What is a Control Chart in 7 QC Tools?

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:

Principles of variation:

[1] Common Cause:

[2] Special Cause:

Types of data:

Types of Control Charts:





How do I create a control chart?

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:

Control Chart Formulas:

Step 2: Calculate the subgroup average:

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

Step 4: Calculate the subgroup Range (R):

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

Step 6: Calculate the control_limit

Step 7: Plot of the data:

Control Chart Example:

Step 8: Interpret the Graph:

[A] Process stability:

[B] Process capability:

Advantages of Control Chart:

👉 Also Read:

Cause & Effect Diagram (Fishbone or Ishikawa) 2.(Fishbone or Ishikawa)

➝ It is a statistical tool used to differentiate between process variation resulting from a common cause & special cause.➝ The Control_Chart inis 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 _harts 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.➝ 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.➝ 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.➝ "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...➝ "Special cause is caused by known factors that result in a non-random distribution of output"➝ e.g. machine breakdown, accident, etc...→ There are two types -[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→ There are many types of control_charts are available in→ 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.→ 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:→ Record the readings and stratify it into subgroups as per our sampling plan and record it in the Check Sheet.→ In the second_step, we find the individual sub group's average as per the formula mentioned in the picture.→ Here we find the overall average by using all sub group's individual average.→ In the fourth_step, we find the individual sub group's range as per the mentioned formula.→ Here we find out the average range of all individual subgroups range.→ 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.→ 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.➨ [A] Example of X-Bar and R_Chart:➝ 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➝ Compare with specification and establishe.g. are our processes_capable enough to achieve customer's specifications?➝ 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