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

[+] Author Affiliations
Jitesh H. Panchal, Janet K. Allen, Farrokh Mistree

Georgia Institute of Technology, Savannah, GA

Christiaan J. J. Paredis

Georgia Institute of Technology, Atlanta, GA

Paper No. DETC2007-35686, pp. 633-647; 15 pages
doi:10.1115/DETC2007-35686
From:
  • ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 6: 33rd Design Automation Conference, Parts A and B
  • Las Vegas, Nevada, USA, September 4–7, 2007
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4807-8 | eISBN: 0-7918-3806-4
  • Copyright © 2007 by ASME

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.

Copyright © 2007 by ASME
Topics: Design

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