Statistical process control (SPC) is a strategic approach to quality management that uses statistical techniques to monitor, measure, and control an operational process. It helps to make sure the process runs efficiently, creating more specifications-conforming goods with minimal waste. Process control seeks to establish the value of a process and the effect that reduced process variation has on the final product. In short, the goal is to improve the quality of the end result by controlling processes that create variation in output. Process control also helps reduce or eliminate waste or improve efficiency.

The objective of statistical process control is to achieve continuous process improvement by controlling processes that create variability. These processes may include variations due to human intervention, external factors, and/or physical or technological variables. Variability can refer to any unwanted outcome such as exceeding capacity, exceeding toleration levels, or any number of other things that can happen. Some processes that cause variability can be extremely difficult to observe and measure, especially at the micro level of a process, such as manufacturing a piece of equipment. In these cases, it is necessary to use statistical metrics to monitor and measure process variability. The methodology used to track and control process variability can vary widely between disciplines; however, the basic statistical concepts are often the same.

One common example of statistical process control is production measurement, which refers to the ability to measure performance over time, which can provide information about capacity, costs, and quality. Another common example is quality inspection, which tracks and measures problems and defects that occur before they become systemic. Another example is manufacturing control, which seeks to improve quality and efficiency by controlling processes that create process variation. While there are many examples of statistical process control at work today, the most common industries using this method are those involved in the manufacturing, construction, and utility industries. Process control charts are a common tool used to track and manage process variability.

There are many different statistical process control tools used in manufacturing businesses. These include data collectors, actuators, regulators, and testers. The data collector can collect and evaluate quality parameters or standards. Regulators ensure that measurements are conforming to specifications. And testers make certain that tests are being properly conducted and maintained.

Quality assurance is one of the main components of statistical process control. Quality assurance charts allow managers and supervisors to keep tabs on manufacturing processes and quality standards. Quality assurance test charts are often used in quality control inspections. These charts will help a manager or supervisor determine if the requirements of a manufacturing process are meeting the company’s specifications. Poor quality control can lead to production accidents, lower productivity, and even employee grievances. Having accurate charts is one key element in improving quality and efficiency in manufacturing processes.

There are many special causes of variation across manufacturing processes. In addition to process variability, there are also natural factors that can contribute to deviations from specifications. Some causes of statistical process control include operator error, human error, output control, quality control, and statistical environment. A company’s SPC policies should take these into consideration as well as the variations caused by outside circumstances, such as economic conditions.