Overview of the Control Chart Platform

A control chart is a graphical way to filter out routine variation in a process. Filtering out routine variation helps manufacturers and other businesses determine whether a process is stable and predictable. If the variation is more than routine, the process can be adjusted to create higher quality output at a lower cost.

All processes exhibit variation as the process is measured over time. There are two types of variation in process measurements:

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Routine or common-cause variation. Even measurements from a stable process exhibit these random ups and downs. When process measurements exhibit only common-cause variation, the measurements stay within expected limits.

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Abnormal or special-cause variation. Examples of special-cause variation include a change in the process mean, points above or below the control limits, or measurements that trend up or down. These changes can be caused by factors such as a broken tool or machine, equipment degradation, and changes to raw materials. A change or defect in the process is often identifiable by abnormal variation in the process measurements.

Control charts quantify the routine variation in a process, so that special causes can be identified. One way control charts filter out routine variation is by applying control limits. Control limits define the range of process measurements for a process that is exhibiting only routine variation. Measurements between the control limits indicate a stable and predictable process. Measurements outside the limits indicate a special cause, and action should be taken to restore the process to a state of control.

Control chart performance is dependent on the sampling scheme used. The sampling plan should be rational, that is, the subgroups are representative of the process. Rational subgrouping means that you sample from the process by selecting subgroups in such a way that special causes are more likely to occur between subgroups rather than within subgroups.

Shewhart control charts are broadly classified into control charts for variables and control charts for attributes. Control charts for variables include moving average and CUSUM charts. CUSUM charts are also a type of attribute chart. For details, see Moving Average Charts and the V-Mask CUSUM Control Charts topic.