## Types of the Control Chart in 7 QC Tools

→ "**The Control Chart"**is classified as per recorded data is variable or attribute?

→ "

**The Control Chart**" is a type of run chart used for studying the process variation over time.

### 1) 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”

➝ “

###
2) History of Control Chart

####
[A]

####
[B]

→ 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] 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?

➝ Establish process variation

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

➝ “

**The****Control Chart****in 7 QC Tools**is a type of run chart used for studying the process variation over time”###
2) 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 the

**Control Chart**also 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.

### 3) Principles of variation in Control Chart in 7 QC Tools:

➝ Every process has variation.

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

➝ Two types of cases available for variation.

➧ A common cause of variation

➧ A special cause of variation

➝ Action on variation entirely depends on the type of cause identified.

####
[A] **Common Cause of variation** i**n** **Control Chart**:

➧"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 in Control Chart**:

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

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

### 4) 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

OK/Not OK or Pass/Fail

→ e.g. aesthetic look of product ok or not ok

### 5) Classification of the Control Chart in 7 QC Tools:

→ Here we take an example of the most common

→ We can easily construct (X-Bar, R chart) in simple 8 steps as mentioned below.

**control chart**(X-Bar, R chart).→ We can easily construct (X-Bar, R chart) in simple 8 steps as mentioned below.

1. Collect the data.

2. Calculate 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 chart.

8. Interpret the chart.

2. Calculate 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 chart.

8. Interpret the chart.

#### ➤ Step 1: Collect the data for Control Chart in 7 QC Tools:

→ Collect and stratify data into subgroups.

#### ➤ Step 2: Calculate the subgroup average for Control Chart in 7 QC Tools:

→ 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 for Control chart:

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

#### ➤ Step 4: Calculate the subgroup Range (R) for Control Chart in 7 QC Tools:

→ 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) for Control Chart in 7 QC Tools:

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

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

➥ Constants for the

**Control chart****in 7 QC Tools:**#### ➤ Step 7: Plot the Control Chart in 7 QC Tools:

→ 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.

#### ➤ Step 8: Interpret the Control Chart in 7 QC Tools:

➝ 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

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

### 7) Benefits of the Control Chart:

➝ The

➝ It also helps in determining whether the Process is capable or not & the process is stable or not?

➝ It helps in predicting process performance.

➝ The

➝ It makes possible to implement substantial quality improvement.

**Control Chart**gives information about common causes 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.

➝ The

**Control Chart**indicates whether the process is in control or out of control thus we can get the information about the behavior of the process.➝ It makes possible to implement substantial quality improvement.

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See Also: Content 7 QC Tools

2. Flow Charts - 7 QC Tools

3. Cause and Effect Diagram (Fishbone diagram or Ishikawa Diagram) - 7 QC Tools

4. Check Sheet - 7 QC Tools

5. Histogram - 7 QC Tools

6. Pareto Chart - 7 QC Tools

7. Scatter Diagram - 7 QC Tools

8. Control Chart - 7 QC Tools

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