# 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.

➝

→ 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.

→ 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.

➝ It should be random and not a systematic pattern.

➝ Look for the presence of special causes.

➝ For detailed information, go through this

➝ 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 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.

### ➤ 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:

➝ "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

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:

→ 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:

- Collect the data.
- Calculate the subgroup average.
- Determine the overall average.
- Calculate the range.
- Compute the average of the range.
- Calculate the control limit for X-bar and R chart.
- Plot the data in the graph.
- 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.

#### ➥ 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.

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

→ Constants for the Graph:

#### ➥ 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.

#### ➥ 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.

➝ For detailed information, go through this

__e.g. are our process enough to continuously meet the customer's specification?__

**8 rules of special cause identification**#### [B] Process capability

➝ 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 the 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.

➝ 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.

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