Key Points

• Special cause variation refers to any variations outside of control limits.
• They can point to defects, mistakes, or other issues in a process.
• They only work with current data, relying on historical data won’t shed light on any variation.

Special cause variation, I love to see it! That’s because I know I’m about to learn something important about my process. A special cause is a signal that the process outcome is changing — and not always for the better.  

Overview: What Is Special Cause Variation?

A control chart can show two different types of variation: common cause variation (random variation from the various process components) and special cause variation.

Special cause variation is present when the control chart of a process measure shows either plotted point(s) outside the control limits or a non-random pattern of variation.

When a control chart shows special cause variation, a process measure is said to be out-of-control or unstable. Common types of special cause variation signals include:

•   A point outside of the upper control limit or lower control limit
•   A trend: 6 or 7 points increasing or decreasing
•   A cycle or repeating pattern
•   A run: 8 or more points on either side of the average

 A special cause of variation is assignable to a defect, fault, mistake, delay, breakdown, accident, and/or shortage in the process. When special causes are present, process quality is unpredictable.

Special causes are a signal for you to act to make the process improvements necessary to bring the process measure back into control.

RELATED: COMMON CAUSE VARIATION VS. SPECIAL CAUSE VARIATION

Drawbacks of Special Cause Variations

The source of a special cause can be difficult to find if you are not plotting the control chart in real time. Unless you have annotated data or a good memory, control charts made from historical data won’t aid your investigation into the source of the special cause. 

If a process measure has never been charted, it is almost certain that it will be out of control. When you first start studying a process with a control chart, you will usually see a variety of special causes. To find the sources, begin a study of the status of critical process components. 

When a special cause source cannot be found, it will become common in the process. As time goes on, the special causes repeat and cease being special. They then increase the natural or common cause variation in the process. 

Why Is Special Cause Variation Important to Understand? 

Let’s define quality as minimum variation around an appropriate target. The study of variation using a control chart is one way to tell if the process variation is increasing or if the center is moving away from the desired target over time.  

A special cause is assignable to a process component that has changed or is changing. Investigation into the source of a special cause will:

1. Let you know when to act to adjust or improve the process.
2. Keep you from making the mistake of missing an opportunity to improve a process. If the ignored special cause repeats, you still don’t know how to fix it.
3. Provide data to suggest or evaluate a process improvement.

If no special cause variation exists, that is, the process is in control, you should leave the process alone! Making process changes when there is no special cause present is called Tampering and can increase the variation of the process, lowering its quality.

An Industry Example of Special Cause Variation

In this example, a control chart was used to monitor the number of data entry errors on job applications. Each day a sample of applications was reviewed. The number of errors found was plotted on a control chart. 

One day, a point was plotted outside the control limit. Upon investigation, the manager noticed it occurred when a new worker started. It was found the worker wasn’t trained.

The newly trained worker continued data entry. A downward trend of errors followed, indicating the training was a source for the special cause! 

The manager issued guidelines for new worker training. Since then, there have been three new workers without the error count spiking. 

Why It Matters

So, why should you consider using special cause variation? While there are plenty of tests and tools to use during your statistical analysis, few are going to show outliers with ease. Utilizing special cause variation lets immediately see outliers as they happen, however, you need to have the right data available.

3 Best Practices When Thinking About Special Cause Variation

Special causes are signals that you need to act to move your process measure back into control. 

Identify the source

When a special cause of variation exists, make a timely effort to identify its source. A good starting point is to check if any process component changed near the time the special cause was seen. Also, you could ask process experts to brainstorm why the special cause samples were out of control.

For example, a trend in screw thickness could be caused by a gage going out of calibration.

Make improvements at the source

Implement improvements to the source of special cause variation. Once you make improvements to the source of the special cause (like re-calibrating that gage), watch what happens as the next thickness samples are plotted.  If the plot moves back toward stability, you know you found the issue! 

Document everything

As you identify recurring special causes and their sources, document them on a control plan so process operators know what to do if they see the special cause again.

For our Gage, the control plan could direct a worker to recalibrate the next time the screw thickness trends up, sending the process back to stability.  

Other Useful Tools and Concepts

While we’ve discussed special cause variation at length, that’s only one tool at your disposal. If you need special tools to get down to the root of a problem, the 6W approach is a handy and low-cost way to do so. This approach utilizes 6 basic questions to get down to the cause of a problem.

Further, how do you design a product without issues? Well, nothing is perfect, but you can reach for the unattainable a lot easier with poka-yoke. This design methodology accounts for failure and defects, allowing you to design a product that meets customer needs while whittling down faults.

Final Thoughts on Special Causes 

Every process measure will show variation, you will never attain zero variability. Still, it is important to understand the nature of variability so that you can use it to better improve and control your process outcomes. 

The special cause variation signal is the key to finding those critical process components that are the sources of variation needing improvement. Use special cause variation to unlock the path to process control.