In the Lean Six Sigma framework, recognizing and understanding patterns of variation is crucial for both process adjustment and improvement. Variation is inherent in any process, whether it's manufacturing, service delivery, or any operational activity. Identifying the patterns of variation helps in diagnosing the underlying causes and implementing effective solutions to improve process performance. This article delves into how recognizing patterns of variation plays a pivotal role in process adjustment and improvement efforts.

Patterns of Variation

Patterns of variation refer to the way data points fluctuate around the mean or expected value of a process over time. These patterns can be categorized into two main types: common cause variation and special cause variation.

• Common Cause Variation: Also known as natural variation, this type is inherent in the process and exists due to the system's design. It is predictable and stable over time, indicating that the process is under control but may need improvement to reduce variability.
  

• Special Cause Variation: This variation is due to identifiable factors or changes in the process, environment, or inputs. It is unpredictable and signals that the process is out of control. Identifying and eliminating special cause variations are essential for maintaining process stability.
  

Process Adjustment

Process adjustment involves making changes to the process inputs or conditions to correct or eliminate special cause variations. It is a reactive approach that aims to bring the process back into a state of control. For instance, if a machine starts producing parts outside of the specification limits due to a worn-out tool, replacing the tool would be a process adjustment. The key to effective process adjustment lies in accurately identifying the special cause variations and understanding their impact on the process.

Process Improvement

Process improvement, on the other hand, focuses on reducing or eliminating common cause variation to enhance the process's overall performance. This requires a more strategic approach, often involving the redesign of the process or system to achieve higher efficiency, quality, and consistency. Lean Six Sigma tools such as DMAIC (Define, Measure, Analyze, Improve, Control) are used to systematically identify, analyze, and improve common cause variations. Process improvement efforts aim to shift the process mean closer to the target value and reduce variability without merely reacting to defects or errors as they occur.

Recognizing Patterns of Variation

Effective process adjustment and improvement depend on the ability to recognize patterns of variation. Control charts, a fundamental tool in the Lean Six Sigma methodology, are instrumental in this regard. By plotting data points over time, control charts help distinguish between common cause and special cause variations, allowing practitioners to decide whether to adjust the process or seek improvement opportunities.

• For Process Adjustment: When a control chart indicates a special cause variation, such as a data point outside the control limits or a non-random pattern, it signals the need for immediate process adjustment to rectify the issue.
  

• For Process Improvement: If the process displays only common cause variations within the control limits but does not meet the desired performance levels, it indicates an opportunity for process improvement.
  

Conclusion

Understanding and managing patterns of variation are critical to the success of Lean Six Sigma projects. While process adjustment is necessary for dealing with special cause variations and ensuring the process remains in control, process improvement efforts aim to elevate the process performance by addressing common cause variations. Both approaches require a deep understanding of the process dynamics and a commitment to continuous improvement, emphasizing the Lean Six Sigma philosophy of eliminating waste, enhancing quality, and maximizing value.