DOWNLOAD ← Click this to download the “Control Chart” template file.
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How to make and use a Control Chart from the 7 QC Tools
Hi, this is Mike Negami, Lean Sigma Black Belt.
Control Charts are often used in the Improvement Phase and the Control Phase in DMAIC. But it is not used much in the service operation improvement since there is a need to collect data on a regular basis. However, the concept of monitoring operational data and taking preventive actions is fundamental in Kaizen activities, so we’ll learn the Control Chart today.
I made a Control Chart template. Please try it by clicking the link in this video’s description as usual.
- DOWNLOAD ← Click this to download the “Control Chart” template file.
Before going into the template explanation, I have a little announcement: I’ve been making templates in English and Japanese. This time I added a Spanish version. Click the link below to go to the English ‘Free Download’ page, where you can go to the Spanish and Japanese ‘Free Download’ pages.
- Click this to got to the Excel Template Download Page with the links to Spanish Version and Japanese Version
How to use the Control Chart Template
This is the template’s sheet. There are 7 types of Control Charts, depending on what kind of data you use. This template is the most commonly used XbarR Control Chart.
First, prepare your measured data on Excel. In this example, this has one set of 5 operational times in a row for 30 days vertically in time series.
After opening the file, on the template file, double-click here, then follow each message and click ‘Yes’ or ‘No’ depending on your situation.
Here, Excel is asking where your data is. Select your data in a maximum of 5 columns with your mouse and, click ‘OK’.
How to read the Control Chart
This is the result. Two charts appeared. This column shows each set of 5’s averages. Those averages are plotted in yellow in the above chart.
In the chart, the upper and lower lines are control limit lines. In short, if your plots go out of the lines, there would be an abnormality, in other words, a ‘Special Cause’ in your process. Here it is.
Next, this column shows the differences between each maximum value and minimum value of each set of 5 data. We call it ‘Range R’. Those Range Rs are plotted in gray in the R Control Chart below. You can see variations in your data from the chart.
Control Chart’s Interpretation and Action Plan
Returning to the first worksheet of the template, there is the ‘Interpretation and Action Plan’ session. Let’s read it.
1) Create this control chart. When ‘Special Causes’, which will be explained next, are shown in your control chart, identify a root cause in your process and take preventive actions.
2) A ‘Special Cause’ is a signal that some abnormality in the process may have occurred. In the Control Chart, they are represented when the following symptoms appear.
a) When plot(s) are beyond the upper or lower control limit line. We’ve already learned this.
b) Even within the control limit lines, when there are 7 or more consecutive plots above or below the average line. See the example charts below on the left.
c) Even within the control limit lines, when there were 7 or more consecutive plots trending up or down. See the example charts below on the right. In order to remember these 4 ‘Special Causes’ easily, we call them the ‘Rule Of Seven’.
Since there are more definitions of ‘Special Cause’ in other companies and industries, it’s a good idea to add applicable ones to your company’s practice.
You can use this Control Chart effectively in the Check stage of the PDCA Cycle. Even in service industries, you can use it when you collect data on a regular basis. Even if not, it’s still a good idea to use it for a limited time.
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