Fuzzy-Logic-based integration of qualitative uncertainties into monetary factory-evaluations

Nowadays turbulent environment exposes global producing companies to many risks caused by uncertain parameters. Therefore, a correct evaluation of investments in factories including uncertainties becomes increasingly important. Existing monetary evaluation methods as the calculation of the net present value (NPV) integrate only static parameters like wages, material costs or overhead costs. Most evaluating methods do not consider the development of those parameters, e.g. future developments of wages. In a correct factory-evaluation method, a multitude of influence parameters including uncertain parameters should be integrated. These uncertainties can be both quantitative and qualitative. In this paper, factors like the development of wage cost, quantities and currency exchange rates are defined as quantitative uncertainties. So-called weak factors like cultural differences, staff motivation or quality awareness are specified as qualitative uncertainties. Qualitative uncertainties are not yet integrated into monetary factory-evaluation methods. Moreover, the dependencies between uncertainties-e.g. the impact of staff motivation to their productivity-have not been considered. This paper therefore presents an approach on how to integrate both qualitative and quantitative uncertainties and their dependencies into a monetary evaluation method for investments in factories through the use of a fuzzy-evaluation-net. This evaluation-net is based on fuzzy-logic and is similar to the structure of artificial-neural-networks (ANNs).