." Then look at that goal and deconstruct it into its functional elements. Identify each element of your operations that may impact the goal. Then look at those elements. Study them. Turn a critical eye to them. Lock them down. Measure them. Evaluate them. And then, for those elements that are not getting you to the goal they way they should, change them. Improve them.

You change them by collecting and analyzing performance data.

Let's take this example: a bulb that lights for seven years. One of the Ys that can impact that X is the carbon coating of the filament. Our scientists tell us that a coating 5 microns thick will deliver the desired longevity, give or take .25 microns. But that's a lab number, that's ideal conditions. On the factory floorwith varying environmental conditionsour carbon-coating machines fluctuate more than a few microns. A few microns here, a few there, and our bulbs lose hours on hours of performance. So the idea is to find out two things.

First, discover the "real" performance of those machines under typical floor conditions. How close to 5 microns can they consistently hit? And so we measure the machines. We collect the data. We look at the data. If the data tells us that the variance is within our limits of performance, then that Y is OK. Let's focus our efforts on other Ys.

But if the performance varies too muchif the machines coat too many filaments with too-thick or too-thin carbon coatingswe better take a closer look. We should find out the second thing: the thing that is causing that variance. Until we get control of that, our goal of a light bulb that burns for seven years will only be a goal. Just a goal. We'll have no practical way of getting there.

7.3.2. Why Is It Six? Why Is It Sigma?

Six Sigma is all about the spread of variation in a set of data. In a normal distribution, data tends to spread out in a very predictable pattern. Most of the values fall around the middle. Some fall more or less to either side. If you plot the result, the figure will look like a bell. Let's move away from light bulbs. Think of another common example: people's height. If you measure the height of 100 18-year-old males, you'll end up with a range of heights. Some will measure 6'2". Some will measure 5'5". Some, 5'11½". Some 6' even. And so on and so on. But most of the 18 year olds will measure 5'9½". In theory, 18-year-old males can be any height. But in nature, the average height is 5'9½", and the natural distribution tapers off evenly on both sides of that number. It looks something like Figure 7-1.

Figure 7-1. The traditional bell curve shows a normal distribution of data. The "average" values fall in the middle and the less common values fall to either side of the center.


A chart such as that illustrated in Figure 7-1 is called a normal distribution. It has the general shape of a bell, and we know from statistics that's a normal way that data like that should fall.

That brings us to Six Sigma. Six Sigma predicts that when you run a process, the way the performance varies over time will dance up and down around the center line, the average linejust like the range of heights in nature. (There are two caveats here, but don't worry about them right now. I'll look at them later.) But here is the key with Six Sigma: it wants you to put techniques in place to control what numbers (what data points) are going to most influence the average.

The common understanding of achieving Six Sigma performance is that for every 1,000,000 data points, only 3.4 will deviate from either side of the average. In a grossly exaggerated example, that might look more like Figure 7-2.

Figure 7-2. Here we see a very controlled sample set. We have been able to get most of the values to fall into the center, with very few falling to either side.


In Figure 7-2, we see hardly any variation. Everything is grouped right at the middle. In general, that's not a bad understanding. But the technical explanation is better, and it sheds more light on the purpose and design of Six Sigma. Let's get at this by looking at the name Six Sigma.

Sigma means the same thing as standard deviation. Standard deviation (SD) is a well-founded measure of the range of variation from the average for a group of measurements. In any set of data, 68 percent of all the measurements will fall within one standard deviation of the average. 95 percent of all the measurements will fall within two standard deviations of the average. By the time you're out to six standard deviationssix sigmayou've accounted for 99.9997 percent of the data. Practically nothing is out of those bounds.

At first blush, that might seem like the opposite of what Six Sigma promotes, conformance to the center. But now here's the push: Six Sigma is about good numbers and bad numbers. Good numbers are measures of performance you deem to be acceptable: the numbers you want to hit, the number range in which you want your process to perform. If you're an inventor out to clone The Average Male, you might decide that "good" heights for the clones would fall only between 5'9¼" and 5'9¾". Any other height would be deemed a failure. And so if you were to develop a cloning process performing at six sigma, you would find that 99.9997 percent of the 18-year-old males you turned out would stand between 5'9¼" and 5'9¾" tall. That's a very predictable process. That's a process under control. For every 1,000,000 transactions, that process would turn out only 3.2 defects, taller or shorter males.

