Run Chart vs Control Chart

Picture of Kerrie Preston

Published on 9 August 2018 at 14:58

by Kerrie Preston

Run Chart vs Control Chart
We often get people asking us what the difference is between Run Charts and Control Charts. So we thought we would write a quick article for you!
Run Charts and Control Charts are two basic QI tools with varying reporting abilities:

Run Charts

Run charts are one of the simplest to use but still provide valuable information. A Run chart is a line chart of data plotted overtime. The continual plotting of data enables you to uncover trends (upward and downward) and patterns in your project/process. This chart offers straight forward visualisation of data, making this chart type easy to analyse. There are limitations as they do not display statistical control limits, so don't detect unusual levels of variation with the same fidelity as a Control chart can. However this chart still has the ability to identify Common Cause and Special Cause Variation. 
 SPC Run Chart Example
Control Charts
A Control chart is a more advanced version of a Run chart. You may hear this chart referred to as a Shewhart chart. Whilst this chart still plots a single line of data, it also displays an upper line for the upper control limit and a lower line for the lower control limit. These lines allow you to identify the difference between normal and unusual variation in data and also whether the variation is positive or negative. Like Run Charts, Control Charts can identify different types of variation such as Common Cause and Special Cause, however the rules are different:
Common Cause - If controlled variation is displayed in the SPC chart, the process is stable and predictable. Which means that the variation is inherent in the process and the system will not need to be changed. For example, the seasonal increase in the number of Emergency Department visitors over the Christmas holiday period is a variation but it is expected and common. 
Special Cause - If special cause variation is displayed in the SPC chart then the process is deemed unstable and unpredictable. 
However, this instability is not necessarily a bad thing. For example, the number of pressure sores on a hospital ward may drop dramatically due to increased patient awareness. The aim of most quality improvement projects is to produce a positive special cause variation by introducing changes to a stable system through rapid fire Plan Do Study Act (PDSA) cycles, and then sustain the new level of performance displayed by the improved process.
It's important to remember variation may be caused by factors outside the process you are focused on. In this case, you need to identify these sources and resolve them, rather than change the process itself.
SPC P Chart Example
Control Charts do involve the use of basic statistics, so there is a certain amount of statistical knowledge required to create these charts which can be off-putting. However, nowadays there are QI systems available such as Life QI that can automatically calculate the charts for you which removes the need to be a statistician!




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