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Multiattribute interaction design: An integrated conceptual design process for modeling interactions and maximizing value

Published online by Cambridge University Press:  10 June 2011

Andrew Baratz Ehrich  and
John Riker Haymaker
Andrew Baratz Ehrich
Affiliation:
Center for Integrated Facility Engineering, Stanford University, Stanford, California, USA
John Riker Haymaker*
Affiliation:
Civil and Environmental Engineering, Center for Integrated Facility Engineering, Stanford University, Stanford, California, USA
*
Reprint requests to: John Haymaker, 477 Vermont Street, San Francisco, CA 94107, USA. E-mail: [email protected]
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Abstract

Integrated design synthesizes combinations of options into alternatives that take advantage of interactions to maximize multidisciplinary value. As resources become further constrained, options become more numerous, and goals become increasingly complex, it is more critical and more challenging for design teams to find these integrated solutions. Theory proposes the integration of transformation, flow, and value views as necessary to support such integrated design. This paper develops requirements for these views that encourage flexible yet systematic integrated conceptual design processes. It then illustrates how these requirements are only partially satisfied by current design management systems, provides motivating case studies, and introduces a new framework, multiattribute interaction design (MAID), to fill this void by systematically guiding design teams to explicitly consider the potential interactions of options and the resulting value of design solutions. The paper defines the terms relevant to design space exploration and interactions. It then defines the MAID method and specifies metrics and a process for its validation. Initial laboratory charettes carry out first validations, illustrating evidence for how MAID can help integrate transformation, flow, and value views and lead teams of students to discover and record more interactions in a relatively short amount of time. The paper then lists future work required to further develop and validate MAID.

Keywords

Conceptual DesignIntegrated DesignInteractionValue
Type
Practicum Article
Information
AI EDAM , Volume 26 , Issue 1 , February 2012 , pp. 85 - 101
Copyright
Copyright © Cambridge University Press 2012

