To show a common workflow with the Defect profiler, we use The experimental data in the sample data table results from an experiment to study the effects of SILICA, SILANE, and SULFUR on four measures of tire tread performance.
Select Help > Sample Data Library and open
Select Graph > Profiler.
Select Pred Formula ABRASION, Pred Formula MODULUS, Pred Formula ELONG, and Pred Formula HARDNESS and click Y, Prediction Formula.
Select Spec Limits from the Simulator red triangle menu.
Profiler Random Specifications
Select Defect Profiler from the Simulator red triangle menu to see the defect profiles. The curves, Means, and SDs will change from simulation to simulation, but will be relatively consistent.
Defect Profiler
Look at the curve for SILICA. As its values vary, its defect rate goes from the lowest 0.001 at SILICA=0.95, quickly up to a defect rate of 1 at SILICA=0.4 or 1.8. However, SILICA is itself random. If you imagine integrating the density curve of SILICA with its defect profile curve, you could estimate the average defect rate 0.033, also shown as the Mean for SILICA. This is estimating the overall defect rate shown under the simulation histograms, but by numerically integrating, rather than by the overall simulation. The Means for the other factors are similar. The numbers are not exactly the same. However, we now also get an estimate of the standard deviation of the defect rate with respect to the variation in SILICA. This value (labeled SD) is 0.057. The standard deviation is intimately related to the sensitivity of the defect rate with respect to the distribution of that factor.
Looking at the SDs across the three factors, we see that the SD for SULFUR is higher than the SD for SILICA, which is in turn much higher than the SD for SILANE. This means that to improve the defect rate, improving the distribution in SULFUR should have the greatest effect. A distribution can be improved in three ways: changing its mean, changing its standard deviation, or by chopping off the distribution by rejecting parts that do not meet certain specification limits.
Select Defect Parametric Profile from the Simulator red triangle menu. This command shows how single changes in the factor distribution parameters affect the defect rate.
Defect Parametric Profile
Let’s look closely at the SULFUR situation. You might need to enlarge the graph to see more detail.
For the red curve, Mean Shift, the current rate is where the red solid line intersects the vertical red dotted line. The Mean Shift curve represents the change in overall defect rate by changing the mean of SULFUR. One opportunity to reduce the defect rate is to shift the mean slightly to the left. If you use the crosshair tool on this plot, you see that a mean shift reduces the defect rate to about 0.02.
For the orange curve, USL Chop, there are good opportunities. Reading the curve from the right, the curve starts out at the current defect rate (0.03), then as you start rejecting more parts by decreasing the USL for SULFUR, the defect rate improves. However, moving a spec limit to the center is equivalent to throwing away half the parts, which might not be a practical solution.
Looking at all the opportunities over all the factors, it now looks like there are two good options for a first move: change the mean of SILICA to about 1, or reduce the variation in SULFUR. Because it is generally easier in practice to change a process mean than process variation, the best first move is to change the mean of SILICA to 1.
Adjust the Mean of SILICA from 1.2 to 1.0. Click Rerun.
Adjusting the Mean of SILICA
After clicking Rerun, we get a new perspective on defect rates.
Adjusted Defect Rates

Help created on 9/19/2017