Managing Design Process Complexity: A Value-of-Information Based Approach for Scale and Decision Decoupling

Abstract

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? and 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 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.

A SysML Framework for Modeling Contingency Basing

“…A model that includes all components and interactions will typically end up with better designs, but may not be resource efficient. There is typically a trade‐off between design detail and the return from the effort of adding the detail, known as value‐of‐information [Panchal et al., ]. The effort and time spent on detailing an entire contingency base would be limitless due to the ad‐hoc nature of bases and their components.…”

A Normative Approach for Identifying Decision Propagation Paths in Complex Systems

“…The design community has applied such techniques in probabilistic decision theory to characterise the inherent uncertainty of engineering design decisions. Studies such as Aughenbaugh and Paredis (2006), Panchal et al (2009), and Thompson and Paredis (2010) addressed the value of information in dealing with design decisions complexity, and deployed the probabilistic decision theory to express uncertainty in design information (i.e., imprecision, incompleteness). Silver and De Weck (2007) focused on the changeability of a design system over time, and developed time-expanded DN.…”

Separating Design Optimization Problems Into Decision-Based Design Processes

“…Other relevant approaches include using the information gathered during Quality Function Deployment to identify the key design variables [40] and using the value of information to identify simplifications [41].…”