Control Charts Show You Variation that Matters

They say "variety is the spice of life," but when it comes to doing business, variation is not your friend. That's why we have control charts.


One of these things is not like the others...is it common cause variation you can accept, or special variation that needs to be addressed? Control charts could tell you.

We know that a little bit of variation is inevitable, but we tolerate it within acceptable limits.

When you buy a burger from a fast-food joint you want consistency, not unpredictability. Now, the pickle on your burger may be closer to the edge of the bun today than it was last week, true -- but as long as the pickle is there, it's acceptable.

Businesses use statistical process control (SPC) to keep processes stable, consistent, and predictable, so they can ensure the quality of products and services. And one of the most common and useful tools in SPC is the control chart.

The control chart shows how a process or output varies over time so you can easily distinguish between "common cause" and "special cause" variation.  Identifying different causes of variation lets you take action on a process without over-controlling it.

"Common cause" variation at our burger joint would be pickles being placed on different sides of the buns. We expect that level of variation, and it's no big deal.

"Special cause" variation would be a sudden rash of burgers that have 10 pickles instead of 1. Clearly, something unusual is causing unacceptable variation, and it needs to be addressed! 


Now you probably wouldn't need a control chart to detect special cause variation that results in 10 pickles instead of 1 on a burger. But most process variation is much more subtle, and control charts can help you see special cause variation when it isn't so obvious.

Depending on the type of data you're looking at, you can choose from several different control charts. But they all share a few basic parts:

  • Control limits: the upper and lower boundaries of variation you can accept.
  • Expected variation region: the area between your lower and upper control limits. This is where your process is expected to perform unless a change occurs. Also known as common cause variation.
  • Unexpected variation region: The area beyond your control limits. Also known as special cause variation. This variability should be investigated and acted upon.

Common Components of a Control chart

You can easily create a wide variety of different control charts using Minitab Statistical Software; in a future post I'll discuss some different types of charts and when you might want to use them.

Have you used control charts to manage your processes?


Name: Omar Mora • Thursday, August 4, 2011

Dear Eston,
You are right: it is very easy to create Control Charts with Minitab.
I really like the "Update Automatically" feature,as well as the possibility to configure the S-limits and control-tests.
The Assistant is another tool every Minitab´s user has to try when selection Control Charts.

Name: IRIS • Friday, December 23, 2011

I Have a question. What chart type is used to datos of output/shift or output/ day..... ect....... some people see this data as continuos an other see as count.......

Name: Eston Martz • Thursday, January 5, 2012

Hi Iris,
That's not an easy question to answer. Or maybe I should say it's not a question with a single correct answer...I can see where output could be either continuous or discrete data, depending on how output is being measured. Continuous data usually involve measurements, and often include fractions or decimals. If you're using Minitab 16, have you tried the Assistant menu? It can guide you to the appropriate control chart to use based on how you answer the questions it presents. Hope this helps!

Name: Skip Danielson • Tuesday, November 6, 2012

I am looking for a good reference that discusses trend analysis using control charts. Particularly the 5,7,and 9 rules and their possible causes and fixes.

Thank you

Name: Eston Martz • Tuesday, November 6, 2012

Hi Skip,

I'm not sure there's a simple answer to your question; causes and fixes for out-of-control points on a control chart need to be looked at case-by-case. Are you talking about detecting, for instance, subtle drift in a process over time? In any event, one seminal text on control charts, etc., is Quality Control and Industrial Statistics by Acheson Duncan. But there are many other good references available. If you contact our technical support team with a specific question and more details about what you're trying to do, they should be able to help.


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