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References

REFERENCES

Akao, Y. (2004). Quality Function Deployment: Integrating Customer Requirements Into Product Design (Mazur, G.H., Trans.). Cambridge, MA: Productivity Press.Google Scholar
Akin, Ö. (2001). Variants of design cognition. In Design Knowing and Learning: Cognition in Design Education (Eastman, C., Newstetter, W., & McCracken, M., Eds.), pp. 105–124. New York: Elsevier.CrossRefGoogle Scholar
Ballard, G., & Koskela, L. (1998). On the agenda of design management research. Proc. 6th Annual Conf. Int. Group for Lean Construction, p. 13.Google Scholar
Balling, R., & Rawlings, M.R. (2000). Collaborative optimization with disciplinary conceptual design. Structural and Multidisciplinary Optimization 20(3), 232–241.CrossRefGoogle Scholar
Barrie, D.S., & Paulson, B.C. (1992). Professional Construction Management: Including CM, Design-Construct, and General Contracting, 3rd ed.New York: McGraw–Hill.Google Scholar
Chachere, J., & Haymaker, J. (2011). Framework for measuring rationale clarity of AEC design decisions. Journal of Architectural Engineering. Advanced online publication. doi:10.1061/(ASCE)AE.1943-5568.0000036CrossRefGoogle Scholar
Clausing, D. (1994). Total Quality Development. New York: ASME Press.Google Scholar
Clevenger, C., & Haymaker, J. (2010). Metrics to Assess Design Guidance, Technical Report No. 191, Stanford University Center for Integrated Facility Engineering. Accessed at http://cife.stanford.edu/online.publications/TR191.pdfGoogle Scholar
Corcoran, B., Walton, K., Ehrich, A., & Stoutenburg, E. (2008). Green Dorm Energy Technology Decision. Accessed at http://cgi.stanford.edu/~class-cee115-1076/wiki_win08/index.php?n=Main.Final6Google Scholar
Dong, J. (2008). Special report: train tunnels in Palo Alto? Palo Alto Online, September 26. Accessed at http://www.paloaltoonline.com/news/show_story.php?id=9431Google Scholar
Edvardsson, K., & Hansson, S.O. (2005). When is a goal rational? Social Choice and Welfare 24(2), 343–361.CrossRefGoogle Scholar
EHDD Architects. (2006). Stanford University Green Dorm Feasibility Study. Accessed at http://www.stanford.edu/group/greendorm/greendorm/feasibility_study/GD_fullreport_060405_forSCREEN.pdfGoogle Scholar
Finger, S., & Dixon, J.R. (1989). A review of research in mechanical engineering design: Part 1. Descriptive, prescriptive, and computer-based models of design processes. Research in Engineering Design 1, 51–67.CrossRefGoogle Scholar
Gallaher, M.P., O'Connor, A.C., Dettabarn, J.L., & Gilday, L.T. (2004). Cost Analysis of Inadequate Operability in the U.S. Capital Facilities Industry, Report No. GCR 04-867. Gaithersburg, MD: National Institute of Standards and Technology.CrossRefGoogle Scholar
Girerd, A.R. (2005). A rapid, flexible approach to rapid tradespace definition and exploration. 2005 IEEE Aerospace Conf., p. 13.0101.CrossRefGoogle Scholar
Green, S.D. (1994). Beyond value engineering: SMART value management for building projects. International Journal of Project Management 12(1), 49–56.CrossRefGoogle Scholar
Griffin, A., & Hauser, J.R. (1993). The voice of the customer. Marketing Science 12, 1–27.CrossRefGoogle Scholar
Hauser, J.R., & Clausing, D.P. (1988). The house of quality. Harvard Business Review 66(May–June), 63–73.Google Scholar
Hawken, P., Lovins, A., & Lovins, H. (1999). Natural Capitalism: Creating the Next Industrial Revolution. New York: Little, Brown and Company.Google Scholar
Haymaker, J., & Chachere, J. (2006). Coordinating goals, preferences, options, and analyses for stanford living laboratory feasibility study. In Intelligent Computing in Engineering and Architecture (Smith, I., Ed.), pp. 320–327. New York: Springer.CrossRefGoogle Scholar
Haymaker, J., Chachere, J., & Senescu, R. (in press). Measuring and improving rationale clarity in a university office building design process. Journal of Architectural Engineering.Google Scholar
Holzer, D., Hough, R., & Burry, M. (2007). Parametric design and structural optimisation for early design exploration. International Journal of Architectural Computing 5(4), 625–643.CrossRefGoogle Scholar
Howard, R. (1988). Decision analysis: practice and promise. Management Science 34(6), 679–695.CrossRefGoogle Scholar
Ïpek, E., McKee, S., Caruana, R., de Supinski, B., & Schulz, M. (2006). Efficiently exploring architectural design spaces via predictive modeling. Proc. 12th Int. Conf. Architectural Support for Programming Languages and Operating Systems, pp. 195–206.CrossRefGoogle Scholar
Jansson, D.G. (1990). Conceptual engineering design. In Design Management (Oakley, M., Ed.). Oxford: Basil Blackwell.Google Scholar
Kam, C. (2005). Dynamic Decision Breakdown Structure: Ontology, Methodology, and Framework for Information Management in Support of Decision-Enabling Tasks in the Building Industry, Technical Report No. 164, Stanford University, Civil and Environmental Engineering Department. Accessed at http://cife.stanford.edu/online.publications/TR164.pdfGoogle Scholar
