Statistical Process Control (SPC) is a methodology used to control and improve the quality of a process by using data and statistical analysis. One of the key tools used in SPC is the control chart. A control chart is a graphical representation of process data that helps to identify patterns, trends, and variations in a process. In this article, we will discuss control chart limits, patterns, common cause variation, and special cause variation associated with SPC charts.

Control Chart Limits

The control chart limits are used to determine whether a process is within or outside of control. The control chart limits are calculated based on the data collected from the process.

Upper Control Limit (UCL)

The UCL is the upper limit of the control chart. It represents the maximum value that the process can produce without being considered out of control. The UCL is calculated as the average of the data plus three standard deviations.

UCL = X̅ + 3σ

Lower Control Limit (LCL)

The LCL is the lower limit of the control chart. It represents the minimum value that the process can produce without being considered out of control. The LCL is calculated as the average of the data minus three standard deviations.

LCL = X̅ – 3σ

Centerline (CL)

The centerline is the average of the data collected from the process.

CL = X̅

By plotting the data on a control chart with the UCL, LCL, and CL, it is possible to determine whether the process is within or outside of control.

Patterns on Control Charts

There are several patterns that can be identified on a control chart. These patterns can provide valuable insights into the process and help to identify sources of variation.

  1. Run A run is a pattern of data that occurs over time. A run can be an upward or downward trend, a steady level, or a random pattern.
  2. Cyclic A cyclic pattern is a repeating pattern that occurs over a set period of time. A cyclic pattern can be caused by seasonal variations or other periodic events.
  3. Trend A trend is a pattern of data that shows a steady increase or decrease over time. A trend can be caused by changes in the process, equipment, or raw materials.
  4. Oscillation An oscillation pattern is a pattern of data that shows a regular up-and-down movement. An oscillation pattern can be caused by changes in the process, equipment, or raw materials.
  5. Shift A shift is a sudden change in the data. A shift can be caused by changes in the process, equipment, or raw materials.

Common Cause Variation

Common cause variation is a type of variation that is inherent in the process. It is caused by factors that are present in the process all the time and cannot be controlled.

Common cause variation is also known as random variation or noise. It is represented on the control chart by data that falls within the control limits.

Common cause variation is predictable and can be used to determine the normal behavior of the process. By monitoring the common cause variation, it is possible to identify when the process is moving outside of the control limits.

Special Cause Variation

Special cause variation is a type of variation that is caused by factors that are not present in the process all the time. Special cause variation is also known as assignable cause variation.

Special cause variation is represented on the control chart by data that falls outside of the control limits or shows a pattern that is not typical of the process.

Special cause variation is unpredictable and can be caused by a variety of factors, such as a change in the process, equipment, or raw materials, or an error in the data collection or analysis.

When special cause variation is identified, it is important to investigate the cause and take corrective action to bring the process back into control. This may involve identifying and addressing the root cause of the variation, making changes to the process or equipment, or retraining personnel.

Differentiating Between Common Cause and Special Cause Variation

Differentiating between common cause and special cause variation is essential for understanding the behavior of a process and making data-driven decisions.

One way to differentiate between common cause and special cause variation is to use the control chart limits. Data that falls within the control limits is considered to be caused by common cause variation, while data that falls outside of the control limits is considered to be caused by special cause variation.

Another way to differentiate between common cause and special cause variation is to use a run chart. A run chart is a graph that shows the data collected over time without the control limits. By examining the data on the run chart, it is possible to identify patterns that may indicate special cause variation.

It is important to note that identifying special cause variation does not necessarily mean that the process is out of control. It simply means that the variation is not typical of the process and requires investigation to determine the cause.

Conclusion

Control chart limits, patterns, common cause variation, and special cause variation are important concepts associated with Statistical Process Control (SPC) charts. Control chart limits are used to determine whether a process is within or outside of control, and patterns on control charts can provide valuable insights into the process. Common cause variation is inherent in the process and is predictable, while special cause variation is caused by factors that are not typical of the process and is unpredictable. Differentiating between common cause and special cause variation is essential for understanding the behavior of a process and making data-driven decisions.

By using SPC charts and understanding these concepts, organizations can monitor and improve their processes, reduce waste, and increase efficiency and quality. SPC is a powerful methodology that can help organizations to achieve their business goals and remain competitive in today’s marketplace.