Editing Control chart (section)

===Chart usage===

If the process is in control (and the process statistic is normal), 99.7300% of all the points will fall between the control limits. Any observations outside the limits, or systematic patterns within, suggest the introduction of a new (and likely unanticipated) source of variation, known as a [[Common- and special-causes|special-cause]] variation. Since increased variation means increased [[quality costs]], a control chart "signaling" the presence of a special-cause requires immediate investigation.

This makes the control limits very important decision aids. The control limits provide information about the process behavior and have no intrinsic relationship to any [[specification]] targets or [[engineering tolerance]]. In practice, the process mean (and hence the centre line) may not coincide with the specified value (or target) of the quality characteristic because the process design simply cannot deliver the process characteristic at the desired level.

Control charts limit [[Specification (technical standard)|specification limits]] or targets because of the tendency of those involved with the process (e.g., machine operators) to focus on performing to specification when in fact the least-cost course of action is to keep process variation as low as possible. Attempting to make a process whose natural centre is not the same as the target perform to target specification increases process variability and increases costs significantly and is the cause of much inefficiency in operations. [[Process capability]] studies do examine the relationship between the natural process limits (the control limits) and specifications, however.

The purpose of control charts is to allow simple detection of events that are indicative of an increase in process variability.<ref> Statistical Process Controls for Variable Data. Lean Six sigma. (n.d.). Retrieved from https://theengineeringarchive.com/sigma/page-variable-control-charts.html. </ref> This simple decision can be difficult where the process characteristic is continuously varying; the control chart provides statistically objective criteria of change. When change is detected and considered good its cause should be identified and possibly become the new way of working, where the change is bad then its cause should be identified and eliminated.

The purpose in adding warning limits or subdividing the control chart into zones is to provide early notification if something is amiss. Instead of immediately launching a process improvement effort to determine whether special causes are present, the Quality Engineer may temporarily increase the rate at which samples are taken from the process output until it is clear that the process is truly in control. Note that with three-sigma limits, [[Common- and special-causes|common-cause]] variations result in signals less than once out of every twenty-two points for skewed processes and about once out of every three hundred seventy (1/370.4) points for normally distributed processes.<ref name="Wheeler112010">{{cite web|last=Wheeler|first=Donald J.|title=Are You Sure We Don't Need Normally Distributed Data?|url=http://www.qualitydigest.com/inside/quality-insider-column/are-you-sure-we-don-t-need-normally-distributed-data.html|publisher=Quality Digest|access-date=7 December 2010|date=1 November 2010}}</ref> The two-sigma warning levels will be reached about once for every twenty-two (1/21.98) plotted points in normally distributed data. (For example, the means of sufficiently large samples drawn from practically any underlying distribution whose variance exists are normally distributed, according to the Central Limit Theorem.)