All manufacturing and business processes contain many sources of variation. The differences may be so small as to be difficult to detect, but they are there. Variation is at the root of all waste. Less variation, less waste. To reduce variation, and effectively solve problems, its sources must first be understood as common cause vs special cause.
Common Cause
Common cause variation is a constant system of chance. The sources of common cause variation are many in number but small in size. A process characterized by common cause variation is in a state of statistical control: its output is stable and predictable. The magnitude of common cause variation can be measured using control charts. The sources of common cause variation can be identified and quantified using Design of Experiments, regression analysis, and other statistical methods. The reduction of common cause variation requires actions on the system. Example: the total of two die.
Special Cause
Special cause variation affects processes in disruptive and unpredictable ways. The sources of special cause variation are relatively few in number but are large in size. A process driven by special cause variation is neither stable nor predictable. Special causes can be detected using control charts through out-of-control signals. The elimination of special causes requires local action on the process. Although special causes account for less than 20% of total variation in most processes, they must be removed before common cause variation can effectively be reduced. Two examples: an untrained operator or parts from an unapproved supplier suddenly appearing one day and generating defects at an otherwise stable and predictable work station.
Statistical Control
Statistical control is not a natural state. All processes are under relentless attack from special causes. Confusing common cause vs special cause variation results in one of two mistakes:
(1) The first mistake is to assume variation to be a special cause when it is in fact common cause. The mistake of over adjustment leads to more variation and time wasted looking for a reason for a defect when there is no single assignable cause.
(2) The second mistake is to assume variation to be common cause when it is in fact special cause. The mistake of under adjustment is a lost opportunity to find and eliminate a special cause. Special causes are not always present so it is best to start looking for them right away before the trail grows cold…
To provide a rational means to make the distinction between common cause vs special cause variation, Walter Shewhart invented control charts circa 1930. W. Edwards Deming took them to Japan after World War II and planted the seeds for the quality revolution.
This distinction is not often obvious. A machine alarm and subsequent shutdown could be due to common cause variation. In the digital age, we have sensors on almost everything. Don’t just assume that alarm or defect is due to a special cause. Analyze data over time with a control chart.
Making the correct distinction between common cause vs special cause variation is a big part of my day job as a Professional Engineer. Visit my Operations Engineering page for methods and case studies.
