The combination of all the following features makes CFAR-m unique.
Objectivity: no handling of weightings - the weighting is resolutely objective and it emanates from the informational content of the variables themselves of their research and internal dynamics.
Specificity: a specific equation for each individual piece of data is used to calculate the indicator
Decision support: ability to run simulations and propose plans of action and optimal sequences of reforms to decision makers.
· It can provide an indication of the precise contribution of the variables to the ranking
· It keeps all the variables during the calculus and so it is helpful for extracting what is happening within the noise. This is very interesting for predictive models.