What is Control Charts in Six Sigma

Apr 28

3 min read

TL;DR

Control charts track process stability and performance over time.
They are critical in the Control phase of the Six Sigma DMAIC cycle.
Key elements include Upper Control Limit (UCL), Lower Control Limit (LCL), and Center Line (CL).
Control charts help distinguish between common cause and special cause variation.
When used correctly, they prevent unnecessary corrections and maintain process excellence.

What Are Control Charts in Six Sigma?

Control charts are a fundamental tool in the Six Sigma methodology, particularly during the Control phase of DMAIC (Define, Measure, Analyze, Improve, Control). They provide a visual way to monitor a process over time and determine if it remains within acceptable limits.

A basic control chart includes:

Center Line (CL): The average of the data.
Upper Control Limit (UCL): The threshold above which variation is considered abnormal.
Lower Control Limit (LCL): The threshold below which variation is considered abnormal.

By plotting process data points on a graph with these limits, teams can quickly spot when a process goes "out of control," signaling the need for corrective action.

How to Use Control Charts Effectively in Six Sigma Projects

Using control charts effectively requires more than just plotting data. Here's how Six Sigma practitioners typically approach it:

Step 1: Select the Right Type of Control Chart Depending on the data type:

X-bar and R Chart: For continuous data like measurements.
p-Chart: For attribute data like defect rates.
c-Chart: For counting the number of defects per unit.

Step 2: Collect Consistent Data Ensure that measurements are taken under similar conditions and at regular intervals to maintain data integrity.

Step 3: Plot Data Points and Control Limits Use historical data to calculate the mean, UCL, and LCL. Plot new data points over time to monitor process behavior.

Step 4: Interpret the Chart Look for trends, shifts, or points outside the control limits. If spotted, investigate and address the root cause.

Example: A Six Sigma team at an electronics manufacturer used an X-bar chart to monitor soldering temperature. When temperatures suddenly exceeded UCLs, they found a faulty heating element, avoiding costly product failures.

Pro Tip: The Western Electric Rules offer detailed guidelines for interpreting patterns within control charts, improving accuracy.

Benefits of Using Control Charts in the Six Sigma Process

Implementing control charts provides multiple strategic advantages:

1. Early Detection of Problems Control charts highlight variations immediately, enabling corrective actions before defects reach the customer.

2. Reduction of Overcorrections Without control charts, teams might react to normal fluctuations unnecessarily. Charts help differentiate "common cause" and "special cause" variations.

3. Continuous Improvement Culture By consistently monitoring processes, organizations reinforce a data-driven approach to quality management.

4. Better Resource Allocation Control charts help prioritize improvements by identifying where processes are genuinely unstable.

Example: A pharmaceutical company introduced p-charts to track tablet coating defects. It helped them shift from frequent line stoppages to targeted interventions, reducing downtime by 18% in just three months.

Real-World Stat: A study from NIST.gov reports that manufacturers using statistical process control methods like control charts achieve up to 30% faster cycle times.

Real-World Application: Control Charts in Action

Let's break down a real-world case of using control charts within Six Sigma:

Problem: Increased customer complaints about order processing times at a logistics company.

Six Sigma DMAIC Approach:

Define: Customers report late deliveries.
Measure: Track order processing times.
Analyze: Identify variation and potential bottlenecks.
Improve: Implement better staff scheduling and upgraded software.
Control: Use an X-bar control chart to monitor processing times post-improvement.