This is a complete guide to learn about **control charts**.

If you really want to learn detail control charts in easy way, you will enjoy and learn in this post.

The purpose of this post is to get the concept knowledge of the control chart & types of control charts. It is very helpful in statistical process control (SPC)

Lets started in…

We are daily fighting with the process to achieve defect free product outcome.

so for that we need to control the production process parameters.

In this post we will learn the quality methods used during the control stage of process.

The only tool to read the process is control charts. This is the major tools use to control the parameters in production.

Table of Contents

## What is mean by process control?

During the manufacturing, process control is the quality efforts.

Therefore the aim is to achieve the manufacturing objective proactively.

So to avoid the production of defects in process.

So the process is run using the terms such as time, speed, pressure and temperature. This terms are nothing but the parameters. Therefore we called the process parameters.

These process parameters are set by using the experiments during the initial project development i.e APQP stage.

So that we get the product output at within specification level.

similarly change in process parameter will affect the product quality.

Therefore these parameters are maintain or controlled consistently, to produce OK product.

So to monitor the product and process parameters, for controlled consistently we use the control charts.

And finally the process of controlling the product and process parameter is process control.

**Control Chart**

Now a days manufacturing industry are facing the lot due to process variation and leads to produce defect and face the huge cost.

Lets dive into the topics which we will learn in this post…

Today I’m going to explore the concept of control charts with detail SCOPE,

You will also see what are the types of control charts in process control approach.

**What is control chart?**

The control charts is invented by Dr. Walter Shewhart in 1920’s.

Therefore the control charts also called as Shewharts chart.

The control chart procedure is propose during the working for Bell lab.

According to Shewhart, the source of variation present in the process by two way.

Variation due to common cause and special cause variation.

Therefore control chart is the differentiating between two source of variation.

You can read common cause and special cause variation, explained in SPC.

**Purpose of control chart**

As we set there is lot of variation in process parameter leads to produce defects.

Therefore the primary purpose of the control charts is to predict the expected product outcome.

It is the statistical analysis tool. This tools shows that the process is in control or not.

Also this is one of the tool from 7 QC tools. Learn more about the 7QC tools Click here.

So the main purpose of control charts is taking samples from process, and detect the possibility of process is in control or out of control.

**How does it works?**

First we need to define limits, such as Upper limit, lower limit and medium value (Center line).

Then draw a chart with these limits.

Gather values from process and draw them into chart.

In the figure shows the sample control chart for the data collected for 10 samples.

There is Center Line at 10,

Upper specification limit which is 10.5

and Lower specification Limit with 9.5.

Lets add and draw the data points on chart.

And the chart shows that the two data points which is not in specification / not in control condition.

What it means? what happen in the process?

So at these two point, there is chance of common cause or special cause occurrence in the process, leads to produce the output which is not in control.

**Next we will see the types of control charts and how the control limits are set.**

**Benefits of control chart**

- Predict process out of control and out of specification limits
- Distinguish between specific, identifiable causes of variation
- Can be used for statistical process control
- Improved worker morale because of the satisfaction they get from seeing

the results of their work

**Types of control chart**

Next, we will touch on types of charts,

Selection of control charts

And details of each type of chart

Lets dive in..

The selection of type of chart is base on the what data you capture?

So we will touch to all chart selection criteria and their use..

Also we will see the structure for selection of control charts.

**7 Types of control charts**

**Xbar-R chart****Xbar-S chart****I-MR chart****P chart****np chart****U chart****C chart**

**Selection of control charts**

The selection of type of chart is base on the what data you capture?

Therefor in the below chart you will see what type of control chart are use where it is applicable.

So in this chart you see, the chart is categorize base on data type, Subgroup size & defect/defective type.

You can learn the what is subgroup? by chick here.

**What is Data types?**

The data is the basics for control chart.

Therefore in statistical techniques there is the analysis of samples and the data.

This data is collected by measurement of sample quantity.

So all processes are generating the data and this data is categorized as ‘Continuous data’ and Attribute Data’.

**1.CONTINUOUS DATA:**

Also called as variable data.

Continuous data is obtain by measurement using the measuring instrument. Example: Diameter of rod, Width of block, Temperature, weight etc.

**2. ATTRIBUTE DATA: **

Also called as discrete data.

This type of data is categorized with level or it is countable data. Example: Count of students at each division, good – bad, Ok – Not ok etc.

**1# Xbar-R chart**

You will learn step by step guide of each type of control chart.

**What is Xbar-R chart?**

The Xbar-R chart is the tool to monitor the process.

