SQC

Statistical Quality Control (SQC) is a collection of statistical methods used to monitor and control a manufacturing process to ensure its outputs satisfy predefined quality standards. SQC relies on data collected from samples rather than inspecting every item, enabling timely decisions while limiting inspection costs. The goal is to detect and reduce variation, improve process capability, and maintain consistent quality.

Core tools include control charts, such as X-bar and R charts for variables and p, np, c, and u charts for attributes, which signal when a process may be out of statistical control. Acceptance sampling plans decide whether to accept or reject a production lot based on the outcome of sampled items. Process capability analysis uses indices such as Cp, Cpk, and sometimes Cpm to assess how well a process fits against specification limits. Design of experiments and regression analysis are also used to understand and reduce sources of variation.

SQC originated with Walter A. Shewhart in the 1920s at Bell Telephone Laboratories, who introduced control charts as a practical tool for distinguishing common cause from special cause variation. The approach gained prominence after World War II through the work of Deming, Juran, and others, and became foundational in modern quality-management systems, including Total Quality Management and Six Sigma.

In practice, SQC is closely related to statistical process control (SPC); the terms are often used interchangeably. Its application spans manufacturing and service industries, where data collection, appropriate sampling plans, and clear decision rules are essential. Limitations include sampling error, misinterpretation of charts, and the need for stable processes to obtain meaningful capability estimates.