Selection of technology acquisition methods using an artificial classification technique

In aerospace sector, product life cycle and technology development periods are longer and costly than those in most of the other sectors. Therefore technology management activities are important for high technology firms and technology acquisition is one of the most important aspect of these activities. Within this context, selection of better technology acquisition methods for justifying cost and schedule needs is an optimization challenge. In this study, input parameters such as dual usage, TRL (Technology Readiness Level), ITAR Restrictions, etc. and their weighing factors are identified. These input parameters are processed using artificial classification techniques (neural network, fuzzy logic etc.) and technology acquisition methods (in house R&D, purchase etc.) are selected as output parameters.