Modern product and process development tools must meet wide-range of requirements. Minimizing the number of experiments while maximizing of the amount of information generated is only one aspect. Time restrictions and constraints imposed by the technology as well as specific customer demands are imperative boundary conditions which must be considered when planning an experiment. Furthermore, the experiment must yield reliable information regarding the feasibility of a project as early as possible. Commercial tools for statistical design of experiments alone cannot meet these requirements at an acceptable cost-benefit ratio. That is why the key of the proposed approach is to integrate the existing methods, models and information sources to explore useful knowledge. To explore and transfer all the useful knowledge needed to operate and optimize products, technologies and the business processes, the research of the applicant aimed the development of a novel methodology to integrate heterogeneous information sources and heterogeneous models. The proposed methodology can be referred as model mining, since it is based on the extraction and transformation of information not only from historical process data but also from different type of process models. The introduction of this novel concept requires the development of new algorithms and tools for model analysis, reduction and information integration. For this purpose fuzzy systems based modeling, clustering and visualization algorithms have been developed. To handle complex and contradictory goals a novel approach has been worked out based on visualization an interactive evolutionary algorithms. The aim of this paper is to provide an overview of these approaches.