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Statistical Process Control (SPC) - Essay Example

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1) "Statistical Process Control (SPC) is a preventative measure to be applied by companies, not a detection process" Comment on this statement, justifying clearly why you agree or disagree with the statement. (20%)
Statistical Process Control is a quality control technique that is used in manufacturing processes by using popular statistical methods like mean, variance, Standard Deviation etc…
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Statistical Process Control (SPC)
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Extract of sample "Statistical Process Control (SPC)"

1) "Statistical Process Control (SPC) is a preventative measure to be applied by companies, not a detection process" Comment on this ment, justifying clearly why you agree or disagree with the statement. (20%)
Statistical Process Control is a quality control technique that is used in manufacturing processes by using popular statistical methods like mean, variance, Standard Deviation etc. Technically Speaking, SPC is a quality control technique that is used to estimate whether a process is within desired and permissible limits. Under the original quality control methodology, a product's quality is ascertained by examining the finished product for a predetermined set of features or standards. In contrast to this, SPC uses statistical techniques to ascertain whether there are any deviations in the production line that may end with the project being rejected.
The concept behind the viability of the SPC is that any product in a production process will have a certain degree of deviation in terms of its properties, which will vary slightly from its design values. SPC manages these deviations and prevents them from getting out of control, by analyzing the variances within the process from time to time. For example, by using statistical tools, any person who oversees the functioning of the production line can use them to detect any profound and undesirable changes (which exceed the specified limits). The causes of any such variation could be due to a host of factors such as wear and tear due to continued use etc. and as such the technique offers a great chance of correcting the problem at the source itself thereby ensuring that subsequent products from the production line stay well within the desired limits. In worst cases, this could alert the supervisor so that he/she may stop the production in order for the problem to be rectified. Thus, it can be seen that the SPC helps in preventing any further damage and as such, this method is extremely beneficial for cutting down the wastage and as such, the statement in the question is a valid one.
/
2) Which of the following charts (if any) indicate a process that is within control, providing reasons for your answers in each case. (20%)
Chart I) This process is clearly out of question as some of the points lie outside the UCL & LCL.
Chart II & IV) Both these charts show a trend in their production. While the former shows a tendency to lie below the mean, the latter is equally above & below the mean value, but for considerable periods of time. Therefore, all the three charts are out of control.
Chart III) This chart shows that there is a random behavior in the process, and as such the product variance is conforming to randomness. Also the samples lie between the two specified limits. Therefore, this process in this chart is under control.
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c) A company manufactures precision components for hard drives. A key component has a critical dimension for one part that has a target value of 1.04 millimeters. Four measurements of this dimension are taken at regular time intervals; the result are tabulated below in table 1:
8am
9am
10am
11am
12am
1pm
2pm
3pm
4pm
#1
1.04
1.05
1.04
1.07
1.04
1.04
1.06
1.07
1.04
#2
1.06
1.05
1.06
1.03
1.05
1.05
1.04
1.06
1.03
#3
1.04
1.06
1.05
1.02
1.01
1.03
1.04
1.06
1.02
#4
1.03
1.07
1.06
1.05
1.03
1.03
1.05
1.05
1.03
Table 1 Sample Measurements
Using the following factors, calculate the upper and lower control limits for both an and R (Mean and range) chart. (40%)
Table 2 - SPC Chart Factors
Create Control charts for this case. (20%)
The means are given as:
#1=1.05
#2=1.047
#3=1.036
#4=1.044
the mean of means is: 1.04425
The ranges are given as:
#1=0.03
#2=0.03
#3=0.05
#4=0.04
the mean of ranges is given by: 0.0375
for the R chart:
LCL = Rmean x D3 = 0
UCL = Rmean x D4=0.0855
For the x chart:
UCL = xmean_of_means + A2 x Rmean =1.071625
LCL = xmean_of_means - A2 x Rmean=1.04425-0.027375=1.016875
The R chart is given as:

0.08 UCL
0.06
x
0.04 x
Mean
0.02 x x
LCL
1 2 3 4
The x chart is given by:
1.07
1.05 x
x
1.04 x
x
1.03
1 2 3 4 Read More
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