What is Caontrol Chart?
As a matter of fact, we've variations everywhere, no process is without variation. this implies that there is no common cause variation or special cause variation. within the control charts, we see how these variations impact our process over a period of your time, whether our process are going to be up to the mark or will cross the method boundaries. Control charts help us in visualizing this variation. Control charts have one central line or mean line (average), and so we've the Upper Control Limit (UCL) and Lower Control Limit (LCL). The upper control limit and lower control limit are three variance distance from the middle line in each side. we will have the upper warning line and lower warning limit also. Now the question is which is that the two variance in distance from the central line? The one which alarms us if data points crossing this limit, this will make the method unstable.
How to create and use an control Chart?
We can create an control chart while using the Minitab, we want to enter the information in Minitab and use the control chart as per data types.
If we don't have a Minitab, we will make it in Excel. In Excel, we want to enter all the information points and cast off the common of information points, then determine the quality deviation with variance formula. We last till we reach 3rd variance and so use the graph.
The above example is for a straightforward I-MR chart, which we will make and use it for continuous data types.
When to use an control Chart?
• We can use an control Chart, at the starting of a project or whenever we wish to determine the VoP. While seeing the VoP we will even find the rationale for running the project.
• We can see process improvement too by employing a Control Chart towards the top of the project. this is able to also help in determining whether the project is successful or not.
• A Control Chart also helps in checking the method stability and verifying whether the method is stable enough to boost and make necessary improvements within the process wherever required.
Four Process States in a Control Chart
The 4 process states in a very Control Chart are discussed below:
1. the best state: this is often where the method is up to the mark and every one the information points represent the control limits. there's no non-conformance.
2. the brink state: Although data points are up to the mark, or the method is stable, however, some non-conformance happen over a period of your time.
3. The Brink of Chaos state: during this, the method is in control; however, it's on the sting of committing errors.
4. and also the fourth stage is when the method is Out of Control and that we have unpredictable non-conformance.
Types of Control Charts
Control Charts are basically of seven types, because it all depends upon the information type. If we've a continual data type, then we will use 3 styles of Control Charts i.e. I-MR Chart, X Bar R Chart, and X Bar S Chart.
If we've a discrete data type, then we use the 4 styles of Control Charts: P, Np, C, and U Charts. of these types are described as below:
• I – MR Chart
We use the I-MR charts once we cannot do the subgrouping of the information, thanks to not much data points, or even the merchandise takes long cycle time to provide, then we will use the I-MR chart, which implies Individual Moving Range Chart. Here, initially we see the information points within the Control Chart and so their difference within the chart.
• X-Bar R Chart
When we have 2 or over 2 subgroup size then, this is often getting used for continuous data. the quality chart for variables data, X-bar and R charts help to see if a process is stable and predictable. within the X bar graph, X indicates the mean of all the subgroups within the chart, whereas R indicates the range of all subgroups within the chart.
• X Bar S Chart
In the X Bar S chart, we use it to test the mean of the subgroups and also the variation of the method. it's getting used for over 2 subgroups size and might even be used for over 10 subgroups.
• P and Np Control Charts
The P and Np charts are used for defective data to test the method stability while seeing the defective data points. the most difference between the P and Np is P chart is employed when sample size varies, whereas Np chart is employed when the sample is constant.
• C and U Control Charts
The C and U charts help to test the soundness in a very single unit, which could have over one defect. for instance, the quantity of defects in one pen. Here also, we will see the defects on the identical size of the sample or it can vary on other samples.
C Control Chart is employed when there's over one defect and also the sample size is fixed. While U Control Chart is employed for over one defect and if the sample size isn't fixed.