Continuous improvement has always been an implicit part of being agile and with the growing awareness of work by Deming , amongst others, plus the increasing popularity of Lean thinking, it is rightly becoming the centre of attention. And yet, I suspect much of the continuous improvement that happens may or may not be actual improvement. I wonder if most of it proves to be negligible in the grand scheme of things.

When thinking about continuous improvement it's important to understand the work as a system. Systems exist within other systems so decide where to define the boundaries. At Energized Work we work in product streams. Each product stream is a system of production geared to the market and includes the customers. Most people are probably aware of Deming's Plan-Do-Check-Act cycle. But in many cases it's probably used only partially. Mostly change is planned without defining measurable expectations for the outcome. Almost never is the outcome checked against those expectations and rarely is the outcome understood in terms of its causes. The vital step of checking is often missed, and planning and doing without checking is just decision-making that’s not validated. Checking reveals all kinds of things we should know about and it helps us understand and remove root causes of problems. But without a way to measure the impact of changes on the system we can only rely on a qualitative assessment as to whether an improvement was achieved. Qualitative assessment is useful but having data allows us to conduct analysis and gain better insight. Measuring the impact of changes using quantitative instrumentation provides us with knowledge that enables us to learn faster and more effectively. Planning decisions are transformed into experiments that help us determine the optimal operating conditions of the system.

Systems exhibit both exceptional and routine variation. When using PDCA listen to the ' voice of the system ' ( Wheeler ’s voice of the process ) because it defines what can be obtained from the system. Statistical process control charts can help understand the system's behavior and identify signals amongst the noise.

Exceptional (special cause) variation

Exceptional variation occurs beyond the natural limits and signals special causes , i.e. things outside the system that are significant enough to skew its output. It indicates dominant cause-and-effect relationships that affect the system that are not being controlled. According to Shewhart 's definition, with the presence of such exceptional variation the system is unpredictable. The system should not be changed to accommodate special causes. Rather the special causes should be eliminated to make the system predictable. Get timely data, react quickly to the signals and conduct root cause analysis to identify the causes. Then take immediate and effective action to remove the causes forever, which may involve making changes to the system (or more likely to the system outside the system :).

The first chart below shows a single data point beyond a natural limit signaling a special cause. The second chart isn't a signal as such but when 3 out of 4 consecutive data points are closer to one of the natural limits than they are to the average it may suggest a possible special cause. Certainly it warrants a closer look.

Joiner proposes additional tests for special causes hidden within the natural limits. These are shown in the last three charts: 8 successive data points on the same side of the average may indicate the system has been 'bumped'; 14 or more data points alternating up and down may indicate the system is moving back and forth with regularity (e.g. perhaps due to on and off-shore teams working opposing hours); 6 or more points in a row steadily rising or falling suggests the emergence of a trend (although Joiner says you seldom detect a special cause with this test).


Signals of possible special cause variations
Originally uploaded by energizr

Routine (common cause) variation

Routine variation occurs within the natural limits over a reasonably long period of time, appears well behaved and is entirely due to common causes that typically reside deep within the system. In this situation the system is predictable within those limits and, unless something is changed, the system continues to behave this way. A predictable system can only be improved by changing the actual system in some fundamental manner, e.g. by introducing new process settings or a new technology or new ways to do the work. Whether to act on common causes of variation is a judgment call. Although the system is stable it may be worthwhile reducing common cause variation to improve predictability even further and increase what the system can deliver. Unfortunately, unlike special causes, there are no obvious signals for common causes of variation. Therefore to reduce its effects run small experiments using PDCA to pinpoint the common causes and then develop changes in the system to counteract them.