Control charts are statistical tools that plot quality characteristics in order to measure whether a process is under statistical control. Their basic purpose of such charts is to control process variation. The characteristic measured by control charts is usually plotted on the vertical axis, while the sample is displayed on the horizontal axis. Control charts also illustrate the process mean as well as upper and lower control limits, which are drawn 3 standard deviations above and below the central line respectively.
These limits are used to make conclusions about the statistical stability of the process.
In case control charts show that the process is stable and currently under control, there is no need for intervention and no corrections to the process are needed. If a control chart indicates a variation in the process that does not come from a source inherent to the process, then the process should be improved in order to avoid performance deterioration. The quality outside of the control limits is called “defective” and defective (or nonconforming) processes have to be corrected. For this purpose, it is not always necessary to inspect every detail, however, it is recommended to implement full-sample inspection in the beginning of the process as well as if the percentage of defective items increases beyond the control limits.
The reasons behind out-of-control indicators vary and depend on the individual specificities of the process. They lead to diverse patterns on the control charts, which can suggest possible ways to address the issue. Thus, for example cyclic control chart pattern occurs if the process has a cyclical nature. For example, the number of errors in the warehouse increases daily during peak load hours and decreases after that. In this case, process operators should either consider a different parameter to use, when plotting it on the control chart, or take actions to smoothen the process. Thus, control charts serve as a helpful tool to monitor processes and to ensure high level of performance.
References
Doty, L. A. (1996). Statistical process control. (2nd ed.). New York, New York: Industrial Press Inc.
