Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-12-01T07:18:46.510Z Has data issue: false hasContentIssue false
  • English
  • Français

Spanning the complexity chasm: A research approach to move from simple to complex engineering systems

Published online by Cambridge University Press:  30 September 2014

Vimal Viswanathan  and
Julie Linsey
Vimal Viswanathan
Affiliation:
Department of Mechanical Engineering, Tuskegee University, Tuskegee, Alabama, USA
Julie Linsey*
Affiliation:
Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
*
Reprint requests to: Julie Linsey, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 801 Ferst Drive NW, Atlanta, GA 30332, USA. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

A multistudy approach is presented that allows design thinking of complex systems to be studied by triangulating causal controlled lab findings with coded data from more complex products. A case study illustration of this approach is provided. During the conceptual design of engineering systems, designers face many cognitive challenges, including design fixation, errors in their mental models, and the sunk cost effect. These factors need to be mitigated for the generation of effective ideas. Understanding the effects of these challenges in a realistic and complex engineering system is especially difficult due to a variety of factors influencing the results. Studying the design of such systems in a controlled environment is extremely challenging because of the scale and complexity of such systems and the time needed to design the systems. Considering these challenges, a mixed-method approach is presented for studying the design thinking of complex engineering systems. This approach includes a controlled experiment with a simple system and a qualitative cognitive-artifacts study on more complex engineering systems followed by the triangulation of results. The triangulated results provide more generalizable information for complex system design thinking. This method combines the advantages of quantitative and qualitative study methods, making them more powerful while studying complex engineering systems. The proposed method is illustrated further using an illustrative study on the cognitive effects of physical models during the design of engineering systems.

Keywords

Cognitive-Artifact StudyComplex SystemsConceptual DesignMixed MethodsPhysical Models
Type
Special Issue Articles
Information
AI EDAM , Volume 28 , Issue 4: Design of Complex Engineered Systems , November 2014 , pp. 369 - 384
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Abildso, C., Zizzi, S., Gilleland, D., Thomas, J., & Bonner, D. (2010). A mixed methods evaluation of a 12-week insurance-sponsored weight management program incorporating cognitive-behavioral counseling. Journal of Mixed Methods Research 4(4), 278–294.Google Scholar
Altshuller, G., Shulyak, L., & Rodman, S. (1997). 40 Principles: TRIZ Keys to Innovation. Worcester, MA: Technical Innovation Center.Google Scholar
Altshuller, G.S. (1984). Creativity as an Exact Science: The Theory of the Solution of Inventive Problems. Amsterdam: Gordon & Breach.Google Scholar
Arkes, H.R., & Blumer, C. (1985). The psychology of sunk cost. Organizational Behavior and Human Decision Processes 35(1), 124–140.Google Scholar
Atman, C.J., Adams, R.S., Cardella, M.E., Turns, J., Mosborg, S., & Saleem, J. (2007). Engineering design processes: a comparison of students and expert practitioners. Journal of Engineering Education 96(4), 359–379.Google Scholar
Atman, C.J., Kilgore, D., & McKenna, A. (2008). Characterizing design learning: a mixed-methods study of engineering designers' use of language. Journal of Engineering Education 97(3), 309–326.CrossRefGoogle Scholar
Auerbach, C.F., & Silverstein, L.B. (2003). Qualitative Data: An Introduction to Coding and Analysis. New York: New York University Press.Google Scholar
Aurigemma, J., Chandrasekharan, S., Nersessian, N.J., & Newstetter, W. (2013). Turning experiments into objects: the cognitive processes involved in the design of a lab-on-a-chip device. Journal of Engineering Education 102(1), 117–140.Google Scholar
Blessing, L.T., & Chakrabarti, A. (2009). DRM: A Design Research Methodology. London: Springer.Google Scholar
