Tool for analysis

Tool for analysis

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Have you ever wondered whether a workgroup is successful because Fred is its leader or whether it is successful although he is the leader? Have you ever wondered whether a product is successful because red is its color or whether it is successful although its color is red?

Whenever several factors of different importance act on a common goal, the FACTORFINDER-Software supports analysis. Using the FACTORFINDER-software reveals the magnitude of influence of factors where it is not simply obvious. People that may use it are e.g. product managers, experts on business development, general managers, consultants and politicians. This software may be used as well in the areas of engineering and science. The result of analysis is an evaluation of the importance of all recorded factors plus a classification of the alternatives given as an input into to software.

Ideal variants are discovered. These variants are characterized by solely goal-promoting attributes. If one of the ideal attributes is missed by accident, it is most likely that the result is nevertheless acceptable. Failing to perform in one or more less important respects is tolerated.

Alternatives are highlighted if their success could not be expected from comparing all the other acceptable alternatives. These alternatives should be examined further, because unknown factors could be missing from analysis.

Important factors are separated from less important factors.

The FACTORFINDER-Software assists to analyse complex input-output-systems that are characterized by two levels of output-strength: the level of output is either acceptable or inacceptable. The object analysed can be e.g. a workgroup, where the team performance is either acceptable or inacceptable. Another example is a technical system that works or fails. As well, different designs of a product can be acceptable or inacceptable. The point is: Assigning intermediate levels of performance makes no sense here.

The procedure is new and it is not based on any known statistical method. And there are differences between the FACTORFINDER-Method and neural networks. It is an artificial intelligence approach and within those methods a hybrid of symbolic and sub-symbolic representation with an evolutionary learning component. The method is not statistically but inferred from evolutionary methods. Within statistical methods it would be classified as a dependency analysis with nominally scaled independent variables and with a dichotomic scaled dependent variable. The independent variables are assumed to be linearly combined. The procedure highlights specific data sets from all known and at the same time it calculates values that represent the importance of the independent variables (indirectly via the tolerability of arbitrary values).