{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T14:17:43Z","timestamp":1761488263151,"version":"3.41.2"},"reference-count":40,"publisher":"ASME International","issue":"2","content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2009,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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\u2014specifically 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.<\/jats:p>","DOI":"10.1115\/1.3130791","type":"journal-article","created":{"date-parts":[[2009,6,4]],"date-time":"2009-06-04T22:25:47Z","timestamp":1244154347000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":14,"title":["Managing Design-Process Complexity: A Value-of-Information Based Approach for Scale and Decision Decoupling"],"prefix":"10.1115","volume":"9","author":[{"given":"Jitesh H.","family":"Panchal","sequence":"first","affiliation":[{"name":"School of Mechanical and Materials Engineering, Washington State University, P.O. Box 642920, Pullman, WA 99164-2920"}]},{"given":"Christiaan J. J.","family":"Paredis","sequence":"additional","affiliation":[{"name":"Systems Realization Laboratory, G.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332"}]},{"given":"Janet K.","family":"Allen","sequence":"additional","affiliation":[{"name":"Systems Realization Laboratory, George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 210 Technology Circle, Savannah, GA 31407"}]},{"given":"Farrokh","family":"Mistree","sequence":"additional","affiliation":[{"name":"Systems Realization Laboratory, George W. 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