Boujut, J.F., & Blanco, E. (2003). Intermediary objects as a means to foster co-operation in engineering design. Computer Supported Cooperative Work 12(2), 205–219.CrossRefGoogle Scholar
Cagan, J., Dinar, M., Shah, J.J., Leifer, L., Linsey, J., Smith, S., & Vargas-Hernandez, N. (2013). Empirical studies of design thinking: past, present, future. Proc. ASME Int. Design Engineering Technical Confs. Computers and Information in Engineering Conf., Paper No. DETC2013-13302, Portland, OR, Aug 4–7.Google Scholar
Carlile, P.R. (2002). A pragmatic view of knowledge and boundaries: boundary objects in new product development. Organization Science 13(4), 442–455.Google Scholar
Chakrabarti, A., Morgenstern, S., & Knaab, H. (2004). Identification and application of requirements and their impact on the design process: a protocol study. Research in Engineering Design 15(1), 22–39.Google Scholar
Christensen, B.T., & Schunn, C.D. (2005). The relationship of analogical distance to analogical function and pre-inventive structure: the case of engineering design. Creative Cognition: Analogy and Incubation 35(1), 29–38.Google Scholar
Chrysikou, E.G., & Weisberg, R.W. (2005). Following the wrong footsteps: fixation effects of pictorial examples in a design problem-solving task. Journal of Experimental Psychology: Learning, Memory, and Cognition 31(5), 1134–1148.Google Scholar
Clark-Carter, D. (1997). Doing Quantitative Psychological Research: From Design to Report. London: Psychology Press/Erlbaum.Google Scholar
Creamer, E.G., & Ghoston, M. (2013). Using a mixed methods content analysis to analyze mission statements from colleges of engineering. Journal of Mixed Methods Research 7(2), 110–120.CrossRefGoogle Scholar
Crede, E., & Borrego, M. (2013). From ethnography to items: a mixed methods approach to developing a survey to examine graduate engineering student retention. Journal of Mixed Methods Research 7(1), 62–80.Google Scholar
Creswell, J.W., & Clark, V.L.P. (2007). Designing and Conducting Mixed Methods Research. Thousand Oaks, CA: Sage.Google Scholar
Dorst, K., & Cross, N. (2001). Creativity in the design process: co-evolution of problem–solution. Design Studies 22(5), 425–437.Google Scholar
Fish, J. (2004). Cognitive catalysis: sketches for a time-lagged brain. In Design Representation (Goldschmidt, G., & Porter, W., Eds.), pp. 151–184. London: Springer.Google Scholar
Fu, J.-F., Fenton, R.G., & Cleghorn, W.L. (1991). A mixed integer-discrete-continuous programming method and its application to engineering design optimization. Engineering Optimization 17(4), 263–280.Google Scholar
Gentner, D., & Stevens, A. (1983). Mental Models. Mahwah, NJ: Erlbaum.Google Scholar
Gero, J.S., & McNeill, T. (1998). An approach to the analysis of design protocols. Design Studies 19(1), 21–61.CrossRefGoogle Scholar
Goldschmidt, G. (2007). To see eye to eye: the role of visual representations in building shared mental models in design teams. CoDesign 3(1), 43–50.CrossRefGoogle Scholar
Haller, L., & Cullen, C. (2004). Design Secrets: Products 2: 50 Real-Life Projects Uncovered. Beverly, MA: Rockport.Google Scholar
Hannah, R., Michaelrag, A., & Summers, J. (2008). A proposed taxonomy for physical prototypes: structure and validation. Proc. ASME Int. Design Engineering Technical Conf., Paper No. DETC2008-49976, New York, August 3–6.Google Scholar
Harrison, S., & Minneman, S. (1997). A bike in hand: a study of 3-D objects in design. In Analysing Design Activity (Cross, N., et al. , Eds.), pp. 417–436. New York: Wiley.Google Scholar
Holcomb, J.H., & Evans, D.A. (1987). The effect of sunk costs on uncertain decisions in experimental markets. Journal of Behavioral Economics 16(3), 59–66.Google Scholar
Horton, G.I., & Radcliffe, D.F. (1995). Nature of rapid proof-of-concept prototyping. Journal of Engineering Design 6(1), 3–16.CrossRefGoogle Scholar
Hsiao, C., Malak, R., Tumer, I.Y., & Doolen, T. (2013). Empirical findings about risk and risk mitigating actions from a legacy archive of a large design organization. Procedia Computer Science 16, 844–852.Google Scholar
Hutchins, E., & Lintern, G. (1995). Cognition in the Wild. Cambridge, MA: MIT Press.Google Scholar
IDSA. (2003). Design Secrets: Products. Beverly, MA: Rockport.Google Scholar
Jansson, D., & Smith, S. (1991). Design fixation. Design Studies 12(1), 3–11.Google Scholar
