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What does it do?  | Control charts are time-ordered graphical displays of data that plot process variation over time. Control charts are the major tools used to monitor processes to ensure they remain stable. Control charts are characterized by A centerline, which represents the process average, or the middle point about which plotted measures are expected to vary randomly. Upper and lower control limits, which define the area three standard deviations on either side of the centerline. Control limits reflect the expected range of variation for that process. Control charts determine whether a process is in control or out of control. A process is said to be in control when only common causes of variation are present. This is represented on the control chart by data points fluctuating randomly within the control limits. Data points outside the control limits and those displaying nonrandom patterns indicate special cause variation. When special cause variation is present, the process is said to be out of control. Control charts identify when special cause is acting on the process but do not identify what the special cause is. There are two categories of control charts, characterized by type of data you are working with: continuous data control charts and discrete data control charts
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Why Use? 
| Control charts serve as a tool for the ongoing control of a process and provide a common language for discussing process performance. They help you understand variation and use that knowledge to control and improve your process. In addition, control charts function as a monitoring system that alerts you to the need to respond to special cause variation so you can put in place an immediate remedy to contain any damage |
When Use? 
| In the Measure phase, use control charts to understand the performance of your process as it exists before process improvements. In the Analyze phase, control charts serve as a troubleshooting guide that can help you identify sources of variation (Xs). In the Control phase, use control charts to : 1. Make sure the vital few Xs remain in control to sustain the solution - 2. Show process performance after full-scale implementation of your solution. You can compare the control chart created in the Control phase with that from the Measure phase to show process improvement -3. Verify that the process remains in control after the sources of special cause variation have been removed |
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In statistical process control, the control chart, also known as the 'Shewhart chart' or 'process-behaviour chart' is a tool used to determine whether a manufacturing or business process is in a state of statistical control or not. If the chart indicates that the process is currently under control then it can be used with confidence to predict the future performance of the process. If the chart indicates that the process being monitored is not in control, the pattern it reveals can help determine the source of variation to be eliminated to bring the process back into control. A control chart is a specific kind of run chart that allows significant change to be differentiated from the natural variability of the process. This is key to effective process control and improvement. A control chart consists of the following: - Points representing measurements of a quality characteristic in samples taken from the process at different times [the data]
- A centre line, drawn at the process characteristic mean which is calculated from the data
- Upper and lower control limits (sometimes called "natural process limits") that indicate the threshold at which the process output is considered statistically 'unlikely'
The chart may contain other optional features, including: - Upper and lower warning limits, drawn as separate lines, typically two standard deviations above and below the centre line
- Division into zones, with the addition of rules governing frequencies of observations in each zone
- Annotation with events of interest, as determined by the Quality Engineer in charge of the process's quality
However in the early stages of use the inclusion of these items may confuse inexperienced chart interpreters. If the process is in control, all points will plot within 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 special-cause variation. Since increased variation means increased costs, a control chart "signaling" the presence of a special-cause requires immediate investigation.
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