Six Sigma was developed as an improvement approach at Motorola in the late 1970s to deal with nagging quality problems. Sigma is the lowercase Greek letter for the letter s and is written as “σ.” Statisticians use it as a symbol for a standard deviation. As σ gets larger, it signals greater variability in a distribu-tion and lowers the probability (compared to a smaller σ) of guessing where the next score added to the distribution will fall. In almost all facets of business and life, you want σ to be small, which means that all the scores in the distri-bution are closer to the average of the distridistri-bution and thus easier to predict.
For example, a critical characteristic in a pair of jeans is the waist measure-ment. If a customer buys a pair of jeans with a waist size of 34 inches, it is important to customer satisfaction to ensure that all of the jeans with a 34-inch waist label have a waste circumference very close to 34 inches. If not, many customers will be unhappy.
Let’s say that a customer who buys a pair of jeans with a 34-inch waist
This sets the specification limits at 33½ and 34½ inches. Although we would like to have all of the 34-inch-waist jeans be exactly 34 inches, this is almost impossible. Because of normal variation, every pair of jeans will vary slightly from 34 inches. Therefore, we will (in this example) settle for having actual waste measurements between 33½ and 34½ inches. For this to be true for 99.9% of jeans (meaning that only one pair in 1000 has a waist measurement outside the specification limits and thus is likely to be noticed as “bad” by a typical customer), the standard deviation for this distribution of jeans would be about 0.15 inches. This means that the average distance between any one waist size in the production run and the mean of 34 inches is 0.15 inches.
Motorola arbitrarily set the model for outstanding quality at six sigma (6σ) because it was a quality level almost unattainable at the time and thereby stood as a worthy performance goal for a world-class organization. Another reason is that the term six sigma, with its alliteration, sounds better than five sigma or seven sigma. A six sigma process has a distance of six sigmas between the mean of the process and the closest specification limit. This means that there is very little chance that normal processes will produce a product that falls outside the specification limits. In terms of the jeans with the 34-inch waist, a production process that produced a six sigma waist measurement output distri-bution would result in no more than about one pair of 34-inch-waist jeans in a million with a waist circumference outside the specification limit for that size.
The σ for this level of quality would be 0.08 inches.
As first developed at Motorola, Six Sigma was heavy on process mea-surement, analysis, and improvement using statistical process control (SPC) methods, design of experiments, and general problem-solving tools to reduce variability in cell phone components, among other things. The problem-solving tools use many of the kaizen tools reviewed in Chapter 7. Six Sigma uses statistical thinking and techniques to attack specific problems created by unknown causes of variation. DMAIC (define, measure, analyze, improve, control) is one of the methods for organizing thoughts about process improve-ment. Six Sigma thinking posits that a process is impacted by known and unknown causes of variance. In the jeans example, known causes of variance would include the tension on cutting and sewing machines, sharpness of cut-ters, thickness of material, and so on. If all the known causes are controlled, waist measurements will be less variable, unless unknown causes of variation arise. Unknown causes of variance are those that occur without warning. In the jeans example, this would include such things as unexpected variability in the thickness of the material or a poorly trained operator running a machine.
Lean and kaizen methods assume that a significant problem in any pro-cess is waste, because waste is everywhere. These methods attack waste with comparatively little in-depth statistical analysis because waste can usually be easily found. The emphasis of Six Sigma is on eliminating a problem through rigorous process definition, metric development and measurement, process capability studies, and root cause analysis, followed by the installation of cess improvements. The goal is to achieve (or begin to achieve) dramatic pro-cess improvements with an eventual six sigma (or better) objective.
Six Sigma Black Belts (SSBBs) are the general practitioners of Six Sigma.
They are trained in both the theory and practice of using statistical thinking and problem-solving methods to fix a process or problem. Six Sigma train-ing typically involves two to four weeks of classroom traintrain-ing separated by a number of weeks in the field during which the trainee works on a Six Sigma project in the actual work environment. If the teacher reviews the project as successful, the student is awarded a black belt. Most black belts are awarded by organizations that have developed their own internal programs, so there is a broad range of black belt skills and experience, depending on the rigor of the training.
Six Sigma Master Black Belts (SSMBBs) are trained in both typical black belt skills and the “soft” people and project management challenges of organiz-ing and runnorganiz-ing complex, cross-functional problem-solvorganiz-ing and/or improve-ment projects. SSMBBs typically lead improveimprove-ment projects in a business area or unit. They work with business unit or site champions (executive-level spon-sors) to select and direct projects in a portion of the organization. In a very large organization (with many locations), there is often a full-time SSMBB at headquarters who helps design large projects and provides technical assistance to SSBBs at various locations as required. In smaller organizations, an execu-tive with either an SSBB or an SSMBB usually provides such assistance on a part-time basis. Many organizations also have Green Belts and Yellow Belts.
Yellow Belts receive a day or two of training in problem-solving methods such as cause-and-effect (C&E) diagrams, Pareto charts, and so on. Green Belt training can last anywhere from a week to several weeks and might include SPC charting and capability studies, as well as problem-solving methods.
Typically, a Six Sigma project designed and directed by an SSMBB con-sists of several individual problem-solving and process control endeavors organized as one effort (e.g., to fix the quality problem in the paint depart-ment). The desired outcome may be lowered inventory, an in-control (statisti-cally speaking) process, faster cycle times, lower costs, more machine uptime, and so on (or all of the above).
As you can imagine, once organizations started using lean and Six Sigma methods, a great deal of cross-pollination occurred. As a result, few Six Sigma programs do not have some lean DNA in them, and few lean programs have not absorbed some Six Sigma DNA. The challenge is that if all you know is the name of an organization’s initiative, you don’t know what tools and meth-ods are actually being used.