Statistical process control (SPC)

Statistical process control (SPC) monitors a process in real time using control charts, distinguishing normal, expected variation from a genuine signal that something has changed. A control chart plots a metric over time against a center line and upper and lower control limits, commonly set at plus or minus three standard deviations from the process mean.

SPC separates "common cause" variation — ordinary noise inherent to a stable process — from "special cause" variation, a real shift worth investigating, using rules such as a single point outside the control limits or a run of several points on one side of the mean. This differs from a simple fixed-threshold alert, because the limits are derived from the process's own observed variability rather than an arbitrary number.

SPC matters because it helps teams avoid two opposite mistakes: over-reacting to normal noise, which can make a stable process worse, and under-reacting to a real shift. It is a core tool in Six Sigma and manufacturing quality control, and is increasingly applied to software and service metrics. A common pitfall is treating a single outlier as proof of a trend without following up with root cause analysis, or applying control limits to a process that was never actually stable to begin with.