So Six Sigma is about process control. The more you are able to control a process, the better you will be able to make it hit the performance numbers you want.

7.3.3. Forget Six. Forget Sigma.

Six Sigma is a program that works best when it uses hard data as the foundation for process improvement. That's why one of the general interpretations of this program is that it is heavy on statistics. So far, we've looked at a few common Six Sigma concepts: Voice of the Customer and X=f (Y). Here's another one: DPMO.

DPMO is Defects Per Million Opportunities for defects. When you are building a product, you want your production processes to be predictable. You want to know how many microns of carbon coating they are going to lay onto a filament. No process is perfect. No process operates without variance. One of the things you need to establish with Six Sigma is the number of process variations.

Think of a simple example: catching a baseball. The outcome is either/or. You catch the ball, that's success. You don'tyou drop or miss the ballthat's a defect. If you want to measure how good a catcher is, you have to measure only two things: the number of throws and the number of catches. You could generate a pretty good sigma rating by throwing the ball to the catcher a million times and then analyzing the outcome.

To get a valid statistical indicator of the reliability of this process (the talent of the catcher), you have to repeat the transaction over and over. Measure and measure. When you have a process that achieves statistical six sigma, you can pretty much guarantee that you'll have only 3.4 defects for every million transactions. That's 3.4 misses for every million throws. Whatever you are doing, it is so controlled, so streamlined, so proven that the outcome is a safe bet.

Sigma (statistical standard deviation) is used as a measure of process capability. In the realm of Six Sigma, you can measure your processes and then, through analysis, generate a performance sigma for each one. But most companieseven machine-driven companieshave not achieved six sigma in their processes. And they probably don't need to. Even if they have stringent quality goals, they can probably get there without getting to the 3.2 DPMO goal.

Six Sigma, then, is not about achieving six sigma. And so the "six" in Six Sigma is not a mandate. And generating process sigmas is not an absolute requirement. This is often misunderstood. Six Sigma is about putting in place the tools you need to control your processes to the point that they meet customer expectations. That's all. In your business, you might find four sigma is fine. With three sigma, you're hitting the mark 93 percent of the time. If you're in the pizza delivery business, that may be OK if you used to be able to make on-time delivery only 80 percent of the time. So an essential part of any successful Six Sigma program is defining what process sigma you should strive for in order to be successful. Decidein the interest of quality, in the interest of the customer, in the interest of your business goalswhat level of certitude is practical for you and your business teams.

7.3.4. Six Sigma Methodologies

Six Sigma employs two basic methodologies to problem solving. The first is termed DMAIC. DMAIC is used to improve existing processes in an organization. The other methodology is DFSS. It is used when you want to design a new process and introduce it into an organization in a way that supports Six Sigma management techniques.

DMAIC is the one that gets the most press. There are five basic steps in the methodology: define, measure, analyze, improve, control. DMAIC is used to improve and increase the efficiency and reliability of processes that exist in an organization. It is a process improvement methodology that employs incremental process improvement using Six Sigma techniques.

DFSS stands for Design for Six Sigma. It is also sometimes referred to as DMADV. This methodology also has five steps: define, measure, analyze, design, verify.

DFSS is used when an organization wants to design and produce new products in a timely, cost-effective manner to meet exact customer needs. It is a business development methodology. The core steps, DMADV, are used to create reliable processes in an organization that does not have processes, or when an organization must discard a deeply faulted process. DFSS is a process design approach.

Between DMAIC and DFSS, DMAIC is probably the one used by most people most often when implementing a Six Sigma project. The two are strongly similar, so I will focus on DMAIC for this chapter.




Process Improvement Essentials
Process Improvement Essentials: CMMI, Six SIGMA, and ISO 9001
ISBN: 0596102178
EAN: 2147483647
Year: 2006
Pages: 116

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