Welcome to lecture 43. This is in continuation with the previous lecture about statistical process control. In this lecture, we will learn about constructing the x bar charts in Minitab and learn about the attribute control charts such as B, MP u and C charts. We have discussed about the x bar charts in the previous lecture. Let us learn to construct in Minitab. Download the respective Minitab file from this lecture and Double click on the file to open mini tab.

Click stat control charts, reviewable charts, for subgroups and x bar, R. This step is same for x bar, s, and IMR charts to the x bar, our chart dialog box opens. Select all observation for a chart are in one column at the top, double click on C three diameter in the second box double click on C to sample number. You can also click on x bar our options, if you want to change the default settings. Now, the Minitab will show the control chart on graphical window we can see both average control chart and a range control chart in one graph, we have to analyze for the out of control points here. As you can see, there is an out of control point in this range chart. Now, once we identify out of control point, analyze the reasons and take corrective actions if necessary.

In the similar manner, we can develop x bar s chart as well as I am our charts in the mini tab. Now, let us learn about the attribute control charts. Though control chart for variables are more powerful tools for studying the variations and most of the practical cases it is difficult to get data in variable format. Most of the managers get information and attribute form of data such as number of pieces rejected or accepted number of lots accepted or rejected rework percentage defects, defective units, etc. The various types of control charts are B char and NP charts for percentage of defective units Q and C charts for controlling total defects. Now, the question arises as what are the differences between p and NP charts as well as C and you charts and when are they used?

Let me illustrate it suppose an operation manager received rejection data from one of his process lines as it is shown on the screen at first of September out of 300 units inspected 23 units were rejected. Now, what is a control subject of the managers interest? Is he interested in controlling the percentage of non conforming items or is he interested in controlling the total number of defects in process. If he is interested in controlling the proportion of the unit, then which chart should be used Well, he can Use either p or NP chart. If the sample size is constant else, if the sample size is not constant, he would be using the P chart. In the same case, however, one defective unit may contain more than one defect.

If the manager is interested in controlling the total defects in process, then you can use either C or you chart if the sample size is constant, else use you chart if the sample size is not constant Now, let us learn to develop the P chart. P chart is the most versatile and widely used chart. P chart is based on the binomial probability distributions. Take an example. From a process. The data is as shown on the screen.

We can say the proportion rejected from the table as eight divided by 506 divided by 400 respectively. We can calculate the process average for proportions As n one into p one plus n two into p two plus nn into p n divided by n one plus n two plus n three plus nn etc. The control limits for proportion can be calculated as p bar plus minus three into square root of p bar into one minus p bar divided by n. NP charts are similar to P charts except sample size should be constant for NP chart see and your charts we have learned in the DPM or lecture that one defective unit may contain more than one defect C and u charts are used in such situations. The process average for C chart is calculated as C bar is equal to c one plus c two plus c three plus c n divided by and we'll see c one, C two etc The defects and in is a number of samples.

The upper and lower control limits are calculated as C bar plus minus three into square root of c bar. The chart is similar to C chart, it is used when sample size is not constant. As I always tell, it is easy to construct those charts in Minitab Let me show you how to construct attribute charts in Minitab. Download the respective file from this lecture. Double click the file to open the Minitab Click Start, control charts, attribute charts and P. Similarly, we can select NP C and your chart also from this menu now, the P chart dialog box will open double click on number rejected click on the subgroup size box and double click on number inspected and click OK. The P chart is opened in the graphical window analyze it in the similar manner as that of x bar chart for the out of control points Let us learn about the analysis of control charts.

Control charts are analyzed in two dimensions for the point above or below the control limits as well as for the patterns or runs. In a process, which is under control, there is rarely any point outside the control limits. Whenever a point faults outside it indicates a special cause variation and need to be investigated. Points beyond control limits indicates either variability from people rupees has been increased changes in measurement system points below a control limit indicates either variability from piece to piece as changed or become better or a change in the measurement system. As far as the patterns are concerned, all unusual patterns even though within control limits are indicators of process, out of control or change. Patterns could also be favorable.

Hence, need to be indicated patterns are observed in The name of runs usually the runs such as seven points in row on any one side of our bar a rise or decline in seven consequent points need attention and to be investigated runs about our bar may be due to greater spread due to loose fixtures, non uniform raw material, etc. measurement data error runs below our bar maybe due to reduced spread our process improvement our measurement data error in the implementation phase, the Six Sigma team identifies the solution to counteract the root causes and validate the solution for implementation. During this phase, the team uses all the tools as described on the screen. We have covered all the tools in detail in various lectures. Let us discuss the last and final concept of this course. The control plan control plan is a document describing the system function trawling the processes is a dynamic document which changes according to the process and product implements.

A sample control plan includes the required process steps, specification limits, gauge used, responsibility and type of control charts, etc. Here is an example of control plan that we can follow for our process. Please review whether all the above mentioned parameters are there in this control plan. That's all for this lecture. But this lecture We have completed all the relevant concepts covered in the body of knowledge for six sigma. various concepts we have covered in this section are a design of experiment, do EV, statistical process control, C implement and validate solutions, D control plan etc.

The final exam contains 15 questions from this section you can have a practice codes related to the section. Now, that is proceed to the final and concluding lecture.