Key Points

• Common cause variation relies on stable, predictable variations that can be observed in a process.
• Special cause variations are significant departures from the norm, often having extreme values.
• Both variations allow teams to determine if a process is good to go, or needs improvement.
• Looking at both variations is a snapshot of production health, and can be a key component of process refinement and analysis.

Common cause vs. special cause variation, aren’t they the same thing? At first glance, you’d be allowed to think so. However, as with any part of the production cycle, there is a reason for these two measurements to exist. So, let’s dive right into what makes these variations different, and how to identify and benefit from their usage.

What is Common Cause Variation?

Common cause variation is the kind of variation that is part of a stable process. These are variations that are natural to a system and are quantifiable and expected. Common cause variations are those that are predictable, ongoing, and consistent. Major changes would typically have to be made to change the common cause variations.

One example of a common cause variation would be when a task takes slightly longer or shorter to accomplish than in the meantime. Other examples could be normal wear and tear, computer lag time, and measurement errors.

The Benefits of Common Cause Variations

Since common cause variations are always present, they can be measured to establish a baseline using statistical techniques of the normal variation. These types of variations also fit easily within the control limits of a control chart.

How to Identify Common Cause Variation

You can identify common cause variation points on the control chart of a process measure by its random pattern of variation and its adherence to the control limits.

What is Special Cause Variation?

Special cause variations are unexpected glitches that occur that significantly affect a process. It is also known as “assignable cause.” These variations are unusual, and unquantifiable, and are variations that have not been observed previously, so they cannot be planned for and accounted for.

These causes are typically the result of a specific change that has occurred in the process, with the result being a chaotic problem.

One example of a special cause variation would be a task taking exorbitantly longer than typical due to an unexpected crisis. Other examples would be power outages, computer crashes, and machine malfunctions.

The Benefits of Special Cause Variation

One benefit of special cause variations is that they are typically connected to a defect in the system or process that is addressable. Changes to components, methods, or processes can help prevent the special cause variation from occurring again.

How to Identify Special Cause Variation

You can identify special cause variation on a control chart by their non-random patterns and out-of-control points.

Common Cause vs. Special Cause: What’s the Difference?

Common cause variation and special cause variation are related in that they can both be present in the performance of a process. The difference between these two types of variation lies in how common cause variations are expected variations that do not deviate from the natural order of a process. With common cause variations, a process remains stable.

With special cause variations, however, a process is dramatically affected and becomes unstable. In short, common cause variations reflect a stable process, while special cause variations reflect an unstable process.

Why It Matters

So, we’ve explained and explored both of these concepts, but why? Understanding when to differentiate between these two variations within a process can help determine whether there are problems that need adjusting. If you’ve got a stable process with minimal fluctuations in the variation, it likely is good to go.

However, if you’re seeing extremes, it might need addressing sooner rather than later.

Common Cause vs. Special Cause: Who would use A and/or B?

Both of these types of variation are important to have an understanding of in project management. You can keep track of a project’s health by observing control charts. However, being able to spot the differences between common cause variations and special cause variations.

The ability to spot the differences allows for knowing if a process is stable or not. It also lets you know if there are variations that need to be addressed by making changes or if they can likely be left alone.

Choosing Between Common Cause and Special Cause: Real World Scenarios

A project manager has been tasked with looking at the performance of a project during the previous quarter. A control chart is drafted that shows any variance that occurred during that quarter. With an understanding of how common cause and special cause variance is displayed on a control chart, the project manager looks for points on the chart that appear non-random and that go outside the control of the chart.

Upon inspection, the project manager finds a group of points that fall well outside the parameters of what is typical. A few of the workers are called. It is determined that at the time those points fell under, there was a flood that prevented the necessary work from being done.

This adequately explains the presence of special cause variation on the control chart.

Getting Control of Your Processes

You’re going to need more than an understanding of variations to get your processes reined in. I heavily recommend taking a look at how Takt Time and Cycle Time differ and relate, at least for your process workflow. These are basic concepts and are readily applied to any industry.

If you need more clarity regarding data types, it is our guide on categorical and continuous data is worth reading. This in-depth guide covers the ins and outs of these data types and where they are best utilized.

Summary/Conclusion

Variation in a process is expected. Over a given time, it is essentially unavoidable. Nevertheless, by understanding control charts and being able to recognize variances that are typical for the process and those that are atypical, we can make changes to processes to prevent or safeguard against the same special cause variation in the future.