Editor’s note: The first installment of Scott Cornish’s series on process improvement appeared in the July issue of Newspapers & Technology. In this, the fourth installment, Cornish talks about the foundations supporting Six Sigma process improvement.

Last month we covered a few necessary points and topics before starting a process improvement project.

A major component of process improvement is the Six Sigma approach, a collection of tools - dubbed DMAIC for define, measure, analyze, improve and control - aimed at reducing operational glitches and variations.

Before we jump into that, I want to share a brief history of the U.S. quality movement in the mid-1980s that lead to Six Sigma’s entrance as a process control determinant.

 I joined the American Society for Quality (ASQ) in 1984. At that time, the “Quality Circle” movement had nearly run its course and use of statistical process control, or SPC, was rising.

The main quality “gurus” of the day were Dr. W. Edwards Deming, Dr. Joseph M. Juran, Armand V. Feigenbaum and Philip Crosby. Each approached quality improvement from a different perspective because of his background and experience. And each had a formal philosophy, from Deming’s “Fourteen Points” of management to Crosby’s “Zero Defects.” There were differences and, in fact, contradictions between each guru’s philosophies. At times, it was very confusing.

Sharpen definitions

Fortunately for quality management adherents everywhere, two separate developments occurred that began to sharpen the definition of quality improvement.

The first was the International Organization for Standardization, which in 1987 issued the ISO-9000 standards, a set of formal, international specs governing quality assurance and management. The standards, general for all industries and dynamic, have been updated through the years.

Later that year, the U.S. Congress established the Malcolm Baldridge Quality Award. This is given annually to different categories of U.S. companies that successfully implemented quality management systems.

Almost from day one, both ISO-9000 and the Baldridge award were controversial. Some disparage one or the other as something companies can easily achieve if they want to spend enough money.

I don’t want to get into that debate. What’s important to note is that both, finally, put stakes in the ground for criteria that one must follow and adhere to in order to launch a quality improvement and management program.

In the following years, three other management approaches came on the scene that contributed to the quality movement: re-engineering, benchmarking and balanced scorecard. Each of these has its proponents and opponents. For our purposes though, they all focused on processes and/or better performance metrics.

Equally important, each approach laid a crucial brick in the foundation of what became known as Six Sigma. Some critics state that there’s really nothing new in Six Sigma. And from a purist perspective, he or she may be right. Many of the tools, techniques and concepts of Six Sigma were “borrowed” from one of the approaches noted above.

What was new with Six Sigma, and its DMAIC toolset, was an organized, formal body of knowledge, practiced by formally certified experts.

Basics of DMAIC

The first step of DMAIC is define. ASQ notes three areas at this stage: project scope, metrics and problem statement. I look at these three as necessary elements to ensure you get started on the right foot. Tools used can include cause and effect diagrams and Pareto charts.

Measure is the second step. Accurate measurement is crucial and as such, a number of tools are addressed here. These include process analysis and documentation, probability and statistics, collecting and summarizing data, properties and applications of probability distributions, measurement systems and analyzing process capability. As I noted in an earlier article, not all of these will be necessary or even appropriate for all process improvement projects.

The third step is analyze. At this stage, you will develop a deeper knowledge of the subject of the project. The tools used here focus on variation and how to study it.  ASQ lists two tools for this step: exploratory data analysis and hypothesis testing.

Improve step No.4. Tools used here include design of experiments (DOE), response surface methodology and evolutionary operations. These tools are very powerful but are probably more advanced than the typical project you will encounter, at least initially. DOE, for example, requires a carefully constructed series of experiments or tests, which would necessitate a significant commitment of resources.

The final step is control. At this stage, you should have recommended and put in place a solution to the problem identified in the define step. But it is crucial that you put in place a system so that the problem does not recur. The ASQ’s list of tools at this step are statistical process control, advanced statistical process control, lean tools for control and measurement system re-analysis.

There are other options available, such as those recommended by the Certified Six Sigma Black Belt Book of Knowledge. But although we may selectively borrow some of these tools, such as project management and design for Six Sigma, these are more important for advanced projects.

We are going to keep it basic for now.

I’d like to end this month’s article with a table presented at a recent ASQ conference by Rutgers University Professor Rosa Oppenheim. In it, she compares results from some processes that operate at Three to Four Sigma (99 percent good) with the same processes at Six Sigma (99.9996 percent good):

Pretty eye-opening, right? These examples show the importance of very tight tolerances for some areas of our lives. But there are others where it would be too costly and inappropriate to employ these same tolerance levels. Consider: Are 3.4 defects per million opportunities appropriate when printing your newspaper? What would that cost? Moreover, would your clients and consumers pay for that level of quality? It’s something to think about.