Johnson, R.B., & Onwuegbuzie, A.J. (2004). Mixed methods research: a research paradigm whose time has come. Educational Researcher 33(7), 14–26.Google Scholar
Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica 47(2), 263–291.Google Scholar
Keeney, R.L., & Raiffa, H. (1993). Decisions With Multiple Objectives: Preferences and Value Tradeoffs. Cambridge: Cambridge University Press.Google Scholar
Kelley, T. (2001). Prototyping is the shorthand of innovation. Design Management Journal 12(3), 35–42.Google Scholar
Kempton, W. (1986). Two theories of home heat control. Cognitive Science 10(1), 75–90.Google Scholar
Kiriyama, T., & Yamamoto, T. (1998). Strategic knowledge acquisition: a case study of learning through prototyping. Knowledge-Based Systems 11(7–8), 399–404.Google Scholar
Kirk, R.E. (1982). Experimental Design. Monterey, CA: Brooks/Cole.Google Scholar
Kurtoglu, T., Campbell, M.I., Arnold, C.B., Stone, R.B., & Mcadams, D.A. (2009). A component taxonomy as a framework for computational design synthesis. Journal of Computing and Information Science in Engineering 9, 011007.Google Scholar
Lemons, G., Carberry, A., Swan, C., Jarvin, L., & Rogers, C. (2010). The benefits of model building in teaching engineering design. Design Studies 31(3), 288–309.Google Scholar
Lidwell, W., Holden, K., & Butler, J. (2003). Universal Principles of Design. Beverly, MA: Rockport.Google Scholar
Linsey, J., Clauss, E.F., Kurtoglu, T., Murphy, J.T., Wood, K.L., & Markman, A.B. (2011). An experimental study of group idea generation techniques: understanding the roles of idea representation and viewing methods. ASME Transactions: Journal of Mechanical Design 133(3), 031008.Google Scholar
Linsey, J.S., Tseng, I., Fu, K., Cagan, J., Wood, K.L., & Schunn, C. (2010). A study of design fixation, its mitigation and perception in engineering design faculty. ASME Transactions: Journal of Mechanical Design 132(4), 041003.Google Scholar
McKim, R.H. (1972). Experiences in Visual Thinking. Boston: PWS.Google Scholar
McMillan, J.H., & Schumacher, S. (2014). Research in Education: Evidence-Based Inquiry. Essex: Pearson Education.Google Scholar
MITRE Systems Enginering Process Office. (2005). Perspectives on complex-system engineering. Collaborations 3(2). Accessed on September 16, 2013, at http://necsi.edu/necsi/mitrecoll3.2.pdfGoogle Scholar
Nelson, B.A., Wilson, J.O., Rosen, D., & Yen, J. (2009). Refined metrics for measuring ideation effectiveness. Design Studies 30(6), 737–743.Google Scholar
Nersessian, N.J. (1995). Opening the black box: cognitive science and history of science. OSIRIS: Constructing Knowledge in the History of Science 10, 194–211.CrossRefGoogle Scholar
Ott, L., & Longnecker, M. (2008). An Introduction to Statistical Methods and Data Analysis. Belmont, CA: Brooks/Cole.Google Scholar
Otto, K.N., & Wood, K.L. (2001). Product Design: Techniques in Reverse Engineering and New Product Development. New York: Prentice Hall.Google Scholar
Pahl, G., & Beitz, W. (2003). Engineering Design: A Systematic Approach. London: Springer.Google Scholar
Paton, B., & Dorst, K. (2011). Briefing and reframing: a situated practice. Design Studies 32(6), 573–587.Google Scholar
Petre, M. (2004). How expert engineering teams use disciplines of innovation. Design Studies 25(5), 477–493.Google Scholar
Purcell, A., & Gero, J. (1992). Effects of examples on the results of a design activity. Knowledge-Based Systems 5(1), 82–91.Google Scholar
Purcell, A.T., & Gero, J.S. (1996). Design and other types of fixation. Design Studies 17(4), 363–383.Google Scholar
Saunders, M.N., Seepersad, C.C., & Hölttä-Otto, K. (2009). The characteristics of innovative, mechanical products. ASME Transactions: Journal of Mechanical Design 133(2), 021009.Google Scholar
Schon, D.A., & Wiggins, G. (1992). Kinds of seeing and their functions in designing. Design Studies 13(2), 135–156.Google Scholar
Shah, J.J., Kulkarni, S.V., & Vargas-Hernandez, N. (2000). Evaluation of idea generation methods for conceptual design: effectiveness metrics and design of experiments. ASME Transactions: Journal of Mechanical Design 122(4), 377–384.Google Scholar
Shah, J.J., Smith, S.M., & Vargas-Hernandez, N. (2003 a). Metrics for measuring ideation effectiveness. Design Studies 24(2), 111–134.Google Scholar
