(WARNING: THIS SECTION IS A PRELIMINARY DRAFT. MORE-USER-FRIENDLY SOFTWARE AND DOCUMENTATION IS UNDER DEVELOPMENT FOR FUTURE EYE RELEASES)
RSM stands for Response Surface Methods. They are methods by which statisticians choose experiments to model and optimize systems. The purpose of the RSM software is to automate the process of choosing experiments to model and optimize systems.
Imagine you have a widget maker you would like to model and optimize. Your widget maker has several knobs on the outside of it that may be adjusted which will affect the speed at which completed widgets come out. You would like to find the knob settings that maximize the rate at which widgets are produced.
If you have a way to set the knobs to lots of different positions automatically and it takes a very short amount of time after each setting (say less than a second) to observe the widget rate for that setting, then you might just try a lot of settings and choose the best. If, however, it takes a longer amount of time to observe how many widgets come out for each setting, or it is expensive to change the settings because you risk lost widget production at a poor setting then you would not want to, or would not be able to try lots of settings. This is where the RSM software can help you. It can consider the expense in running a experiments and choose experiments very carefully to optimize your widget maker while minimizing the cost incurred.
RSM is specifically designed to optimize noisy systems. This is good if your system happens to be a noisy one. It may also be good even if you don't think your system is particularly noisy. Suppose you have determined that you can count the number of widgets that come out in 15 minutes time to get an accurate estimate of the production rate at that setting so you assume the system you're optimizing is not noisy. However, it may turn out that if you only watch for one minute, you will get slight fluctuations in the number you observe. Then your process has become a noisy one.
Why would you want to take a non-noisy optimization problem and turn it into a noisy one? Suppose that some knob settings are so bad that it is obvious after one minute that they are far from optimal. Then you wouldn't want to waste time watching what happens for a full 15 minutes. You would like to cut the experiment short and move on to a new one. If you switch to watching each setting for only one minute and reporting the noisy result to RSM, it will automatically request more experiments in the more promising areas and quickly ignore the poor ones. The software will give you the effect of running the poor experiments for only a minute or two while running the good ones for longer periods because it is built for noisy optimization.
Do you have a widget maker? If you're still reading this document, the answer is almost certainly yes. Do you have a system where decisions are made that affect the value of a result? Do you have an opportunity to try out some different decisions to see if they'll work better? If so, then you have a candidate for our RSM software. In addition to manufacturing processes, there are lots of other examples: