Read this chapter on the basics of statistical process control (SPC). SPC is a standard tool for monitoring whether a process is performing as expected and, if not, where problems occur. While reading, consider how this kind of tool factors in process capacity management.
Example 1
Assume that in the manufacture of 1 kg Mischmetal ingots, the product weight varies with the batch. Below are a number of subsets taken at normal operating conditions (subsets 1-7), with the weight values given in kg. Construct the \(X\)-Bar, \(R\)-charts, and \(S\)-charts for the experimental data (subsets 8-11). Measurements are taken sequentially in increasing subset number.
Subset # | Values (kg) |
1 (control) | 1.02, 1.03, 0.98, 0.99 |
2 (control) | 0.96, 1.01, 1.02, 1.01 |
3 (control) | 0.99, 1.02, 1.03, 0.98 |
4 (control) | 0.96, 0.97, 1.02, 0.98 |
5 (control) | 1.03, 1.04, 0.95, 1.00 |
6 (control) | 0.99, 0.99, 1.00, 0.97 |
7 (control) | 1.02, 0.98, 1.01, 1.02 |
8 (experimental) | 1.02, 0.99, 1.01, 0.99 |
9 (experimental) | 1.01, 0.99, 0.97, 1.03 |
10 (experimental) | 1.02, 0.98, 0.99, 1.00 |
11 (experimental) | 0.98, 0.97, 1.02, 1.03 |
Solution:
First, the average, range, and standard deviation are calculated for each subset.
Subset # | Values (kg) | Average (cc) |
Range (R) |
Standard Deviation(s) |
1 (control) | 1.02, 1.03, 0.98, 0.99 | 1.0050 | 0.05 | 0.023805 |
2 (control) | 0.96, 1.01, 1.02, 1.01 | 1.0000 | 0.06 | 0.027080 |
3 (control) | 0.99, 1.02, 1.03, 0.98 | 1.0050 | 0.05 | 0.023806 |
4 (control) | 0.96, 0.97, 1.02, 0.98 | 0.9825 | 0.06 | 0.026300 |
5 (control) | 1.03, 1.04, 0.95, 1.00 | 1.0150 | 0.09 | 0.040509 |
6 (control) | 0.99, 0.99, 1.00, 0.97 | 0.9875 | 0.03 | 0.022583 |
7 (control) | 1.02, 0.98, 1.01, 1.02 | 1.0075 |
0.04 | 0.028930 |
8 (experimental) | 1.02, 0.99, 1.01, 0.99 | 1.0025 | 0.03 | 0.025000 |
9 (experimental) | 1.01, 0.99, 0.97, 1.03 | 1.0000 | 0.06 | 0.025820 |
10 (experimental) | 1.02, 0.98, 0.99, 1.00 | 0.9975 | 0.04 | 0.027078 |
11 (experimental) | 0.98, 0.97, 1.02, 1.03 | 1.0000 | 0.06 | 0.029409 |
Next, the grand average \(X_{G A}\), average range \(R_{A}\), and average standard deviation \(S_{A}\) are computed for the subsets taken under normal operating conditions, and thus the centerlines are known. Here \(n=4\).
\(\begin{aligned}
&X_{G A}=1.0004 \\
&R_{A}=0.05428 \\
&S_{A}=0.023948
\end{aligned}\)
\(X\)-Bar limits are computed (using \(R_{A}\)).
\(\begin{aligned}
&\mathrm{UCL}=X_{G A}+A_{2} R_{A}=1.0004+0.729(0.05428)=1.04 \\
&\mathrm{LCL}=X_{G A}-A_{2} R_{A}=1.0004-0.729(0.05428)=0.96
\end{aligned}\)
\(X\)-Bar limits are computed (using \(S_{A}\)).
\(\begin{aligned}
&\mathrm{UCL}=X_{G A}+A_{3} S_{A}=1.0004+1.628(0.023948)=1.04 \\
&\mathrm{LCL}=X_{G A}-A_{3} S_{A}=1.0004-1.628(0.023948)=0.96
\end{aligned}\)
Note: Since \(n=4\) (a relatively small subset size), both \(R_{A}\) and \(S_{A}\) can be used to accurately calculate the UCL and LCL.
\(R\)-chart limits are computed.
\(\begin{gathered}
\mathrm{UCL}=D_{4} R_{A}=2.282(0.05428)=0.12 \\
\mathrm{LCL}=D_{3} R_{A}=0(0.05428)=0
\end{gathered}\)
\(S\)-chart limits are computed.
\(\begin{gathered}
\mathrm{UCL}=B_{4} S_{A}=2.266(0.023948)=0.054266 \\
\mathrm{LCL}=B_{3} S_{A}=0(0.023948)=0
\end{gathered}\)
The individual points in subsets 8-11 are plotted below to demonstrate how they vary with in comparison with the control limits.

Figure E-1: Chart of individual points in subsets 8-11.
The subgroup averages are shown in the following \(X\)-Bar chart:
Figure E-2: \(X\)-Bar chart for subsets 8-11.
The \(R\)-chart is shown below:
Figure E-3: \(R\)-chart for subsets 8-11.
The \(S\)-chart is shown below:
Figure E-4: \(S\)-chart for subsets 8-11.
The experimental data is shown to be in control, as it obeys all of the rules given above.