From this chart we can take action on any outside points (on special cause) to bring our process in control.

In the Xbar-R chart you need to collect data by measuring samples from process.

This data is measure at equal intervals of time.

So that you get the average of each interval and Range from measure data.

Xbar = Sample Average ( X̅ )

R = Sample Range (R)

**How to collect data for Xbar-R chart?**

Collect data by measuring samples from process.

You need to select the sample size (Subgroup), according to that collect the data by measurement of samples at regular interval of time.

So as per the selection criteria we are using the Xbar-R chart when the subgroup size is 8 or less.

Lets say I am checking the diameter of the rod, as 5 samples per production shift.

You can define the time interval by your choice as per day, after every 4 hour etc.

This data is measure at equal intervals of time.

Therefore I have gather the data of 10 days in below table

**How to draw Xbar-R chart?**

Now we don’t have any control limits define.

Therefore we need to draw only average chart at initial data collection stage.

So calculate the **Average (Xbar)** of the data, I have calculate the same in below table.

Similarly calculate the **Range (R)** of the data,

The range is shown, the same in below table.

Now after collecting the data for define time period, next step is to define / set the control limits for the control chart.

We will see the control limit for Xbar-R chart.

The Center line is the Average of all Data points

**Xbar chart CL = Average of all data points = 20.72**

**R chart CL = Average of Range (Rbar) = 0.15**

Finally after calculating the control limit,

Draw the chart as shown below.

Now its time to monitor the process,

We have set our control limits for the process.

So do the regular measurement of the parameter and put this reading in chart so that you can monitor the behaviors of the process.

For example here I have plotted the same reading of above into the chart and we get the output as Xbar-R control chart.

**How to read / predict control chart?**

Many of us can draw the control chart but not able to analyze the chart.

From the chart we need to find out the special cause present in the process.

So that we can take action on that causes and bring our process within control limit.

**Common cause criteria:**

The variation present in the process is due to common causes.

Therefore you observe that each data point fluctuates with small amount of variation, for this we can say common cause variation in the chart.

**Special cause criteria:**

In the AIAG manual for Statistical process control (SPC), we found the criteria for identify the special cause present in the process.

- If you found one point above upper or below lower control limit, it means of special cause occurrence at this time in the process.
- 7 points found consistently above or below the center line then also there is a special cause phenomenon present in the process.
- Similarly continuous 6 point in increasing or decreasing order can say special cause present.
- 14 points in a row alternatively up & down condition.
- 2 out of 3 points > 2 standard deviation from center line (same side).
- 4 out of 5 points > 1 standard deviation from center line (same side).
- 15 points in a row within 1 standard deviation from center line (either side).
- 8 points in a row > 1 standard deviation from center line (either side).

**2# Xbar-S chart**

For the Xbar-S chart, similar process is to be followed as on Xbar-R chart,

Only difference is instead of calculating Range in R-chart,

Here Standard deviation (S) is calculated for S chart and plot the same in graph.

So that you get the average of each interval and standard deviation from measure data.

Xbar = Sample Average ( X̅ )

S = Standard deviation (S) – You can calculate standard deviation in excel by formulae **[=STDEV(number 1,number 2…number n)]**

below is the formulae for Standard deviation (S)

Now after collecting the data for define time period, next step is to define / set the control limits for the control chart.

We will see the control limit for Xbar-S chart.

The Center line is the Average of all Data points

**Xbar chart CL = Average of all data points**

**S chart CL = Average of Standard deviations**

**3# I-MR chart**

**What is I-MR chart?**

I = Individual chart

MR= Moving range chart

I-Chart : Individual chart use for monitoring the individual points measure in the process. In this chart we can monitor the mean of our process at each interval of the reading measure. we can identify common & special cause from this chart and monitor the same.

MR Chart : In the moving range chart we are measure the difference between each individual measurement and plot it to the chart. Therefore this will help us to identify the process variation present in the system.

**how to collect data for I-MR chart?**

The data is collected at regular interval of time,

As there is no subgroup wise data is require for the I-MR chart,

Now after collecting the data for define time period, next step is to define / set the control limits for the control chart.

Below are the data measure for diameter of rod,

**how to draw I-MR chart?**

Now from the data calculate the moving range,

So moving range is the difference between current data and last measure data.

In the table we have calculate the moving range.

Now calculate the control limit for individual and moving range chart from the data we have,

Formulae of control limits for I-MR chart,

You can draw the chart in excel as the same method for drawing the Xbar-R chart.

**Attribute data type control charts**

As we seen in above paragraph for what is attribute / Deseret data type?

