Statistical process control (SPC) is the use of statistical methods to assess the stability of a process and the quality of its outputs. For example, consider a bottling plant. The entire system of production that produces filled bottles is termed a process. Suppose the weight of liquid content added to a bottle is critical for cost control and customer satisfaction. The content should weigh 250 grams, but it is acceptable if the actual weight is between 245 and 255 grams. Monitoring means that the weight of every bottle is measured and recorded; sampling means that only a few bottles (say one in a thousand) are actually weighed (analysis to determine the rate of sampling and to assess sample representativeness is a well-established part of SPC.)
SPC relies on quantitative and graphic analysis of measurements to evaluate observed variation. If the attributes of interest (content weight in this example) vary within an acceptable range, a process is said to be in control, in statistical control, or stable. When unacceptable variation is noted, actions are typically taken to determine and correct their cause. In the bottling example, suppose too many bottles are filled with less than 245 grams. Checking the plant equipment reveals that one of ten filler valves is malfunctioning.
SPC has had broad application in manufacturing since its introduction in the 1920s and in many other kinds of repetitive activities.
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.