Keeney, R. (2007). Developing objectives and attributes. In Advances in Decision Analysis: From Foundations to Applications (Edwards, W., Miles, R.F. Jr., & von Winterfeldt, D., Eds.), pp. 104–128. New York: Cambridge University Press.CrossRefGoogle Scholar
Keeney, R., & von Winterfeldt, D. (2007). Practical value models. In Advances in Decision Analysis: From Foundations to Application (Edwards, W., Miles, R.F. Jr., & von Winterfeldt, D., Eds.), pp. 232–252. New York: Cambridge University Press.CrossRefGoogle Scholar
Krishnamurti, R. (2006). Explicit design space? Artificial Intelligence for Engineering, Design, Analysis and Manufacturing 20(2), 95–103.CrossRefGoogle Scholar
Krishnan, V., & Ulrich, K.T. (2001). Product development decisions: a review of the literature. Management Science 47(1), 1–21.Google Scholar
Kunz, J., & Fischer, M. (2007). Virtual Design and Construction: Themes, Case Studies, and Implementation Suggestions, Center for Integrated Facility Engineering Working Paper No. 097, Stanford University, Civil and Environmental Engineering Department. Accessed at http://cife.stanford.edu/online.publications/WP097.pdfGoogle Scholar
Lewis, K.E., Chen, W., & Schmidt, L. (Eds.). (2007). Decision Making in Engineering Design. New York: ASME Press.Google Scholar
McDonough, W., & Braungart, M. (1998). The NEXT industrial revolution. Atlantic Monthly 282(4), 82–90.Google Scholar
Pahl, G., Beitz, W., Feldhusen, J., & Grote, K.H. (2007). Engineering Design: A Systematic Approach (Wallace, K., & Blessing, L., Trans.), 3rd ed.London: Springer–Verlag.Google Scholar
Pugh, S. (1981). Concept selection: a method that works. Proc. Int. Conf. Engineering Design, ICED 81.Google Scholar
Rechtin, E. (1991). Architecting: Creating and Building Complex Systems. Englewood Cliffs, NJ: Prentice–Hall.Google Scholar
Roedel, H., Talbot, L., & Bian, J. (2009). Palo Alto high speed rail project. Accessed at http://www.stanford.edu/group/narratives/classes/08-09/CEE215/Projects/palo-alto-rail-link/goals.pngGoogle Scholar
Rogers, J.L. (1989). A Knowledge-Based Tool for Multilevel Decomposition of a Complex Design Problem. NASA TP 2903.Google Scholar
Shah, J., Vargas-Hernandez, N., & Smith, S. (2003). Metrics for measuring ideation effectiveness. Design Studies 24(1), 111–134.CrossRefGoogle Scholar
Simon, H. (1977). The New Science of Management Decision, 3rd revised ed. (1st ed. 1960). Englewood Cliffs, NJ: Prentice–Hall.Google Scholar
Simpson, T.W., Rosen, D., Allen, J.K., & Mistree, F. (1998). Metrics for assessing design freedom and information certainty in the early stages of design. Journal of Mechanical Design 120(4), 628–635.CrossRefGoogle Scholar
Sobek, D., & Ward, A. (1996). Principles from Toyota's set-based concurrent engineering process. Proc. ASME Computers in Engineering Conf., p. 96, Paper No. DETC1996/DTM-1510.Google Scholar
Stanford Green Dorm. (2006). Our project. Accessed at http://www.stanford.edu/group/greendorm/project.html on May 24, 2009.Google Scholar
Sutton, R. (2002). Weird Ideas That Work: 11.5 Practices for Promoting, Managing, and Sustaining Innovation. New York: Free Press.Google Scholar
Thurston, D. (2007). Utility function fundamentals. In Decision Making in Engineering Design (Lewis, K.E., Chen, W., & Schmidt, L., Eds.). New York: ASME Press.Google Scholar
Thurston, D.L., & Carnahan, J.V. (1992). Fuzzy ratings and utility analysis in preliminary design evaluation of multiple attributes. Journal of Mechanical Design 114(4), 648–659.CrossRefGoogle Scholar
Weas, A., & Campbell, M. (2004). Rediscovering the analysis of interconnected decision areas. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18(3), 227–243.CrossRefGoogle Scholar
Weber, R., & Condoor, S. (1998). Conceptual design using a synergistically compatible morphological matrix. IEEE Frontiers in Education Conference 1, 171–176.CrossRefGoogle Scholar
Whitney, D.E., Anderson, M., Cadet, C., Fine, C., Gossard, D., Thornton, A., Groover, M., Nagel, R., Ozsoy, T., Stenger, H., Anderson, M.A., Chang, M., Cunningham, T., Gutwald, P., Hardy, G., Laukaitis, J.J., Lee, D., Mantripragada, R., Pomponi, R., Soman, N., Baykan, E., Greif, P., Johnston, T., Kim, D.H., Marquette, D., Meixell, M., & Roth, S. (1995). Agile pathfinders in the aircraft and automobile industries—a progress report. Agility Forum 4th Annual Conf. Proc., pp. 245–257.Google Scholar
Woodbury, R.F., & Burrow, A.L. (2006). Whither design space. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 20(2), 63–82.CrossRefGoogle Scholar
Yassine, A., & Braha, D. (2003). Complex concurrent engineering and the design structure matrix method. Concurrent Engineering Research and Applications 11(3), 165–176.CrossRefGoogle Scholar
Zwicky, F. (1948). The Morphological Method of Analysis and Construction (Courant Anniversary Volume). New York: Interscience Publishers.Google Scholar