True enough, but not the whole story. The fact is that when I look at what my clients do with Lean Six Sigma, and review projects from students, I can see that they are, in fact, innovating. In Phase I, when companies begin Lean Six Sigma, it is usually viewed as an initiative and the first efforts focus on creating a culture where change is possible, organizing an infrastructure for change, training a cadre of part- and full-time change agents, and pursuing projects chosen to move the organization towards its vision. This sets the stage for innovation. The real transformation here is in the way people in the organization think, specifically:

• They are fact and data driven. Opinions are considered the source of hypotheses to be tested, not absolute truth. The change agents have the tools they need to rigorously test these hypotheses.
• They are customer focused and they know how to identify the voice of the customer. This gives them insights into customers needs that go well beyond what customers explicitly say their needs are.
• They think of organizations as processes as well as functions. They understand that functions exist to serve stakeholders and enable core processes.
• They understand variation differently than their untrained counterparts. They know that some variation demands an immediate response, but other variation requires system changes. They know how to tell one type of variation from the other.
• They think of results as stemming from systems rather than individuals.
• They know that outcomes–both wanted and unwanted–are caused, and they know how to drill down to these causes. I.e., they understand that processes are transfer functions that transform inputs into outputs.
• They understand the importance of focusing on the few critical to quality drivers, and how to identify them.
• They know how to organize people for change.

By design the time spent as a full-time change agent is limited. Black Belts serve their terms and return to the organization in other roles.  As time goes by these Lean Six Sigma change agents begin to change the organization’s DNA. Phase II occurs as the culture change takes hold and the change agents, now in key leadership positions, see the Lean Six Sigma approach as the best way to lead the organization towards its vision. They see that they can create new and innovative ways to serve their customers’ latent needs based on the intimate knowledge of the customer and the insights gained using Lean Six Sigma on a smaller scale. They better understand the organization’s capabilities based on experiences learned during the deployment of the initiative. Lean Six Sigma moves far beyond discrete improvement projects and becomes the  framework for leading the organization as a whole towards its vision.

Lean Six Sigma also teaches leaders a new way to lead. Their involvement in defining the organization’s core processes and enabling functions, identifying process owners, finding opportunities for improvement linked to their strategies, defining the drivers of these opportunities, selecting relevant metrics for the drivers, and linking the metrics to activities throughout the organization (including but not limited to Lean Six Sigma projects,) gives them a new way to get things done.

The combination of a new way of thinking, intimate knowledge of the customer, a culture that embraces and expects change, and a powerful new way to lead, makes it possible for the leadership to bring together disparate parts of their organization all focused on a single purpose: wowing the customer. In short, innovation. This is not the aforementioned clumsy and ill-advised attempt to measure the unmeasurable or to “manage the innovation process,” it is an inspired expansion of the scope of Lean Six Sigma from a purely operational improvement tool to a purposeful search for innovative improvement opportunities. It is the application of the core principles of Lean Six Sigma to the  problem of creating a resilient organization that not only responds quickly to changing customer needs and competitive pressures, but also improves the human condition by creating products and services never before conceived.

In summary, Lean Six Sigma becomes the springboard for continuous innovation. It’s a natural extension of the idea of continuous improvement.

Fig. 1-Large and Small Samples of Normally Distributed Data

One of the exercises I assign to students in my training involves creating two histograms from normally distributed random numbers. The results often look similar to those shown in figure 1. When I ask students to comment on their histograms I usually get comments about the averages, spread, and other statistical properties. However, that misses the point I’m trying to teach.

When we do Six Sigma we usually spend a lot of time mining historical data from databases. Sometimes the sample sizes are large, and sometimes they can be quite small. In fact, even large sample sizes can become small when we slice-and-dice them drilling down with various categories and sub-categories in search of CTQs. Statistical software will often automatically fit a normal curve to histograms created from these data. It’s often tempting to use the fitted curves to make an eyeball judgment about the normality of the data. Sometimes this is a good idea, and sometimes it isn’t. If the sample sizes are small, then the curve may not appear to fit the data very well simply because of small sample variation. Witness the top histogram in figure 1 for an example of a curve fitted to a histogram from a sample size of n=20. The histogram looks like a poor fit, but the p-value of a normality test tells us that the fit is pretty good anyway. So we’re probably safe assuming normality and acting accordingly.

