Statistical process control (SPC) uses statistical data to monitor and control processes. It focuses on quantitative analysis to identify sources of variation and determine if a process is under statistical control. SPC aims to bring the process under control to predict results, and negative causes are addressed through investigation. Positive causes aim to implement causality at every step of the process.
Commonly used in the manufacturing process, statistical process control (SPC) makes use of statistical data gathered through statistical analysis to monitor and control virtually any process where output can be measured. SPC employs a variety of method-related tools to include experimentation, control charts, and continuous improvement processes. The key difference between SPC and other process control methods is a focus on quantitative analysis, rather than opinion, when analyzing variations in a process. Applied to a wide range of processes beyond manufacturing, statistical process control focuses on identifying the sources of variation and determining the extent of that variation. Based on that information, managers can decide whether the change is acceptable, whether it indicates a problem or a positive causal link that needs to be replicated.
Based on the premise that any measurable output will have variation from assignable common, natural or special causes, statistical process control seeks to determine whether a variation is under statistical control. Using control charts, analysts will look for changes in a process over the time period specified by the chart. Having identified those changes, the analyst will then use the chart to determine the source of the change and whether that change falls within a predetermined and specified range. When the identified variations fall within a predetermined and specified range, the process is defined as being under statistical control. Otherwise, however, the process is considered to be outside statistical control.
Variations that are beyond statistical control are said to arise from special, assignable causes. Such variations are usually determined by the actual process and statistical software is often used to perform the required calculations, which are then plotted on the control chart. Statistical process control aims to determine if a process is under statistical control, because if it is then the process and be expected. Accurately forecasting the outputs of a process provides analysts with important information, such as the time it takes to fulfill a specific type of production order. Next, the concern of the SPC method is to bring the process under statistical control so that the results can be reliably predicted.
Once a process is determined to be out of statistical control, it looks for assignable causes and determines whether they are good or bad for the process. Negative causes are addressed after an investigation to ascertain and eliminate the cause, then the process is repeatedly re-analysed with SPC until the problem is resolved. Positive causes usually follow the same process, but with the goal of implementing causality at every step of the process.
Protect your devices with Threat Protection by NordVPN
