Statistical process control (SPC)
Statistical process control (SPC) monitors a production process with control charts so operators can tell routine variation from a real change and correct drift before it makes bad parts. Process measurements (a dimension, a temperature, a fill weight) are plotted against statistically derived control limits; points outside the limits, or non-random patterns inside them, signal that something in the process has changed.
Examples
Tool wear caught early: A CNC cell turns a shaft to 25.00 ± 0.05 mm. The X-bar chart shows seven consecutive points trending upward, a classic wear signal, while every part is still in spec. The operator swaps the insert at the next break; nothing is scrapped and nothing ships out of tolerance.
In control, not capable: A molder's chart on a boss diameter is stable, but the capability study over 125 parts returns Cpk 1.08 against a required 1.33. The process needs a change (a steel-safe tool adjustment to recenter, then revalidation), not more operator vigilance.
Quarterly supplier review: A buyer requires Cpk data on five critical dimensions with each quarter's shipments. When one dimension slides from 1.65 to 1.41 over two quarters, the supplier investigates and finds fixture wear before any nonconforming part is produced.
Definition
Walter Shewhart developed the control chart at Bell Labs in the 1920s, and the core distinction is still his: common cause variation is the noise inherent to a stable process, while special cause variation comes from an assignable event (a tool change, a bad material lot, a loose fixture). The distinction tells you what to do. Special causes get investigated and removed; common cause variation only shrinks if you change the process itself. Adjusting a stable process point by point (tampering, in Deming's term) adds variation instead of removing it.
Control limits are not specification limits. Control limits describe what the process does; spec limits describe what the drawing wants. Capability indices connect the two: Cp measures whether the spread fits the tolerance, Cpk whether it fits while centered, and supplier quality agreements commonly require Cpk of at least 1.33 on critical characteristics. A process can be perfectly in control and still incapable.
For buyers, ongoing SPC beats inspection after the fact. Incoming quality control catches bad lots at the dock, and a first article inspection proves one part was right once; a control chart proves the process stayed right across thousands. Asking suppliers for capability data on critical dimensions, and reviewing it quarterly, catches the slow drift that erodes yield before it becomes scrap and sorting.
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