The lower curve is fitted to a sample of n=500 data values. It appears to be a much better fit, and the p-value will back this conclusion. But what if the eyeballed curve fit and the p-value disagree?

Fig. 2-Decent Fit but Lousy P-value

Sometimes the fit of the curve is “close enough,” but the p-value will tell you that the fit is awful. Take a look at figure 2. The histogram suggests that the normal curve fits the data pretty well. There are many practical situations where you could use the normal distribution to make estimates and your estimates would be just fine. These are data on the time it takes to complete technical support calls. If you assume normality and you estimate costs or make a decision about process acceptability, your decisions will be essentially correct. However, the probability plot and AD goodness-of-fit statistic clearly show that the data are not normal and that the lack of fit is particularly poor in the tails (p < 0.005.) A closer examination shows that even in the tail areas the discrepancies are fractions of a percent. For example, the normal distribution estimates that 99.9% of all calls will take less than 35 minutes to complete, while the data show about 99.5%. Chances are these differences are of little or no practical importance.

The point is that in the business world we often need to make decisions and then get on to other, more urgent matters. The normal distribution is a handy device for getting quick estimates that are useful for such decisions. If your sample size is relatively large (say 200 or more) then you can go with the normality assumption if the fitted curve looks reasonably good. On the other hand, if you only have a small amount of data, you can still use the normality assumption if the histogram fit looks lousy, providing the p-value of the goodness-of-fit statistic says the normal curve is okay, i.e., if p > 0.05. The normality assumption is so useful that it’s worth using as a default, even if you bend the rules a bit.

Imagine the following scene. The boss rushes into the quality director’s office. He’s obviously distraught.

(Boss enters, walking quickly from stage right.)

Boss: “Jane, we’ve got a serious problem. Our biggest customer just called. Their assembly line is shut down because the last batch of XYZ-50′s that we shipped won’t fit into their assembly fixtures. What happened?”

(Jane, sitting at her desk, puts down her pen and looks up at her boss. She shakes her head in dismay.)

Jane: “I knew this would happen sooner or later, boss. The problem is that our customer requires us to provide a Cpk of 1.33 or higher. But the formula they make us use assumes normality, and the XYZ-50 has a skewed distribution. If we center the process to maximize Cpk, then the tail area extends beyond the specification limit .”

(Boss exits, stage right, shaking his head and wearing a puzzled expression.)

I fear that when the quality profession talks about process capability, this is how we sound to others. To many quality engineers and managers, process capability is a jumbled confusion of ideas expressed in jargon that only the anointed can understand. Let me try to clear the air on the subject.

Process capability is about one thing, and one thing only: quality. It answers the simple question, “Can you meet my requirements?” Ideally the customer would like a simple answer, yes or no. Unfortunately, this is not possible due to one or more of the following:

Inspection is not perfect; even 100-percent inspection won’t guarantee 100-percent quality. Explaining this becomes complicated.

All processes vary, and the variation must be analyzed using statistical methods that always predict at least an occasional failure. The statistics virtually always get complicated.

Measurement isn’t perfect, so even if a process did have zero variation, our measurements would still vary. This means that we might accidentally ship a defective item even if we measure it carefully. Not only that, our measurements of a particular item might be somewhat different from our customer’s measurements. Explaining how two trained people using the same type of instruments can check the same item and get different results can get complicated.

We or our customer might not properly understand the requirements. Human communication is always complicated.

Yet it’s really not complicated at all. In fact, the customer’s question can be answered easily, and the answer is: no. For all of the reasons listed, and many more, we cannot guarantee that we will always deliver a product or service that meets the customer’s requirements as understood by the customer.

So, now what? The best approach is also the most radical: Be honest. Tell customers about how many items they are likely to receive, on average, that will not meet the requirements. This cuts right to the heart of the matter. It tells customers what they want to know. It works for variables data and attributes data. If control charts are being used, the estimate can be obtained directly from the process average (for attributes data) or the process average and standard deviation (for variables data). The count can be adjusted to include sorting operations, inspection error, measurement error and all of the other factors that influence what the customer receives.

