Control Chart
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A control chart is a statistical tool that monitors process variation over time, distinguishing between common cause and special cause variation.
Definition
A control chart is a time-series graph used to monitor process performance and detect changes. It plots data points over time against statistically calculated control limits (typically ±3 standard deviations from the mean). When all points fall within the limits in a random pattern, the process is "in control"—variation is due to normal, inherent causes. Points outside the limits or non-random patterns signal "special causes" requiring investigation. Control charts are fundamental to Statistical Process Control (SPC) and the Control phase of DMAIC.
Examples
A machining process monitors shaft diameter hourly. The control chart shows UCL of 10.06mm and LCL of 9.94mm. When two consecutive points exceed 10.05mm, the team investigates and finds a worn cutting tool—catching the issue before producing out-of-spec parts.
Key Points
- Control limits are calculated from process data, not from specifications or targets
- Points outside limits or patterns (trends, runs, cycles) indicate special causes
- Different chart types exist for different data: X-bar/R for continuous, p-chart for proportions, c-chart for counts
- Process must be "in control" before capability can be assessed
Common Misconceptions
Control limits are the same as specification limits. Control limits show what the process IS doing; specification limits show what it SHOULD do. A process can be in control (stable) but still produce defects if control limits exceed specification limits.
Any point outside limits means the process is broken. Approximately 0.3% of points will fall outside 3-sigma limits by chance alone. Rules for detecting special causes consider patterns, not just individual points.