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A quality control chart is a graphical representation of whether a firms products or processes are meeting their intended specifications. If problems appear to arise, the quality control chart can be used to identify the degree by which they vary from those specifications and help in error correction.
Statistical Process Control (SPC) charts are simple graphical tools that enable process performance monitoring. They are used to identify which type of variation exists within the process. They highlight areas that may require further investigation.
One of the most widely used SQC tools are control charts, which are graphical displays of data that show how a process or a product characteristic varies over time. Control charts can help you identify and eliminate sources of variation, detect trends and patterns, and determine if a process is stable and capable.
statistical quality control, the use of statistical methods in the monitoring and maintaining of the quality of products and services. One method, referred to as acceptance sampling, can be used when a decision must be made to accept or reject a group of parts or items based on the quality found in a sample.
The control chart is a graph used to study how a process changes over time. Data are plotted in time order. A control chart always has a central line for the average, an upper line for the upper control limit, and a lower line for the lower control limit.
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Control charts allow process variation to be separated into common and special causes and the causes to be treated separately. Control charts use common causes to set the control limits. Control charts give clear guidance on when to adjust a process and when to leave it alone.
The R (range) chart is a quality control chart used to monitor the variation of a process based on small samples taken at specific times. A quality control chart can also be univariate or multivariate, meaning that it can show whether a product or process deviates from one or from more than one desired result.
Control charts can help you: Understand the variations that are always present in processes. Variations within your control limits indicate that the process is working. Variations that spike outside of your control limits indicate problems that need to be corrected.