Common cause variation

Common cause variation refers to variations in a process that are inherent to the system and are not due to special causes. These variations are usually random and follow a predictable pattern. Common cause variation is also known as "within-group" variation, as it occurs within a group of data. It is important to distinguish common cause variation from special cause variation, which is due to specific, identifiable factors that cause the process to deviate from its expected behavior. Identifying and addressing special cause variation is necessary to improve the process and reduce variation, while common cause variation is typically accepted as a normal part of the process.

Common cause variation examples

Common cause variation examples can be found in many different types of processes and industries. Here are a few examples:

• Manufacturing: In a manufacturing process, common cause variation might occur due to variations in the raw materials or the machinery used to produce a product. For example, variations in the size or shape of raw materials might lead to variations in the finished product.
• Service industry: In a service industry, common cause variation might occur due to variations in the skill or experience level of the service providers. For example, a restaurant might experience common cause variation in the cooking time of a dish due to the skill level of the chef preparing it.
• Healthcare: In healthcare, common cause variation might occur in lab test results due to variations in the equipment or techniques used to perform the tests.
• Construction: In construction, common cause variation might occur due to variations in the weather or the condition of the materials used. For example, variations in the temperature or humidity might cause variations in the curing time of concrete.
• Retail: In retail, common cause variation might occur due to variations in customer traffic or the availability of certain products.

These are a few examples, Common cause variation can be found in any process, and it is important to understand and accept it as a normal part of the process, in order to improve the process, it is important to identify and address special cause variation.

Common cause variation formula

Common cause variation can be measured using statistical process control (SPC) techniques such as control charts. One common method for measuring common cause variation is through the use of control limits. Control limits are calculated using the formula:

• UCL (Upper Control Limit) = Xbar + A2Rbar
• LCL (Lower Control Limit) = Xbar - A2Rbar

Where:

• Xbar is the average of the data
• Rbar is the average range of the data
• A2 is a constant determined by the number of samples in the data set

When data points fall within these control limits, it indicates that the process is likely in a state of common cause variation. Points that fall outside of the control limits may indicate special cause variation and warrant further investigation.

It is important to note that, in addition to the above mentioned method, there are other methods to measure common cause variation, such as using capability index like Cp, Cpk, Pp, Ppk etc.

Other types of variations

In addition to common cause variation, there are several other types of variation that can occur in a process:

• Special cause variation: This is variation that is caused by specific, identifiable factors such as equipment malfunction or human error. Special cause variation can be detected using statistical process control techniques like control charts and can be addressed through process improvement efforts.
• Assignable cause variation: This type of variation is also caused by specific, identifiable factors, but they are often not known or understood. It is also called as special cause of variation which are not yet identified.
• Noise: This refers to small, random variations that have no meaningful impact on the process. Noise is typically considered to be a form of common cause variation and is not considered when analyzing process data.
• Trend: This refers to a gradual change in the process over time. Trends can be caused by changes in the process, equipment, or the environment, and can be identified by analyzing process data over time.
• Cyclical variation: This type of variation is caused by patterns that repeat over time. These patterns can be due to external factors such as weather or internal factors such as production schedules.

It is important to differentiate between these types of variations in order to understand and improve a process.

Suggested literature

• Goedhart, R., & Woodall, W. H. (2022). Monitoring proportions with two components of common cause variation. Journal of Quality Technology, 54(3), 324-337.
• Benneyan, J. C., Lloyd, R. C., & Plsek, P. E. (2003). Statistical process control as a tool for research and healthcare improvement. BMJ Quality & Safety, 12(6), 458-464.
• Stoecklein, M., & Director, N. (2015). Understanding and Misunderstanding Variation in Healthcare: Case Study.