Resumo:
Investment analysis is carried out through the use of proven evaluation tools, such as the
DCF (Discounted Cash Flow), which includes the NPV method (net present value) and the
IRR method (Internal Rate of Return). The DCF method represents a high value for
Engineering Economic Analysis, being in fact a part of its conceptual basis.
Nevertheless, the uncontrolled application of the above mentioned tools might lead to false
results, once they do not preview the variations which occur in the input and output variables
of the problem that is analyzed.
It is common for enterprises to make use of the knowledge of experienced specialists when
studying approximations in the ranges of input variables. Such ranges are often expressed in
words as “around”, “approximately”, “very high”, which are inexact expressions and bring
uncertainty to variables, the so called lexical uncertainty.
Fuzzy logic is able to quantify lexical uncertainty and, from there on, to generate more
realistic answers to financial problems. Quantification may be initially based on the
experience of specialists and then be available for future use.
The Fuzzy Set Theory is a widespread theory and it is not new, once it has already been
considered in former studies. Nevertheless, the point of the present work is to provide an
analysis of the application of fuzzy theory together with economic analysis techniques. There
will be an emphasis in the study of the internal rate of return (IRR) calculated according to
fuzzy set theory.
Also, there is the intention to present the results of another application of this theory,
combined with Engineering Economic Analysis, showing its effectiveness in investment
analysis. It should be mentioned that practical examples enrich the literature on the subject,
because the power of the tool is then proven accordingly to the kind of analysis that is
desired.
The present work searches for the state of the art concerning the use of the theory mentioned
through an updated bibliography review and proposes a case study for verifying the IRR in
the fuzzy form, concluding that there is a possible interpretation for this IRR. The case study
is carried out by developing a program which executes automated fuzzy calculation.
