Welcome to the ultimate guide to Six Sigma control charts, where we explore the power of statistical process control and how it can help organizations improve quality, reduce defects, and increase profitability. Control charts are essential tools in the Six Sigma methodology, visually representing process performance over time and highlighting when a process is out of control.
In this comprehensive guide, we’ll delve into the different types of control charts, how to interpret them, how to use them to make data-driven decisions, and how to become a Lean Six Sigma expert.
Let’s get started on the journey to discover the transformative potential of Six Sigma control charts.
What is a Control Chart?
A control chart is a statistical tool used in quality control to monitor and analyze process variation. No process is free from variation, and it is vital to understand and manage this variation to ensure consistent and high-quality output. The control chart is designed to help visualize this variation over time and identify when a process is out of control.
The chart typically includes a central line, which represents the average or mean of the process data, and upper and lower control limits, which are set at a certain number of standard deviations from the mean. The control limits are usually set at three standard deviations from the mean, encompassing about 99.7 percent of the process data. If the process data falls within these control limits, the process is considered in control, and variation is deemed to be coming from common causes. If the data points fall outside these control limits, this indicates that there is a special cause of variation, and the process needs to be investigated and improved.
Control charts are commonly used in manufacturing processes to ensure that products meet quality standards, but they can be used in any process where variation needs to be controlled. They can be used to track various types of process data, such as measurements of product dimensions, defect rates, or cycle times.
Significance of Control Charts in Six Sigma
Control charts are an essential tool in the Six Sigma methodology to monitor and control process variation. Six Sigma is a data-driven approach to process improvement that aims to minimize defects and improve quality by identifying and eliminating the sources of variation in a process. The control chart helps to achieve this by providing a visual representation of the process data over time and highlighting any special causes of variation that may be present.
The Objective of Six Sigma Control Charts
The primary objective of using a control chart in Six Sigma is to ensure that a process is in a state of statistical control. This means that the process is stable and predictable, and any variation is due to common causes inherent in the process. The control chart helps to achieve this by providing a graphical representation of the process data that shows the process mean and the upper and lower control limits. The process data points should fall within these limits if the process is in control.
Detecting Special Cause Variation
One of the critical features of a Six Sigma control chart is its ability to detect special cause variation, also known as assignable cause variation. Special cause variation is due to factors not inherent in the process and can be eliminated by taking corrective action. The control chart helps detect special cause variation by highlighting data points outside control limits.
Estimating Process Average and Variation
Another objective of a control chart is to estimate the process average and variation. The central line represents the process average on the chart, and the spread of the data points around the central line represents the variation. By monitoring the process over time and analyzing the control chart, process improvement teams can gain a deeper understanding of the process and identify areas for improvement.
Measuring Process Capability with Cp and Cpk
Process capability indices, such as Cpk and Cp, help to measure how well a process can meet the customer’s requirements. Here are some details on how to check process capability using Cp and Cpk:
- Cp measures a process’s potential capability by comparing the data’s spread with the process specification limits.
- If Cp is greater than 1, it indicates that the process can meet the customer’s requirements.
- However, Cp doesn’t account for any process shift or centering, so it may not accurately reflect the process’s actual performance.
- Cpk measures the actual capability of a process by considering both the spread of the data and the process’s centering or shift.
- Cpk is a more accurate measure of a process’s performance than Cp because it accounts for both the spread and centering.
- A Cpk value of at least 1.33 is typically considered acceptable, indicating that the process can meet the customer’s requirements.
It’s important to note that while Cp and Cpk provide valuable information about a process’s capability, they don’t replace the need for Six Sigma charts and other statistical tools to monitor and improve process performance.
Steps to Create a Six Sigma Control Chart
To create a Six Sigma chart, you can follow these general steps:
- Gather Data: Collect data related to the process or product you want to monitor and improve.
- Determine Data Type: Identify the type of data you have, whether it is continuous, discrete, attribute, or variable.
- Calculate Statistical Measures: Calculate basic statistical measures like mean, standard deviation, range, etc., depending on the data type.
- Set Control Limits: Determine the Upper Control Limit (UCL) and Lower Control Limit (LCL) using statistical formulas and tools.
- Plot Data: Plot the data points on the control chart, and draw the control limits.
- Analyze the Chart: Analyze the chart to identify any special or common causes of variation, and take corrective actions if necessary.
- Update the Chart: Continuously monitor the process and update the chart with new data points.
You can use software tools like Minitab, Excel, or other statistical software packages to create a control chart. These tools will automate most of the above steps and help you easily create a control chart.
Know When to Use Control Charts
A Six Sigma control chart can be used to analyze the Voice of the Process (VoP) at the beginning of a project to determine whether the process is stable and predictable. This helps to identify any issues or potential problems that may arise during the project, allowing for corrective action to be taken early on. By analyzing the process data using a control chart, we can also identify the cause of any variation and address the root cause of the issue.
Here are some specific scenarios when you may want to use a control chart:
- At the start of a project: A control chart can help you establish a baseline for the process performance and identify potential areas for improvement.
- During process improvement: A control chart can be used to track the effectiveness of changes made to the process and identify any unintended consequences.
- To monitor process stability: A control chart can be used to verify whether the process is stable. If the process is unstable, you may need to investigate and make necessary improvements.
- To identify the source of variability: A control chart can help you identify the source of variation in the process, allowing you to take corrective actions.
Four Process States in a Six Sigma Chart
Control charts can be used to identify four process states:
- The Ideal state: The process is in control, and all data points fall within the control limits.
- The Threshold state: Although data points are in control, there are some non-conformances over time.
- The Brink of Chaos state: The process is in control but is on the edge of committing errors.
- Out of Control state: The process is unstable, and unpredictable non-conformances happen. In this state, it is necessary to investigate and take corrective actions.
What are the Different Types of Control Charts in Six Sigma?
Control charts are an essential tool in statistical process control, and the type of chart used depends on the data type. There are different types of control charts, and the type used depends on the data type.
The seven Six Sigma chart types include: I-MR Chart, X Bar R Chart, X Bar S Chart, P Chart, NP Chart, C Chart, and U Chart. Each chart has its specific use and is suitable for analyzing different data types.
The I-MR Chart, or Individual-Moving Range Chart, analyzes one process variable at a time. It is suitable for continuous data types and is used when the sample size is one. The chart consists of two charts: one for individual values (I Chart) and another for the moving range (MR Chart).
X Bar R Chart
The X Bar R Chart is used to analyze process data when the sample size is more than one. It consists of two charts: one for the sample averages (X Bar Chart) and another for the sample ranges (R Chart). It is suitable for continuous data types.
X Bar S Chart
The X Bar S Chart is similar to the X Bar R Chart but uses the sample standard deviation instead of the range. It is suitable for continuous data types. It is used when the process data is normally distributed, and the sample size is more than one.
The P Chart, or the Proportion Chart, is used to analyze the proportion of nonconforming units in a sample. It is used when the data is binary (conforming or nonconforming), and the sample size is large.
The NP Chart is similar to the P Chart but is used when the sample size is fixed. It monitors the number of nonconforming units in a sample.
The C Chart, also known as the Count Chart, is used to analyze the number of defects in a sample. It is used when the data is discrete (count data), and the sample size is large.
The U Chart, or the Unit Chart, is used to analyze the number of defects per unit in a sample. It is used when the sample size is variable, and the data is discrete.
Factors to Consider while Selecting the Right Six Sigma Chart Type
Selecting the proper Six Sigma control chart requires careful consideration of the specific characteristics of the data and the intended use of the chart. One must consider the type of data being collected, the frequency of data collection, and the purpose of the chart.
