Experimental design is a fundamental problem in science and engineering. Traditional 'Design of Experiment' (DOE) approaches focus on minimization of variance. In this work, we propose a new "decision-oriented" DOE approach, which takes into account how the generated data, and subsequently the developed model, will be used in decision making. By doing so, the variance will be distributed in a manner such that its impact on the targeted decision making will be minimal. Our results show that the new decision-oriented experiment design approach significantly outperforms the standard D-optimal design approach. The new design method should be a valuable tool when experiments are conducted for the purpose of making R&D decisions.