Statistical process control (SPC) is the use of statistical methods in the monitoring and control of a process, by repeatedly sampling measurements, or counts, to predict results. Under SPC, a process behaves predictably to produce results with the least possible waste. Once a process has become stable and responds as predicted, then it is said to be in a state of “statistical control”. While SPC has been applied most frequently to controlling manufacturing lines, it applies equally well to any process which has measurable factors. Key tools in SPC are control charts, a focus on continuous improvement and designed experiments.
Much of the power of SPC lies in the ability to examine a process, for the sources of variation in that process, by using tools which give weight to objective analysis over subjective opinions and which allow the strength of each source to be determined numerically. Variations in the process, which might affect the quality of the end-product or service can be detected and corrected, thus reducing waste as well as the likelihood that problems will be passed on to the customer. With its emphasis on early detection and prevention of problems, SPC has a distinct advantage over other quality methods, such as inspection, which apply resources to detecting and correcting problems after they have occurred.
In addition to reducing waste, SPC can lead to a reduction in the time required to produce the product or service from end-to-end. This is partially due to a reduced likelihood that the final product will have to be reworked, but it may also result from using SPC data to identify bottlenecks, wait times, and other sources of delays within the process. Process cycle time reductions, coupled with improvements in yield, have made SPC a valuable tool from both a cost-reduction and a customer-satisfaction standpoint.