SPCS

Statistical process control (SPC) is a methodological approach to quality control that uses statistical methods to monitor and control a process. The goal is to reduce variability and ensure the process operates within its designed capability, producing consistent quality with minimal waste. SPC emphasizes prevention of quality problems by measuring performance and responding to signals rather than relying on end-of-line inspection alone.

A core component of SPC is the control chart, which plots a statistic from samples taken over time against predefined control limits. Common charts include X-bar and R charts for variables data and P- or NP-charts for attributes data, while C- and U-charts handle count data. Control limits are typically set at about three standard deviations from the process mean; signals such as points outside the limits or non-random patterns indicate possible special-cause variation that warrants investigation.

SPC also uses process capability analysis to quantify how well a process can meet specifications. Indices such as Cp and Cpk measure potential and actual performance relative to specification limits. Effective SPC requires well-designed data collection and sampling plans to accurately reflect the process state and avoid biased conclusions.

History and scope: SPC was developed by Walter A. Shewhart in the 1920s–1930s and was later popularized by Deming and others. While rooted in manufacturing, SPC has been applied across services, healthcare, software, and other industries where process variation matters.

Relation to quality methodologies: SPC is a foundational tool in lean manufacturing and Six Sigma programs and is often integrated with design of experiments to identify and eliminate sources of variation.

Limitations: SPC requires stable measurement systems, trained personnel, and ongoing management support. It identifies when variation occurs but does not by itself fix design flaws; sometimes process redesign is necessary.