Control Chart | Types of the Control Chart in 7 QC Tools

Control Chart | Types of the Control Chart in 7 QC Tools | Run Chart

What is the Control Chart in 7 QC Tools?

➝ It is a statistical tool used to differentiate between process variation resulting from a common cause & special cause.
The Control Chart in 7 Basic QC Tools is a type of run chart used for studying the process variation over time.
→ This Chart is classified as per recorded data is variable or attribute.
 It is a type of run chart used for studying the process variation over time.

➤ History of Control Chart in 7 QC Tools:

➝ The Control Chart was invented by Dr. Walter A. Shewhart working for Bell Labs in the 1920s.
➝ So this is called as a Shewhart Control Charts.
➝ The company's engineers had been seeking to improve the reliability of their telephony transmission systems.
➝ Because amplifiers and other equipment had to be buried underground, there was a stronger business needs to reduce the frequency of failures and repairs
➝ By 1920, the engineers had already realized the importance of reducing variation in the manufacturing process.
➝ Dr. Walter A. Shewart Published the book called “Economic Control of Quality of Manufactured Product” in 1931.

Principles of variation in Control Chart:

➝ Every process has variation.
➝ More the variation in the process, more the loss to the Organization.

➤ Two types of cases available for variation.

  1. A common cause of variation
  2. A special cause of variation
➝ Action on variation entirely depends on the type of cause identified.

➥ [A] Common Cause of variation:

➝ "Common cause variation 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 part like gears, bearings, etc...

➥ [B] Special Cause of variation:

➝ "Special cause variation is caused by known factors that result in a non-random distribution of output"
➝ e.g. machine breakdown, accident, etc...

➤ Types of data monitoring for Control Chart in 7 QC Tools:

→ There are two types of data set available.
     [A] Variable data :
Variable data can be measured.
→ e.g. Weight, Height, Length, Hardness, Diameter, Angle
     [B] Attribute data :
→ Attribute data that can be counted or can give an answer in Yes/ No, Go/No Go,
     OK/Not OK or Pass/Fail
→ e.g. aesthetic look of product ok or not ok

➤ Types of the Control Chart in 7 QC Tools:

→ The Classification is mentioned below:

Classification of the Control Chart in 7 QC Tools

Eight Easy Steps for constructing the Control Chart:

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

→ We can easily construct (X-Bar, R chart) in simple 8 steps:

  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 for X-bar and R chart.
  7. Plot the data in the graph.
  8. Interpret the Graph.

➥ Step 1: Collect the data:

→ Collect and stratify data into subgroups.

➥ Step 2: Calculate the subgroup average:

→ In the Second step, we find the individual sub group's average as per mentioned formula

➥ Step 3: Determine the overall average X-double bar:

→ Here we find the overall average by using all sub group's individual average.

Step1 of Control Chart in 7 QC Tools .jpeg

➥ Step 4: Calculate the subgroup Range (R):

→ In the fourth step, we find the individual sub group's range as per the mentioned formula.

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

→ Here we find out the average range of all individual subgroup's range.

Step2 of Control Chart in 7 QC Tools .jpeg

➥ Step 6: Calculate the control limit for X-bar and R chart:

→ In this step, we find the limit of X-bar and R chart with below-mentioned formula.

Step3 of Control Chart in 7 QC Tools .jpeg

→ Constants  for the Graph:

Constant value  for the Control chart in 7 QC Tools

➥ Step 7: Plot of the data:

→ Vertical axis: X-Bar and R values.
→ Horizontal axis: subgroup number.
→ Draw the central line: X-double bar and R-bar
→ Draw all control limits UCL & LCL.
→ Plot the X-Bar and R values and join the points.
→ Write necessary items like the name of the process, product, size of the subgroup, work conditions, shift, etc.

    ➥ [A] Example of X-Bar and R Chart:

    Example of X-Bar Chart and R Chart

    ➥ Step 8: Interpret the Graph:

    [A] Process stability 

    ➝ Look at the pattern of variation.
     It should be random and not a systematic pattern.
    ➝ Look for the presence of special causes.
    ➝ 8 rules of special cause identification e.g. are our process enough to continuously meet the customer's specification?

    [B] Process capability

    ➝ Establish process variation
    ➝ Compare with specification and establish process capability e.g. are our process capable enough to achieve customer's specification?

    Benefits of the Control Chart in 7 QC Tools:

    ➝ This Chart gives information about common causes of variation and special causes of variation.
    ➝ It also helps in determining whether the Process is capable or not & the process is stable or not?
    ➝ It helps in predicting process performance.
    ➝ This Chart indicates whether the process is in control or not? so, we can get the information about the behavior of the process.
    ➝ It makes possible to implement substantial quality improvement.