And these charts are categorize by defect / defective and sample size fix / variable.

Now we just touch the term what is defective and defects?

**WHAT IS DEFECTIVE?**

When we inspect the sample of 20 parts and found that 5 parts as NOK, then we have 5 defective parts out of 20.

**WHAT IS DEFECT?**

Now lets go through the example, we have 10 mobile phones for inspection.

It is found that the phones have the non-conformity such as scratches, crack and dent, therefore after inspection found that 10 scratches, 2 cracks and 8 dents among the 10 phones.

Lets conclude that the defects are the non-conformity observed in the part due to the same the part get defective.

Finally the defective part may have many defects.

Now in this attribute data type there are 4 types control chart.

**p-chart :** For defective and subgroup or sample size is same or may vary.

**np-chart :** For defective and Sample size is fixed.

**u-chart :** Use for defect and subgroup or sample size is same or may vary.

**c-chart :** use for defect and Sample size is fixed.

**4# P Chart**

**What is P chart?**

P chart is attribute data type control chart.

Therefore the data should be in attribute form, we seen above that what is attribute data?

P-chart is the percentage of defective of unit non-confirming / Defective.

In this chart subgroup size may vary.

**how to collect data for P chart?**

The attribute data is collected at regular interval of time,

It may or may not require subgroup wise data for the p-chart.

Now the data is collected such as, I have measure the 200 parts and found 20 defective,

So we have 20 defective from sample size of 200, in next sample size of 220 parts found 10 defective.

therefore the sample size may be vary in p-chart.

**how to draw P chart?**

Now from the data, we will calculate the control limit first,

The sample size is not constant, therefore the control limits for each points is vary in chart plotted,

In the table we have calculate the moving range.

Now calculate the control limit for p chart from the data we have,

Formulae of control limits for p chart as below,

Now draw the chart using the control limits and the data points in that,

You can draw the chart in excel, if you have the constant sample size then the control limit lines should be straight line.

You can use p chart for both variable sample size or fix sample size.

**5# np Chart**

**What is np chart?**

np chart is attribute data type control chart.

np-chart is the percentage of defective of unit non-confirming / Defective with fixed sample size.

In this chart subgroup size is fixed.

**how to collect data for np chart?**

The attribute data is collected at regular interval of time,

It require to collect data subgroup wise with fixed sample size.

Now the data is collected such as, I have measure the 200 parts at define time interval and record data for how many defective parts found in the sample frequency of 200 parts.

So we have below data for defective from sample size of 200 per shift,

**how to draw np chart?**

Now calculate the control limit for np chart from the data we have,

Formulae of control limits for np chart as below,

From data calculate the above terms in formulae and draw the chart,

Following are the chart draw in minitab, you can draw in excel as we draw for Xbar-R chart.

**6# u chart**

**What is u chart?**

u chart is attribute data type control chart.

Therefore the data should be in attribute form, we seen above that what is attribute data?

u-chart is the percentage of defect of unit non-confirming.

In this chart subgroup size may or may not vary.

**how to collect data for u chart?**

The attribute data is collected at regular interval of time,

It may or may not require subgroup wise data for the u-chart.

Now the data is collected such as, I have measure the 200 parts and found 20 defect,

So we have 20 defects from sample size of 200, in next sample size of 220 parts found 10 defects.

therefore the sample size may be vary in u-chart.

**how to draw u chart?**

Now from the data, we will calculate the control limit first,

The sample size is not constant, therefore the control limits for each points is vary in chart plotted,

Now calculate the control limit for u chart from the data we have,

Formulae of control limits for u chart as below,

Now draw the chart using calculating the control limits and analyse the data in the chart,

You can draw the chart in excel, if you have the constant sample size then the control limit lines will be straight line.

You can use u chart for both variable sample size or fix sample size.

**7# c chart**

**What is c chart?**

c chart is attribute data type control chart.

In this chart subgroup size is fixed.

**how to collect data for c chart?**

The attribute data is collected at regular interval of time,

It require to collect data subgroup wise with fixed sample size.

Now the data is collected such as, I have inspect the 200 parts at define time interval and record data for how many defects found in the sample frequency of 200 parts.

So we have below data for defects from sample size of 200 per shift,

**how to draw c chart?**

Now calculate the control limit for c chart from the data we have,

Formulae of control limits for c chart as below,

From data calculate the above terms in formulae and draw the chart,

Following are the chart draw in minitab, you can draw in excel as we draw for Xbar-R chart.

Here we complete the control charts and types of control chart topic, which is important tool for monitor the parameters in manufacturing.

One of the tool among 7 QC tools.