Process Illustration
Below is an illustration process distribution against time
Process (a) is very stable and under control within it limit. But process (b) and (c) is not under control. Process (b) distribution is same but the distribution shift against time. Process (c) distribution is variance varies with time.
In manufacturing, process variations or shifts are our biggest problems. If we could control the process in order to maintain always as (a) the defective will be at its minimum and productivity will be at its highest. But it is quite impossible to capture the distribution of the process every lots or everyday, it would take too much time and cost. Control chart is one of the best alternatives.
In manufacturing, process variations or shifts are our biggest problems. If we could control the process in order to maintain always as (a) the defective will be at its minimum and productivity will be at its highest. But it is quite impossible to capture the distribution of the process every lots or everyday, it would take too much time and cost. Control chart is one of the best alternatives.
How Control chart works?
a)Process distribution shift towards UCL
The X chart display upwards shift similar to the distribution shift but the R chart does show normal movement since the variance of the distribution remain same.
b) Process distribution shift towards UCL
The R chart shows a tendency of moving upwards as the distribution variance becomes bigger. The X chart movement also shows slightly similar movement as the distribution.
The control chart can tell us the changes in the process distribution as time goes by. We can also monitor if our process is becoming more stable or not. Our action and improvement plan on the process shows any improvement can also be seen. It is a very useful tool in prediction of process stability. To plot a control chart we do not need to take many samples as compare to plotting the distribution of the process.
The control chart can tell us the changes in the process distribution as time goes by. We can also monitor if our process is becoming more stable or not. Our action and improvement plan on the process shows any improvement can also be seen. It is a very useful tool in prediction of process stability. To plot a control chart we do not need to take many samples as compare to plotting the distribution of the process.
Type of Control Chart
There are several type of chart that we could implement depending on the type of data and control measurement. Please refer below table
Variable control charts are more sensitive than attribute control charts. Therefore, variable control charts may alert us to quality problems before any actual "reject" (as detected by the attribute chart) will occur. The variable control charts are of trouble that will sound an alarm before the number of rejects increases in the production process.
Attribute control charts have the advantage of allowing for quick summaries of various aspects of the quality of a product, that is, the engineer may simply classify products as acceptable or unacceptable, based on various quality criteria. Thus, attribute charts sometimes bypass the need for expensive, precise devices and time-consuming measurement procedures. Also, this type of chart tends to be more easily understood by managers unfamiliar with quality control procedures; therefore, it may provide more persuasive (to management) evidence of quality problems.
There are several type of chart that we could implement depending on the type of data and control measurement. Please refer below table
Variable control charts are more sensitive than attribute control charts. Therefore, variable control charts may alert us to quality problems before any actual "reject" (as detected by the attribute chart) will occur. The variable control charts are of trouble that will sound an alarm before the number of rejects increases in the production process.
Attribute control charts have the advantage of allowing for quick summaries of various aspects of the quality of a product, that is, the engineer may simply classify products as acceptable or unacceptable, based on various quality criteria. Thus, attribute charts sometimes bypass the need for expensive, precise devices and time-consuming measurement procedures. Also, this type of chart tends to be more easily understood by managers unfamiliar with quality control procedures; therefore, it may provide more persuasive (to management) evidence of quality problems.
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