Robot systems promise high potential for improvement of production processes in small and medium-sized enterprises (SME). Often, reliable data for the assessment of costs and benefits of robot systems is missing, because robot systems are special machinery and because the required information is distributed among component manufacturer, system integrator and end-user. This thesis addresses the problem of cost-benefit assessment of industrial robot systems in the face of uncertain and incomplete information by combining methods from business economics and from knowledge management in robotics in a cost-benefit model. The resulting method allows efficient decision support in the planning and realization of SME-type robot systems. A test implementation is used to evaluate the method for two realistic use cases.
