# Statistical Process Control (SPC): Three Types of Control Charts

January 15, 2021
General

If you have already made the decision to embrace a statistical process control (SPC) method—such as a control chart, which can visually track processes and abnormalities—you are already well on your way to bringing manufacturing quality control to your operations.

So, what’s the next step?

Determining which type of control chart to use.

“That decision is based on the style of the data stream that you would like to be presented,” Steve Wise, vice president of Statistical Methods for InfinityQS, says. “Trained statisticians will know when they walk up to a data stream what core control chart they should start with, but the first step is to ask yourself what decisions you want to make from this chart. That will help you dictate the chart type and sampling strategies.”

Control charts typically fall into three categories. Let’s take a quick look at the different types of control charts here. For a deeper dive, visit our Definitive Guide to SPC Charts.

### Xbar and Range Chart

The most common type of SPC chart for operators searching for statistical process control, the Xbar and Range chart, is used to monitor a variable’s data when samples are collected at regular intervals. The chart is particularly advantageous when your sample size is relatively small and constant.

### Individual-X Moving Range Chart

When it doesn’t make sense to take multiple readings, the Individual-X Moving Range chart is the ideal option. This particular SPC chart is used to monitor variables data when it is impractical to use rational subgroups. “When data is very expensive or there is a whole lot of time between samples, the concept of Xbar and Range makes no sense,” Wise explains. “It is better to go with Individual-X Moving Range.”

### Xbar and Standard Deviation Chart

This chart is primarily used to show how much variation or “dispersion” exists from the average or expected value. The Xbar and Standard Deviation chart is touted for helping manufacturers, engineers, and operators understand variation better.

“The more you peel back as you start exploring the data stream, the more you need to refine the chart type you select,” says Wise.