Statistical Process Control (SPC) is a method of total quality management excellence control that uses analytical methods to monitor and control processes in order to ensure they are operating within predetermined limits. The goal of SPC is to optimize process performance in the output of a process.

Implementing SPC requires a thorough understanding of the process, including its factors and interactions. It also involves identifying and analyzing data on these variables, using statistical tools like statistical process capability indices. These tools help to detect variation and identify deviations from expected goals and objectives.

There are four key components to a process for implementing SPC: Process Quality, Efficiency, Predictability, and Consistency. The first component, Variation, is concerned with the distribution of process data. Each process usually produces a unique profile. Next is Predictability or Capability, which concerns knowing how well the process is working, with the aim of achieving accuracy and precision as close as possible to the expected mean of the regulatory requirements.

The process capability, also known as 'process cpk', measures the spread of data of the process. A target value or a minimum specification and a specification limit (USL) or a target value or a minimum specification and a lower specification limit defined is required for a data point to operate within these limits and regulations.

A control chart evaluates process behavior and highlights anomalies and deviations in the process. Setting effective monitoring rules includes implementing improvements specific to the process problems.

Automated control methods, which include predictive analytics and machine learning, offer various tools to optimize and improve process efficiency. These systems usually cut down process deviation rates over time and have become essential in the manufacturing sector.