It's part of the Six Sigma paradox mentioned in the first chapter. Neural nets are popular because they have a proven track record in many data mining and decision-support applications. Neural nets are a class of very powerful, general purpose tools readily applied to prediction, classification, and clustering. They have been applied across a broad range of industries from predicting financial series to diagnosing medical conditions, from identifying clusters of valuable customers to identifying fraudulent credit card transactions, from recognizing numbers written on checks to predicting failure rates of engines. In this section we explore only the application of neural nets to design of experiments for Six Sigma, but this merely scratches the surface of the potential applications of neural nets for quality and performance improvement.
Neural networks use a digital computer to model the neural connections in human brains.
When used in well-defined domains, their ability to generalize and learn from data mimics our ability to learn from experience. However, there is a drawback. Unlike a well-planned and executed DOE, a neural network does not provide an explicit mathematical model of the process. For example, in addition to feeding the neural net X 1 and X 2 raw, one could include transformations, higher-order polynomial and interaction terms, etc. as inputs to the neural network. Still, for the most part, neural networks must be approached as black boxes with mysterious internal workings, much like the mystery of the human mind it is designed to imitate.