Control Chart | Types of Control Charts | Examples

What is a Control Chart?

→ A Control Chart is a statistical tool.

→ It is used to study how a process changes over time.

→ It helps us to identify between process variation resulting from a common cause and a special cause.

→ The Control Chart is the most important tool of 7 QC Tools.

→ The Control Chart is widely used in the Lean Six Sigma Project globally.

Table of Contents:

• What is a Control Chart?
• When to Use Control Chart?
• Why Use a Control Chart?
• Control Chart Uses
• History
• Key Components of Control Charts
• Principles of variation
• Common cause
• Special cause
• Classification of Control Charts
• Example of Control Chart
• How to Make a Control Chart?
• Interpretation of the Control Chart
• Relation Between Process Stability and Capability
• Process Stability
• Process Capability
• Advantages of Control Chart
• Limitations of Control Chart
• Benefits of Control Chart
• Conclusion

When to Use Control Chart?

• Monitoring ongoing production processes
• After implementing process changes
• Detecting variability and trends
• Quality control in manufacturing
• When establishing baselines
• Routine process monitoring

Why to Use a Control Chart?

• Early detection of issues
• Distinguishing between common cause and special cause variation
• Monitoring process stability
• Supporting continuous improvement
• Enhanced quality control
• Resource optimization
• Employee engagement and empowerment

Control Chart Uses:

→ Control charts are versatile tools and those are used in various scenarios to monitor, control, and improve processes.

→ It is used to predict the performance of the manufacturing process.

→ Find out the special causes and common causes within the process.

→ Identify the trend of the process.

→ We can review process variation with time for the manufacturing and non-manufacturing processes with this chart.

⏩Control Chart Use to Monitor Manufacturing Processes Such as:

• Defects Rate,
• Production Time,
• Inventory on Hand,
• Cycle Time
• Cost per Unit, etc

⏩Control Chart Use to Monitor Non-Manufacturing Processes Such as:

• Billing Errors
• Missed Appointments,
• Customer Support Calls,
• Bill Payment Dues,
• Days Between Billing and Payment,
• Expenses,
• On-time Delivery Failure,
• Unplanned Absences, etc.

→ They are fundamental tools in Statistical Process Control (SPC).

→ And used to monitor the quality and variation of processes.

History:

→ Control Chart was invented by Dr. Walter A. Shewhart working for Bell Labs in the 1920s.

→ So It is called "Shewhart Charts".

→ It is also known as a process behavior chart.

→ In the 1920s, Shewhart worked on improving the reliability of the manufacturing process for telephone equipment.

→ The engineers had already realized the importance of reducing variation in manufacturing operations.

→ He introduced the idea that a process could be in statistical control or not in control.

→ Also, he has given the concept of common causes variation and special causes variation.

→ In 1931, Shewhart published a book, "Economic Control of Quality of Manufactured Product." 

→ This book has set the foundation for modern quality control.

→ This concept was further developed and popularized worldwide by W. Edwards Deming.

Key Components of Control Charts:

⏩The key components of control charts are:

1. Center Line (CL)
2. Control Limits
3. Data Points
4. Zones

1. Center Line (CL):

→ The Center Line represents the average or mean of the process data.

2. Control Limits:

→ Two types of control limits are (1) Upper Control Limit (UCL) and (2) Lower Control Limit (LCL).

→ Generally, control limits are set at ±3 standard deviations from the center line.

→ These limits help identify when the process is going out of control.

3. Data Points:

→ Data points are the individual readings, records, measurements, or observations which are plotted on the chart.

→ Over a period plotting data points we can monitor the process.

4. Zones:

→ Zones are the area between the control limits and the center line.

→ Zones are divided into two and that helps interpret the chart.

Principles of variation:

→ All the processes have variations as per the 6M changes such as Man, Machine, Material, Method, Measurement, and Mother Nature (Environment) changes.

→ Machines have wear, tear, malfunction, etc.

→ Control Charts measure the variation of the process.

→ So we can easily interpret it after plotting into the graph.

→ Also, we can easily say whether the process variation is within an acceptable limit or not?

→ Now we learn about the principles of variation of any process.

→ Every process has variation.

→ The more variation, the more loss to the Organization.

→ The variation has a certain pattern.

→ After identifying the pattern we can take appropriate action to eliminate the cause of the variation.

→ So the actions depend on the types of pattern and cause identified.

→ This way we can reduce the loss of the business and improve our process.

⏩Two types of causes are responsible for the variation that are:

1. Common cause
2. Special cause

1. Common Cause:

→ "Common cause is fluctuation caused by unknown factors resulting in a steady but random distribution of output around the average of the data."

→ e.g. the rubbing effect of matting parts like gears, bearings, etc...

2. Special Cause:

→ "Special cause is caused by known factors that result in a non-random distribution of output"

→ e.g. machine breakdown, accident, etc...

Classification of Control Charts:

→ There are many types of charts available in Statistical Process Control.

⏩The most common types of control charts are:

• X Bar & Range “R” Chart
• Standard Deviation “S” & Range “R” Chart
• I-MR Chart
• “u” Chart
• “c” Chart
• “p” Chart
• “np” Chart

⏩The classification of these charts depends on the below parameters:

• Nature of recorded data type such as variable or attribute
• The number of samples is available in each subgroup or we can say subgroup size
• Focus on defects (occurrence) or defectives (pieces or units)
• The subgroup size is equal or not?

→ The main two types of charts are variable and attribute charts.

