Control charts (also known as Shewhart charts, after Walter A. Shewhart, or process-behavior charts) are graphical plots used in statistical process control (SPC) to determine whether a manufacturing or business process is in a state of statistical control. They form the basis of modern statistical process control, and are one of the seven basic tools of quality control. A control chart plots a process statistic, such as a mean, range, or proportion, over time together with a centre line and upper and lower control limits that distinguish common-cause variation from special-cause variation. Points outside the control limits or non-random patterns within them indicate that the process should be investigated for assignable causes of variation. When a process is in statistical control, control charts can be used to predict its future performance. Control charts were invented by Shewhart at Bell Labs in the 1920s.
Overview If analysis of the control chart indicates that the process is currently under control (i.e., is stable, with variation only coming from sources common to the process), then no corrections or changes to process control parameters are needed or desired. In addition, data from the process can be used to predict the future performance of the process. If the chart indicates that the monitored process is not in control, analysis of the chart can help determine the sources of variation, as this will result in degraded process performance. A process that is stable but operating outside desired (specification) limits (e.g., scrap rates may be in statistical control but above desired limits) needs to be improved through a deliberate effort to understand the causes of current performance and fundamentally improve the process. The control chart is one of the seven basic tools of quality control. Typically control charts are used for time-series data, also known as continuous data or variable data. Although they can also be used for data that has logical comparability (i.e. you want to compare samples that were taken all at the same time, or the performance of different individuals); however the type of chart used to do this requires consideration.
History The control chart was invented by Walter A. Shewhart working for Bell Labs in the 1920s. The company's engineers had been seeking to improve the reliability of their telephony transmission systems. Because amplifiers and other equipment had to be buried underground, there was a stronger business need to reduce the frequency of failures and repairs. By 1920, the engineers had already realized the importance of reducing variation in a manufacturing process. Moreover, they had realized that continual process-adjustment in reaction to non-conformance actually increased variation and degraded quality. Shewhart framed the problem in terms of common- and special-causes of variation and, on May 16, 1924, wrote an internal memo introducing the control chart as a tool for distinguishing between the two. Shewhart's boss, George Edwards, recalled: "Dr. Shewhart prepared a little memorandum only about a page in length. About a third of that page was given over to a simple diagram which we would all recognize today as a schematic control chart. That diagram, and the short text which preceded and followed it set forth all of the essential principles and considerations which are involved in what we know today as process quality control." Shewhart stressed that bringing a production process into a state of statistical control, where there is only common-cause variation, and keeping it in control, is necessary to predict future output and to manage a process economically. Shewhart created the basis for the control chart and the concept of a state of statistical control by carefully designed experiments. While Shewhart drew from pure mathematical statistical theories, he understood that data from physical processes typically produce a "normal distribution curve" (a Gaussian distribution, also commonly referred to as a "bell curve"). He discovered that observed variation in manufacturing data did not always behave the same way as data in nature (Brownian motion of particles). Shewhart concluded that while every process displays variation, some processes display controlled variation that is natural to the process, while others display uncontrolled variation that is not present in the process causal system at all times. In 1924, or 1925, Shewhart's innovation came to the attention of W. Edwards Deming, then working at the Hawthorne facility. Deming later worked at the United States Department of Agriculture and became the mathematical advisor to the United States Census Bureau. Over the next half a century, Deming became the foremost champion and proponent of Shewhart's work. After the defeat of Japan at the close of World War II, Deming served as statistical consultant to the Supreme Commander for the Allied Powers. His ensuing involvement in Japanese life, and long career as an industrial consultant there, spread Shewhart's thinking, and the use of the control chart, widely in Japanese manufacturing industry throughout the 1950s and 1960s. Bonnie Small, a high school teacher in Oconomowoc, WI, read Shewhart's work and was inspired to employ control charts while working at her cousin's photography lab in Chicago. In 1942, she began working for Western Electric at their Hawthorne works in Cicero, her experience at the plant leading her to further refine the use of Shewhart's ideas. When General Electric opened a new plant in Allentown, PA to manufacture the newly developed transistor, Small was asked to supervise quality systems at the plant. She was asked by the company to form a committee for the purpose of codifying her approach to quality and in 1956, the committee published the first edition of The Western Electric Statistical Quality Control Handbook.
Chart details A control chart consists of:
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