Statistical Process Control

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.