Shah, J.J., Smith, S.M., Vargas-Hernandez, N., Gerkens, D.R., & Wulan, M. (2003 b). Empirical studies of design ideation: alignment of design experiments with lab experiments. Proc. ASME Int. Design Engineering Technical Confs., Paper No. DETC2003/DTM-48679, Chicago, September 2–6.Google Scholar
Sheldon, D.F. (2006). Design review 2005/2006—the ever increasing maturity of design research papers and case studies. Journal of Engineering Design 17(6), 481–486.Google Scholar
Sushkov, V., Mars, N.J., & Wognum, P. (1995). Introduction to TIPS: a theory for creative design. Artificial Intelligence in Engineering 9(3), 177–189.Google Scholar
Suwa, M., & Tversky, B. (1996). What architects see in their sketches: implications for design tools. Proc. Conf. Companion on Human Factors in Computing Systems: Common Ground, pp. 191–192. New York: ACM.Google Scholar
Tabachnick, B.G., & Fidell, L.S. (2007). Experimental Designs Using ANOVA. Belmont, CA: Thomson/Brooks/Cole.Google Scholar
Tashakkori, A., & Teddlie, C. (1998). Mixed Methodology: Combining Qualitative and Quantitative Approaches. Thousand Oaks, CA: Sage.Google Scholar
Teegavarapu, S., & Summers, J.D. (2008). Case study method for design research. Proc. ASME 2008 Int. Design Engineering Technical Conf., Computers and Information in Engineering Conf., Paper No. DETC2008-49980, New York, August 3–6.Google Scholar
Tseng, I., Moss, J., Cagan, J., & Kotovsky, K. (2008). The role of timing and analogical similarity in the stimulation of idea generation in design. Design Studies 29(3), 203–221.Google Scholar
Veisz, D., Joshi, S., & Summers, J.d. (2012). Computer-aided design versus sketching: an exploratory case study. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26(3), 317–335.Google Scholar
Viswanathan, V., & Linsey, J. (2013 a). Design fixation and its mitigation: a study on the role of expertise. ASME Transactions: Journal of Mechanical Design 135(5), 051008.Google Scholar
Viswanathan, V., & Linsey, J. (2013 b). Role of sunk cost in engineering idea generation: an experimental investigation. ASME Transactions: Journal of Mechanical Design 135(12), 121002.Google Scholar
Viswanathan, V.K., Esposito, N., & Linsey, J. (2012). Training tomorrow's designers: a study on design fixation. Proc. ASEE Annual Conf., Paper No. 2012-4925, San Antonio, TX, June 10–13.Google Scholar
Viswanathan, V.K., & Linsey, J. (2012). Physical models and design thinking: a study of functionality, novelty and variety of ideas. ASME Transactions: Journal of Mechanical Design 134(9), 091004.Google Scholar
Viswanathan, V.K., & Linsey, J.S. (2009). Enhancing student innovation: physical models in the idea generation process. Proc. ASEE/IEEE Frontiers in Education Conf., Paper No. 978-1-4244-4714-5/09, San Antonio, TX, October 18–21.Google Scholar
Viswanathan, V.K., & Linsey, J.S. (2010). Physical models in idea generation—hindrance or help? Proc. Int. Conf. Design Theory and Methodology, Paper No. DETC2010-28327, Montreal, August 15–18.Google Scholar
Viswanathan, V.K., & Linsey, J.S. (2011 a). Design fixation in physical modeling: an investigation on the role of sunk cost. Proc. Int. Conf. Design Theory and Methodology, Paper No. DETC2011-47862, Washington, DC, August 29–31.Google Scholar
Viswanathan, V.K., & Linsey, J.S. (2011 b). Understanding physical models in design cognition: a triangulation of qualitative and laboratory studies. Proc. ASEE/IEEE Frontiers in Education Conf., Paper No. 978-1-61284-469-5/11, Rapid City, SD, October 12–16.Google Scholar
Ward, A., Liker, J.K., Cristiano, J.J., & Sobek, D.K. (1995). The second Toyota paradox: how delaying decisions can make better cars faster. Sloan Management Review 36, 43.Google Scholar
Westmoreland, S.N. (2012). Design thinking: cognitive patterns in engineering design documentation. PhD Thesis. University of Maryland, College Park.Google Scholar
Wong, Y.Y. (1992). Rough and ready prototypes: lessons from graphic design. Proc. Posters and Short Talks of the 1992 SIGCHI Conf. Human Factors in Computing Systems, Paper No. 1125094, pp. 83–84.Google Scholar
Yang, M.C. (2005). A study of prototypes, design activity, and design outcome. Design Studies 26(6), 649–669.Google Scholar
Youmans, R.J. (2011). The effects of physical prototyping and group work on the reduction of design fixation. Design Studies 32(2), 115–138.Google Scholar