→ Classification of the Control Chart is mentioned below.

→ For better understanding refer below picture.

Example of Control Chart:

→ In this example, we will learn how to make a control chart.

→ Now we will take an example of the most common (X-Bar, R chart).

→ We will learn step by step, with one example of the manufacturing process variation.

→ For this example we are taking variable data and subgroup size=5 as per the classification mentioned above.

→ We can easily make (X-Bar, R chart) as explained below.

How to Make a Control Chart?

→ We can easily make this with 7 simple steps.

⏩Seven steps for making a control chart are:

1. Collect the data
2. Calculate the subgroup average
3. Determine the overall average
4. Calculate the range
5. Compute the average of the range
6. Calculate the control limit
7. Plotting of data in graph

Step 1: Collect the Data:

→ The first step is to collect data from the manufacturing process.

→ Record the readings and stratify it into subgroups as per our sampling plan.

→ In our example the subgroup size is 5.

→ So we will take 5 samples at a time during data collection.

Step 2: Calculate the Subgroup Average:

→ Now after data collection, we need to calculate the average of the subgroup.

→ So we will find the individual subgroup's average as per the formula mentioned in the below picture.

→ We need to apply a simple average formula.

Step 3: Determine the Overall Average X-double Bar:

→ So after finding the individual subgroup's average, now we need to find out the overall average of all subgroups.

→ Refer to the below-mentioned formula of the overall average.

Step 4: Calculate the Subgroup Range (R):

→ In the fourth step, we will find the individual subgroup's range.

→ Range is the simple difference between the highest and lowest numbers.

→ For calculating the subgroup range we will use the range formula.

Step 5: Calculate the Average Range (R-bar):

→ Now after finding the range, we need to find the average of the range.

→ So in the fifth step, we will find the average of the range.

→ For this we will use a simple average formula.

Step 6: Calculate the Control Limit:

→ Till now, we have calculated the various parameters based on the data collected and formulas.

→ Now in the sixth step, we will find the control limit of the X-bar and R-chart.

→ As per the below formulas, we will find out the control limits of the X-bar and R chart.

→ Different constant values are mentioned below pictures which are very important for the Graph.

→ The constants depend on the subgroup size.

→ As the subgroup size changes the constant value also changes.

→ The source of this constant value is the AIAG-SPC  handbook.

Step 7: Plotting of Data in Graph:

→ Plot the vertical axis as X-Bar and R values and the horizontal axis as subgroup numbers.

→ Draw the central line as an X-double bar and R-bar.

→ Draw all control limits UCL & LCL as calculated in the previous step.

→ Plot the X-bar and R values and join the points.

→ Write necessary items like the name of the operation, process, product, size of the subgroup, work conditions, shift, etc.

→ Refer to the below picture for an example of a control chart for your better understanding.

Interpretation of the Control Chart:

→ With the help of the control chart we can identify the special cause variation and common cause variation of the process.

→ Based on that we can take action and eliminate special causes and reduce the variation at the minimum level.

→ If all data points fall within the UCL and LCL, then the process is in control.

→ If one or more data points fall outside the UCL or LCL, then it indicates a special cause.

→ A run of seven or more points consistently above or below the center line suggests a systematic shift in the process.

→ A series of points that show a consistent upward or downward trend indicates a potential issue.

→ Study our article where we have explained The Eight Rules of Control Chart Interpretation with examples, patterns, and trends.

Relation Between Process Stability and Capability:

→ Process Stability and Process Capability are the most important concepts in interpretation.

→ Below we have explained both concepts.

Process Stability:

→ Process Stability refers to the consistency of the process to stay within the Control Limits.

→ If the process distribution remains consistent over time then we can say that the process is stable or in control.

→ If the Outputs are spread outside the limits, then we can say that the process is unstable or out of control.

Process Capability:

→ Process Capability is a measure of the ability of the process to meet customer specifications.

→ It is the measure that tells how good each individual output is.

→ An estimation of the ppm is a method to measure process capability.

→ PPM means defective parts per million.

→ Process Capability analysis uses measures like Cp, Cpk, Pp, and Ppk to determine the process capability.

Advantages of Control Chart:

→ It gives information about the common causes and special causes.

→ This chart also helps in determining whether the process is capable or not & stable or not?

→ It helps in predicting operation performance.

→ It makes it possible to implement substantial Quality Improvement.

Benefits of Control Chart:

• Early detection of problems
• Process improvement
• It ensures consistent quality by monitoring process performance over time
• It is very useful for decision-making.
• Continuous Improvement tool
• Quality improvement and cost reduction
• Enhanced customer satisfaction
• Improve plant efficiency and effectiveness

Limitations of Control Chart:

• Control charts frequently assume that the data follows a normal distribution
• Measurement errors can affect on interpretation
• The effectiveness of control charts depends on the accuracy of the data collected
• Time and resource-intensive

Conclusion:

→ Control charts are a fundamental tool in quality control and process management.

→ It helps in monitoring, controlling, and improving processes.

→ Control charts can detect variations and trends.

→ This ability helps organizations in different ways such as: (1) maintaining process stability, (2) enhancing product quality, (3) driving continuous improvement, etc.

→ Apart from the above benefits, they have some limitations as well.

→ We can easily reduce the limitation of the control chart by integrating it with other quality management practices.

→ The organizations can get benefits by using control charts such as: (1) achieving greater process control, (2) higher quality products, and services, (3) overall operational excellence, etc.