As an example, use the Tiretread.jmp sample data set. This data set shows the results of a tire manufacturer’s experiment whose objective is to match a target value of HARDNESS= 70 based on three factors: SILICA, SILANE, and SULFUR content. Suppose the SILANE and SULFUR content are easily (and precisely) controllable, but SILICA expresses variability that is worth considering.
1.

Select Graph > Profiler to launch the Profiler.

2.

3.

Click OK.

4.

Select Optimization and Desirability > Desirability Functions in the Prediction Profiler menu.

5.

Doubleclick in the Desirability plot to open the Response Goal window. Select Match Target from the list.

6.

Select Optimization and Desirability > Maximize Desirability to find the optimum factor settings for our target value of HARDNESS.

We get the following Profiler display. Notice that the SILICA factor’s optimum value is on a sloped part of a profile curve. This means that variations in SILICA are transmitted to become variations in the response, HARDNESS.
Maximizing Desirability for HARDNESS
Now, we would like to not just optimize for a specific target value of HARDNESS, but also be on a flat part of the curve with respect to Silica. So, repeat the process and add SILICA as a noise factor.
1.

Select Graph > Profiler.

2.

3.

4.

Click OK.

5.

Change the Pred Formula Hardness desirability function as before.

6.

Select Optimization and Desirability > Maximize Desirability to find the optimum values for the process factor, balancing for the noise factor.

This time, we have also hit the targeted value of HARDNESS, but our value of SILICA is on its flatter region. This means variation in SILICA does not transmit as much variation to HARDNESS.
1.

Select Simulator from the platform menu.

2.

Assign SILICA to have a random Normal distribution with a standard deviation of 0.05.

3.

Click Simulate.

4.

Click the Make Table button under the Simulate to Table node.

5.

Copy the Pred Formula HARDNESS column from one of the simulation tables into the other table. They must have different names, like Without Noise Factor and With Noise Factor.

6.

7.

When the histograms appear, select Uniform Scaling from the Distribution main title bar.

The histograms show that there is much more variation in Hardness when the noise factor was not included in the analysis.
It is also interesting to note the shape of the histogram when the noise factor was included. In the comparison histograms above, note that the With Noise Factor distribution has data trailing off in only one direction. The predictions are skewed because Hardness is at a minimum with respect to SILICA, as shown in Profiler Showing the Minima of HARDNESS by SILICA. Therefore, variation in SILICA can make only HARDNESS increase. When the nonrobust solution is used, the variation could be transmitted either way.