Analytical models are composed of a set of formulas and/or computational algorithms that provide the values of desired performance measures as a function of the set of values of the performance parameters. Analytical models are generally less detailed than simulation models, for them to be mathematically tractable. Therefore, they are generally less accurate but more efficient to run. Hence, it is common to run analytical models on a personal computer. But in some situations, exact analytical models may not be available or are computationally inefficient. In such cases, we can resort to approximations that render the model easier to solve, and in a more efficient manner. The accuracy of approximation is the factor that decides as to which aproach need to be taken for solving a model. In capacity planning, one is generally interested in being able quickly to compare and evaluate different scenarios. For this purpose, accuracies at the 10 to 30% level are acceptable. So, because of their efficiency and flexibility, analytic models ( exact or approximate ) are generally preferable for capacity planning purposes.
Some important analytical solution techniques are Convolution, Mean Value Analysis, Decomposition/Aggregation.
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