Continuous data requires different charts than attribute data. An individual chart may be more appropriate than an X-Bar chart if the sample size is small. Similarly, if the data is measured in subgroups, an X-Bar chart may be more appropriate than an individual chart. Whether monitoring a process or evaluating a new process, the process can also affect the selection of the appropriate control chart.
How and Why a Six Sigma Chart is Used as a Tool for Analysis
Control charts help to focus on detecting and monitoring the process variation over time. They help to keep an eye on the pattern over a period of time, identify when some special events interrupt normal operations, and reflect the improvement in the process while running the project. Six Sigma control charts are considered one of the best tools for analysis because they allow us to:
- Monitor progress and learn continuously
- Quantify the capability of the process
- Evaluate the special causes happening in the process
- Separate the difference between the common causes and special causes
Benefits of Using Control Charts
- Early warning system: Control charts serve as an early warning system that helps detect potential issues before they become major problems.
- Reduces errors: By monitoring the process variation over time, control charts help identify and reduce errors, improving process performance and quality.
- Process improvement: Control charts allow for continuous monitoring of the process and identifying areas for improvement, resulting in better process performance and increased efficiency.
- Data-driven decisions: Control charts provide data-driven insights that help to make informed decisions about the process, leading to better outcomes.
- Saves time and resources: Six Sigma control charts can help to save time and resources by detecting issues early on, reducing the need for rework, and minimizing waste.
Who Can Benefit from Using Six Sigma Charts
- Manufacturers: Control charts are widely used in manufacturing to monitor and control process performance, leading to improved quality, increased efficiency, and reduced waste.
- Service providers: Service providers can use control charts to monitor and improve service delivery processes, leading to better customer satisfaction and increased efficiency.
- Healthcare providers: Control charts can be used in healthcare to monitor and improve patient outcomes and reduce medical errors.
- Project managers: Project managers can use control charts to monitor and improve project performance, leading to better project outcomes and increased efficiency.
Some Six Sigma Control Chart Tips to Remember
Here are some tips to keep in mind when using Six Sigma charts:
- Never include specification lines on a control chart.
- Collect data in the order of production, not from inspection records.
- Prioritize data collection related to critical product or process parameters rather than ease of collection.
- Use at least 6 points in the range of a control chart to ensure adequate discrimination.
- Control limits are different from specification limits.
- Points outside the control limits indicate special causes, such as shifts and trends.
- Points inside the limits indicate trends, shifts, or instability.
- A control chart serves as an early warning system to prevent a process from going out of control if no preventive action is taken.
- Assume LCL as 0 if it is negative.
- Use two charts for continuous data and a single chart for discrete data.
- Don’t recalculate control limits if a special cause is removed and the process is not changing.
- Consistent performance doesn’t necessarily mean meeting customer expectations.
What are Control Limits?
Control limits are an essential aspect of statistical process control (SPC) and are used to analyze the performance of a process. Control limits represent the typical range of variation in a process and are determined by analyzing data collected over time.
Control limits act as a guide for process improvement by showing what the process is currently doing and what it should be doing. They provide a standard of comparison to identify when the process is out of control and needs attention. Control limits also indicate that a process event or measurement is likely to fall within that limit, which helps to identify common causes of variation. By distinguishing between common causes and special causes of variation, control limits help organizations to take appropriate action to improve the process.
Calculating Control Limits
The 3-sigma method is the most commonly used method to calculate control limits.
Step 1: Determine the Standard Deviation
The standard deviation of the data is used to calculate the control limits. Calculate the standard deviation of the data set.
Step 2: Calculate the Mean
Calculate the mean of the data set.
Step 3: Find the Upper Control Limit
Add three standard deviations to the mean to find the Upper Control Limit. This is the upper limit beyond which a process is considered out of control.
Step 4: Find the Lower Control Limit
To find the Lower Control Limit, subtract three standard deviations from the mean. This is the lower limit beyond which a process is considered out of control.
