INTRODUCTION
mmSensitivity - picks up where your
spreadsheet leaves off. It helps you make the best possible business
decisions by applying proven management techniques such as
Sensitivity analysis. By
integrating all of the quantitative Sensitivity Analysis methods into a
single application, mmSensitivity gives you unprecedented power and
flexibility. What's more, mmSensitivity's innovative interface makes
this power so easy to apply that you will find yourself using it
even for routine problems.
Why do You need mmSensitivity?
A mathematical model is defined by a series of equations, input factors, parameters, and variables aimed to characterize the process being investigated. Input is subject to many sources of uncertainty including errors of measurement, absence of information and poor or partial understanding of the driving forces and mechanisms. This imposes a limit on our confidence in the response or output of the model. Further, models may have to cope with the natural intrinsic variability of the system, such as the occurrence of stochastic events. Good modeling practice requires that the modeler provides an evaluation of the confidence in the model, possibly assessing the uncertainties associated with the modeling process and with the outcome of the model itself. Uncertainty and Sensitivity Analysis offer valid tools for characterizing the uncertainty associated with a model.
What are the reasons to conduct mmSensitivity?
Modelers may conduct SA to determine
(a) the model resemblance with the process under study,
(b) the quality of model definition,
(c) factors that mostly contribute to the output variability
(d) the region in the space of input factors for which the model variation is maximum
(e) optimal regions within the space of factors for use in a subsequent calibration study
(f) interactions between factors.