What specific quality control technique uses statistical analysis to monitor a process and ensure work consistently meets specified standards?

The specific quality control technique that uses statistical analysis to monitor a process and ensure work consistently meets specified standards is Statistical Process Control (SPC). Statistical Process Control (SPC) is a systematic methodology within quality control that applies statistical methods to monitor and control a process. A process is defined as any set of activities that transforms inputs into outputs, such as manufacturing a product or performing a service. Its core purpose is to ensure that the process operates stably and efficiently, consistently producing outputs that conform to specified standards, which are the predefined quality requirements or specifications. SPC functions by continuously collecting data from the process over time and subjecting it to statistical analysis to understand and manage process variation. There are two primary types of variation inherent in any process: Common Cause Variation and Special Cause Variation. Common Cause Variation is the natural, random, and expected variation that is an inherent part of a stable process operating in statistical control, stemming from numerous small, unavoidable factors. Special Cause Variation, conversely, is unexpected, non-random variation caused by specific, identifiable factors that are not part of the normal process, such as a machine malfunction or a change in raw material quality. The central tool of SPC is the control chart. A control chart is a graphical representation that plots process data points over time, alongside statistically calculated control limits. These control limits, specifically the upper control limit (UCL) and lower control limit (LCL), are derived from the process's historical data and statistically define the expected range of common cause variation. By observing the plotted data on a control chart, operators can distinguish between common cause and special cause variation. If data points consistently fall within the control limits and display no unusual patterns, the process is deemed "in statistical control," indicating predictability and that only common cause variation is present. However, if a data point falls outside the control limits, or if specific non-random patterns (like a trend or a run of points on one side of the center line) emerge, it signals the presence of a "special cause variation." This signifies the process is "out of control" and requires immediate investigation and corrective action to identify and eliminate the root cause of the unexpected variation. By promptly detecting and addressing special causes, SPC stabilizes the process and prevents defects. Once a process is brought into statistical control by eliminating special causes, efforts can then focus on reducing the overall common cause variation, thereby further enhancing consistency and ensuring the work reliably meets or exceeds specified quality standards.