Data will always show variation. One of the key questions is whether the variation is normal for the process or is unexpected, indicating that something special or out of the ordinary is happening. A control chart can easily identify these types of variation. Variation that is normal or usual for the process is defined as being produced by common causes. For example, common causes of variation in driving to work are traffic lights and weather conditions. Variation that is unusual or unexpected is defined as being produced by special causes. In the driving to work example, special causes of variation in travel time might be a breakdown of the car or involvement in an accident. Special causes are a result of a specific change and are frequently associated with a chaotic problem, such as an accident.
Once it is determined whether the variation in a process is produced by common or special causes, the next key question is, “how can the process be improved?” Actions to address and improve common causes are quite different from those used for special causes of variation. If common causes are producing too much variation in the system, then improvement is required. Since common causes create the normal everyday variation in the system, then improving them will involve systemic changes. For example, improving the variation in travel time to work would require a major change: using different roads, departing at a different time, changing the mode of transportation to a motor bike, or a more drastic change, such as change of residence. However, if special causes are producing problems in the system, they will be specific events, such as an automobile breakdown. The short-term action to fix this problem is to get towed to a garage. The long-term action is to improve the maintenance of the car. Special causes are usually of this nature. An action must occur immediately to overcome the special cause, and long-term action must be taken to prevent the special cause from recurring. This assumes that the impact of the special cause is negative. This is not always the case and in fact, special causes can be positive. If the special cause is positive, then the question changes from “what’s wrong?” to “what’s right?” The next question is, “how do we do it right all the time?”
Since the actions taken to address common and special causes are so different, it is essential to identify them correctly. One of the key purposes of a control chart is to discriminate between common and special causes so appropriate action can be taken. If control charts are not used, two kinds of mistakes can be made: the assumption that a special cause is occurring when only normal variation is present, or the assumption that the process or system is operating normally when something special is occurring. These mistakes can also be thought of as “overcontrolling” or “undercontrolling” the process. To understand this, consider someone learning to drive. Every lane change is traumatic; a car turning ahead is a cause to hit the brakes. In fact, normal driving events seem unusual to a learner, who is therefore likely to “overcontrol.” The result is a jerky ride at best, a dangerous one at worst. At the other extreme, a new driver takes the car out in heavy rain and drives at the speed limit, applies the brakes as usual at a stop sign, and skids helplessly into the intersection. The driver fails to recognize unusual road conditions and, acting intuitively, “undercontrols.” Control charts do for a process or system what experience does for a new driver. They graphically inform the user of the difference between ordinary and unusual events, common versus special causes, so the best course of action can be taken.
A process without special causes that exhibits only common causes of variation, is considered to be “statistically stable.” When a process or system is statistically stable, the control chart becomes predictive. That is, if the system remains the same, data produced will vary “normally” between the control limits, and will have the same average as that shown on the control chart. This is a powerful aspect of control charts. The ability to assess process variation and system performance facilitates the prediction of future performance. When making improvements to a process, it is generally advisable to eliminate special causes to achieve a statistically stable process before addressing common causes. This usually results in the best use of resources and the best improvements.