Design-processes for multiscale, multifunctional systems are inherently complex due to the interactions between scales, functional requirements, and the resulting design decisions. While complex design-processes that consider all interactions lead to better designs, simpler design-processes where some interactions are ignored are faster and resource efficient. In order to determine the right level of simplification of design-processes, designers are faced with the following questions: (a) How should complex design-processes be simplified without affecting the resulting product performance? (b) How can designers quantify and evaluate the appropriateness of different design-process alternatives? In this paper, the first question is addressed by introducing a method for determining the appropriate level of simplification of design-processes—specifically through decoupling of scales and decisions in a multiscale problem. The method is based on three constructs: interaction patterns to model design-processes, intervals to model uncertainty resulting from decoupling of scales and decisions, and value-of-information based metrics to measure the impact of simplification on the final design outcome. The second question is addressed by introducing a value-of-information based metric called the improvement potential for quantifying the appropriateness of design-process alternatives from the standpoint of product design requirements. The metric embodies quantitatively the potential for improvement in the achievement of product requirements by adding more information for design decision-making. The method is illustrated via a datacenter cooling system design example.