Statistical Process Control (SPC) is a quality control and improvement methodology that uses statistical methods to monitor, control, and improve processes in various industries. SPC is particularly valuable in manufacturing and production settings but can be applied to virtually any process where data can be collected. Here are some key aspects and concepts related to SPC:
Process Variation
SPC is primarily concerned with understanding and managing two types of process variation: common cause variation (inherent to the process) and special cause variation (due to external factors or anomalies).
Control Charts
Control charts, also known as Shewhart charts or process-behavior charts, are a fundamental tool in SPC. They display process data over time, with upper and lower control limits to identify when a process is in or out of control.
Data Collection
SPC relies on the regular collection of data points or samples from a process. This data is then analyzed to assess process stability and identify any trends or deviations from expected performance.
Central Tendency and Variation
SPC often uses statistical measures of central tendency (e.g., mean or median) and measures of variation (e.g., range or standard deviation) to characterize a process and monitor its performance.
Process Capability Analysis
This involves assessing a process’s ability to produce products or services that meet customer specifications. Process capability indices like Cp, Cpk, Pp, and Ppk are used for this purpose.