Importance of Statistical Process Control Charts
Statistical process control charts play a significant role in the Six Sigma methodology as they enable measuring and tracking process performance, identifying potential issues, and determining corrective actions.
Six Sigma control charts allow organizations to monitor process stability and make informed decisions to improve product quality. Understanding how these charts work is crucial in using them effectively. Control charts are used to plot data against time, allowing organizations to detect variations in process performance. By analyzing these variations, businesses can identify the root causes of problems and implement corrective actions to improve the overall process and product quality.
How to Interpret Control Charts?
Interpreting control charts involves analyzing the data points for patterns such as trends, spikes, outliers, and shifts.
These patterns can indicate potential problems with the process that require corrective actions. The expected behavior of a process on a Six Sigma chart is to have data points fluctuating around the mean, with an equal number of points above and below. This is known as a process shift and common cause variation. Additionally, if the data is in control, all data points should fall within the upper and lower control limits of the chart. By monitoring and analyzing the trends and outliers in the data, control charts can provide valuable insights into the performance of a process and identify areas for improvement.
Elements of a Control Chart
Six Sigma control charts consist of three key elements.
- A centerline representing the average value of the process output is established.
- Upper and lower control limits (UCL and LCL) are set to indicate the acceptable range of variation for the process.
- Data points representing the actual output of the process over time are plotted on the chart.
By comparing the data points to the control limits and analyzing any trends or patterns, organizations can identify when a process is going out of control and take corrective actions to improve the process quality.
What is Subgrouping in Control Charts?
Subgrouping is a method of using Six Sigma control charts to analyze data from a process. It involves organizing data into subgroups that have the greatest similarity within them and the greatest difference between them. Subgrouping aims to reduce the number of potential variables and determine where to expend improvement efforts.
- The range represents the within-subgroup variation.
- The R chart displays changes in the within-subgroup dispersion of the process.
- The R chart determines if the variation within subgroups is consistent.
- If the range chart is out of control, the system is not stable, and the source of the instability must be identified.
- The difference in subgroup averages represents between-subgroup variation.
- The X Bar chart shows any changes in the average value of the process.
- The X Bar chart determines if the variation between subgroup averages is greater than the variation within the subgroup.
X Bar Chart Analysis
- If the X Bar chart is in control, the variation “between” is lower than the variation “within.”
- If the X Bar chart is not in control, the variation “between” is greater than the variation “within.”
- The X Bar chart analysis is similar to the graphical analysis of variance (ANOVA) and provides a helpful visual representation to assess stability.
Benefits of Subgrouping in Six Sigma Charts
- Subgrouping helps identify the sources of variation in the process.
- It reduces the number of potential variables.
- It helps determine where to expend improvement efforts.
- Subgrouping ensures consistency in the within-subgroup variation.
- It provides a graphical representation of variation and stability in the process.
Master the Knowledge of Control Charts For a Successful Career in Quality Management
Control charts are a powerful tool for process improvement in the Six Sigma methodology. By monitoring process performance over time, identifying patterns and trends, and taking corrective action when necessary, organizations can improve their processes and increase efficiency, productivity, and quality. Understanding the different types of control charts, their components, and their applications is essential for successful implementation.
A crystal-clear understanding of Six Sigma control charts is essential for aspiring Lean Six Sigma experts because it allows them to understand how to monitor process performance and identify areas of improvement. By understanding when and how to use control charts, Lean Six Sigma experts can effectively identify and track issues within a process and improve it for better performance.
Getting a Six Sigma certification is an excellent way for an aspiring Lean Six Sigma Expert to gain the necessary skills and knowledge to excel in the field. A certification from a prestigious institute like the University of Massachusetts Amherst can demonstrate your competence and proficiency in the field to employers and increase your chances of success in the job market. Additionally, Six Sigma certification can provide you with the tools you need to stay on top of the latest developments in the field, which can help you stay ahead of the competition.