Top-down development following the V-model of systems engineering can help to deal effectively with complexity and uncertainty in systems design. Often in industrial practice, however, it does not support concrete decisions, quantitative requirement decomposition and design measures. This talk presents a simple but effective framework for systems engineers to connect the V-model theory with quantitative design methods, thus enabling a structured process for the systematic distributed design of multi-disciplinary systems subject to uncertainty. The framework includes three distinct steps: first, the system structure is modelled by arranging all dependencies between relevant properties in a polyhierarchy without loops. Second, quantitative bottom-up mappings between properties are established by physical or mathematical models. Third, quantitative top-down mappings are used to compute regions of permissible designs, so-called solution spaces, that are used as requirements. They encompass variability related to epistemic uncertainty and are maximized for the integration of requirements from different disciplines. Several examples with application to concept and product family design are shown to demonstrate the general applicability and the effectiveness of the approach.