If our process is extremely good, we can tell the customer that, while we can’t guarantee perfection, we can provide quality in the near-perfect range. One good way of quantifying this is to use parts-per-million quality statements. For example: “Our return rate on this item is three returns per million items in service per year.” Most people can easily understand this statement. A customer ordering up to several thousand items will probably, and accurately, interpret this to mean “zero defects.”

If our process is less capable, stating the expected number of defective items that the customer will receive might result in a shock to both the employees and the customer. This may provide the incentive needed to improve quality.

High-volume production is another area where stating process capability as expected defectives can provide insights. A defect rate of 1/10 percent sounds pretty good. But a can line may produce in excess of 1,000 cans per minute, so a reject rate of 1/10 percent would result in the production of 1,440 defective cans per day. If the defect is major, say a leaking can that could damage many cases of product in a warehouse or truck, even a defective rate of one in a million might not be acceptable; it would result in several serious problems each week. For such processes, parts per billion quality may be required.

If a process is not in statistical control for unknown reasons, there is no way to state the process capability with any degree of precision. The best option is to tell the customer what the expected defectives will be (based on the historical data) and hope for the best.

The key to good customer relations is clear communication. The easiest way to get the point across is to tell the customer what level of product or service quality to expect, using plain language.

A common problem with SPC is that the world appears too complicated for a statistical approach to work. In complex electronics products, for example, circuit boards may have thousands of holes and microchips may have millions of transistors. Plotting control charts of each and every dimension is clearly not feasible. What can be done?

To answer this question, consider a simple product: the box in Figure 1. How many things could we measure on this box? It turns out, a great many. Length, width and height are obvious choices. But we could also measure the diagonals on all six sides, interior diagonals front-to-back and back-to-front, linear combinations of these measurements and a great many more. We could conceivably come up with dozens of measurements on this simple box.

But–and this is critical–we don’t need these measurements to control the box process. The “P” in SPC stands for process, not product. When we focus on the product, we lose sight of the fact that we’re not trying to control the product. Control of the box process may be a great deal more simple than controlling the product. And if we control the process properly, the product will take care of itself.

The statistical technique known as principle components analysis can help us determine just what is important and what is not. Most statistical software packages can perform PCA. To illustrate the approach, I measured an assortment of boxes (see figure 2). The measurements I obtained are shown (in inches) in Table 1.

When these data are crunched through PCA, we find that three principle components explain 99 percent of the variation in the data set: Component No. 1 explains 76.9 percent of the variation, component No. 2 explains 14.1 percent, and component No. 3 explains 8 percent. The PCA clearly shows that these three components are associated with A, B and C respectively. Thus, the “box process” can be characterized almost entirely by controlling these three characteristics. If we do that, the other dimensions will be OK, too.

When these data are crunched through PCA, we find that three principle components explain 99 percent of the variation in the data set: Component No. 1 explains 76.9 percent of the variation, component No. 2 explains 14.1 percent, and component No. 3 explains 8 percent. The PCA clearly shows that these three components are associated with A, B and C respectively. Thus, the “box process” can be characterized almost entirely by controlling these three characteristics. If we do that, the other dimensions will be OK, too.

An example of using this approach in the real world involves CNC machining. A defense plant machined parts for use in guided missiles. The parts were extremely complex, with thousands of holes, cutouts, etc. on each. However, when the data were analyzed using PCA, it was determined that four principle components accounted for nearly all of the process variation. Further study showed which measurements were correlated with each principle component.

From this, the engineers determined that, for all the apparent complexity, the machining process was, in fact, quite simple. The four principle components corresponded with the machining center’s four axes of movement: X, Y and Z movement of the bed, and the rotation of the table on which the parts were mounted. SPC could be accomplished by selecting those features most difficult to position in each axis of movement. Often, a single feature could measure more than one axis; for example, a hole furthest from the “home” position in both the X and Y axes. The result: One or two control charts suffice for the control of a process placing thousands of features.

Note that the features selected for SPC may be of little or no importance to the product itself. In fact, some parts were designed with “process control features” that were later removed from the part entirely. This makes sense when remembering that P stands for process, not product. If you keep that in mind, the complexity you face might just evaporate before your eyes.