Neural networks can be counted on to structure and parameterize themselves to match the data fed to them. The weaknesses of neural networks arise from their strength. They tend toward accuracy without much precision. Neural networks also are the epitome of the black box performance tracking indicates that their ability to evaluate circumstances by which the retail environment functions is limited. The best use for neural networks is in data exploration, but not necessarily in quantitative analysis, for they are weak in description and virtually useless in explanation.
Most analysis decomposes whole systems into component parts.
Many natural systems, however, only have meaning in their entirety and, therefore, are only to be understood as a whole rather than as pieces. It is the context of the components that creates the function of the components, and one without the other is virtually meaningless. In the marketplace, for example, any attempt to analyze consumer behavior without evaluating interactions with other consumers, or without the proximal influence of supply, provides little realistic or valuable information. For this setting, systems simulation modeling was created. Systems simulation is a set of often complex mathematical algorithms intended to simulate or mimic the function of the entire system rather than simply its component parts.