## What is a 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 QC Tools__is a type of run_chart used for studying the process_variation over time.

→ This is classified as per recorded data is variable or attribute.

→ In our business, any process is going to vary, from raw material receipt to customer support.

→ Machines have wear, tear, and malfunction and tear after a long run.

→ Control _charts measure variation and show it to you graphically and we can easily say that it is within an acceptable limit or not?

→ Many processes can be tracked by this graph like defects, production time, inventory on hand, cost per unit, and other metrics.

→ Also, we can use this graph to measure non-manufacturing processes like billing errors, missed appointments, customer support calls, bill payment dues, days between billing and payment, expenses, on-time delivery failure, unplanned absences, etc.

### Use of Control Chart

- It is used to predict the performance of the manufacturing process
- Find out the special causes within the process
- Identify the trend of the process

### History:

➝ It was invented by Dr. Walter A. Shewhart working for Bell Labs in the 1920s.➝ So this is called "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 operation.

### Principles of variation:

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

➝ Two types of causes are responsible for the variation.

(1) Common cause

(2) Special cause

➝ Action entirely depends on the type of cause identified.

#### [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 part 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...

#### Types of data:

→ There are two types -__Attribute and Variable__

[1] Attribute:

⇢ Attribute data that can be counted or can give an answer in Go/No Go, OK/Not OK or Pass/Fail

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

[2] Variable:

⇢ Variable data can be measured.

⇢ e.g. Weight, Height, Length, Hardness, Diameter, Angle

## Types of Control Charts:

→ There are many types of control_charts are available in__Statistical Process_Control__.

→ The classification 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?

→ For better understanding refer below picture which is very easy to understand with the help of classification.

###
ðŸ‘‰ __Control Chart Excel Template Download__

## How do I create a control chart?

→ Here we take an example of the most common (X-Bar, R_chart)→ To understand this example we are taking variable data and subgroup size=5 as per the classification mentioned above

→ We can easily construct (X-Bar, R_chart) in simple 8 steps which are mentioned below:

- Collect the data.
- Calculate the subgroup average.
- Determine the overall average.
- Calculate the range.
- Compute the average of the range.
- Calculate the control_limit
- Plot the data in the graph.
- Interpret the Graph.

#### Step 1: Collect the data:

→ Record the readings and stratify it into subgroups as per our sampling plan and record it in the Check Sheet.### Control Chart Formulas:

#### Step 2: Calculate the subgroup average:

→ In the second_step, we find the individual sub group's average as per the formula mentioned in the picture.#### 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 subgroups range.#### Step 6: Calculate the control_limit

→ In this_step, we find the limit of the X-bar and R_chart with the below-mentioned formula.→ Different Constants value are mentioned in below pictures which is very important for the Graph:

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

#### 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 operation, product, size of the subgroup, work conditions, shift, etc.

### Control Chart Example:

➨ [A] Example of X-Bar 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.

➝ For detailed information, go through these

__8 rules of special cause identification__.

#### [B] Process capability:

➝ Compare with specification and establish__Process Capability__e.g. are our processes_capable enough to achieve customer's specifications?

### Advantages of Control Chart:

➝ Control_chart gives information about the common causes and special causes.➝ It also helps in determining whether the Process is capable or not & stable or not? so, we can get the information about the behavior of the process.

➝ It helps in predicting operation performance.

➝ It makes possible to implement substantial

__Quality Improvement__.

ðŸ‘‰ Also Read:

2.

__Cause & Effect Diagram__(Fishbone or Ishikawa)
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