Just because a process is stable, or in statistical control, does not mean that its results are satisfactory. A process may be very consistent, day in and day out making items that are nowhere near specification limits. Or, as the Japanese have done so successfully, variation can be systematically reduced, even in stable processes, enabling a gradual tightening of specification limits, and an overall increase in product quality at lower cost.

Improving a stable process is somewhat more difficult than improving an unstable process because, by definition, a stable process has no special causes of variation that jump out at you, asking to be investigated. Instead, you are faced with the task of looking at all data about the process, not simply what made one point different from the others.

Common causes of variation often lie hidden within the system, and are sometimes assumed to be unavoidable. Yet it is very possible, and often very rewarding, to improve processes and reduce common cause variation. Experience had shown that, amongst the people in and around the process, there are enough ideas for improvements to make a significant impact, even on a sound process.

There are many different ways to search for and remove common causes. Probably the most well-known is experimentation, but you can also use stratification. Either of these methods may be helped by disaggregation of data. For more on these, choose one of the following:

Experimentation

Experimenting allows you to test a theory or hunch when you have little or no data available. The best guideline for experimentation with a process is the Plan-Do-Check-Act cycle. The PDCA cycle, described by Walter Shewhart and W. Edwards Deming, is essentially an iteration of the scientific method. The scientific method goes way back…Francis Bacon described it in the 1620's, but its roots reach all the way back to the Greek philosophers.

The PDCA cycle stresses experimentation and observation as the means of discovering truth:

• In the Planning stage, the problem is recognized and analyzed, and possible solutions formulated.
• In the Doing stage, the most likely or effective solution is implemented in a test site.
• The Check is used to compare results of the test solution and the original method to see if there are real improvements.
• Acting involves replacing the old method with the successful solution.

You can then return to the beginning of the cycle to explore other possible problems and strive for new levels of improvement.

The search for common causes is just one of the many arenas in which the PDCA cycle can be used. Most generally, it is used to guide overall process improvement, of which searching for common causes might be but a small part.

The PDCA cycle calls for creative thinking and analytic thinking, both essential to process improvement. Creative or divergent thinking encourages many ideas to be considered and new possibilities to be uncovered. Creativity is an important factor because it can break through paradigms and see beyond the current way of thinking about a process. But creativity must be tempered by analysis or convergent thinking that brings the scattered pieces back together in a workable form.

Stratification

Sometimes experimentation is not necessary, and common causes can be found using stratification of data. Stratifying data is essentially the separation of data into categories: what characteristics are shared or not shared? It often needs to be done iteratively – you stratify at one level, then within one of those categories you stratify again, and so on. If you start at the most general level of the information you have, only the most superficial answer may appear. If you stratify the data at different levels, you may begin to see links. It's like an address on a letter. At the most general level, an address leads you to a country, then to a state, then a ZIP code or city, then to street, then a particular house on the street, and finally to a particular person in the house.

By sorting data into multiple levels of groups with shared characteristics, you can better pinpoint the root cause of a problem. For example, in one 6th grade class, there seems to be a lot of students failing their spelling tests. As you look at the data, you notice that many more students fail the test given on Tuesday than the one given on Friday. When you investigate the Tuesday tests, you notice that most of the failures belong to kids involved with the basketball team. You then learn from the basketball coach that the team holds practice in the evenings on Monday, but immediately after school on Wednesday and Thursday. This means that, most likely, the high number of failures on Tuesdays is due to late practice the night before, leaving the kids less time to study. If you had not stratified the data, becoming more specific at each step, first by days of the week and then by basketball players and non-basketball players, you would never have discovered the common cause in this process.

Stratification can be made easier by using Pareto charts, bar charts, or pie charts, all charts that can display counts of things in different categories. Even the cause & effect diagram could be used to build a tree of branching characteristics, each one being stratified further and further until root causes are reached. In stratification, the characteristic used to separate the data is the "stratification variable." Categories can be composed of a single variable or can combine more than one variable as long as they refer to different characteristics. For example, a category could be "college students who have had their driver's license revoked for underage drinking." This category combine several variables: Are they a college student? Have they had their license revoked? For underage drinking?

Two common mistakes are made when stratifying data. First, it is easy to conclude too much from the stratification. Don't take small differences between category totals too seriously. Look for big differences instead, and try stratifying the data using different stratification variables. Secondly, people tend to jump to the conclusion that an irregularly patterned category is the cause of the problem. The category may provide us a clue as to where to look for the cause rather than being the cause itself.

Disaggregation

Either experimentation or stratification can sometimes be helped by disaggregating the process and viewing its components individually. Sometimes a problem in one part of the process gets covered up by another part of the process. By studying the components separately, a problem that exists in one but is covered up in the whole can rise to the surface.

Disaggregation is not about optimizing each piece at the expense of others. In disaggregation, the parts of the process that are being viewed separately must still be aligned toward the same shared goal and focused on serving the next step in the process. Disaggregation is more about bringing pieces into view rather than actually separating them, or seeing the forest and the trees. Searching for common causes through disaggregation relies heavily on regular meetings between managers of the different parts of the process so that the pieces can be discussed in the context of the whole system.