Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-24T05:05:34.398Z Has data issue: false hasContentIssue false
  • English
  • Français

Selecting system architecture: What a single industrial experiment can tell us about the traps to avoid when choosing selection criteria

Published online by Cambridge University Press:  14 July 2016

Marie-Lise Moullec ,
Marija Jankovic  and
Claudia Eckert
Marie-Lise Moullec
Affiliation:
Engineering Design Centre, Department of Engineering, University of Cambridge, Cambridge, United Kingdom
Marija Jankovic*
Affiliation:
Laboratoire de Génie Industriel, Ecole Centrale, Paris, France
Claudia Eckert
Affiliation:
Department of Engineering and Innovation, Open University, Milton Keynes, United Kingdom
*
Reprint requests to: Marija Jankovic, Laboratoire de Génie Industriel, Ecole Centrale Paris, Grande Voie Des Vignes, Châtenay-Malabry 92290, France. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Decisions related to system architecture are difficult because of fuzziness and lack of information combined with often-conflicting objectives. We organized an industrial workshop with the objective of choosing 5 out of 800 architectures. The first step, the identification of selection criteria, proved to be the greatest challenge. As a result, designers selected system architectures that did not satisfy them without being able to explain why. It appeared that most of the difficulties faced by the designers came from the criteria used for architecture selection. This study aims to identify what made the selection criteria difficult to use. The audio recordings of the workshop were transcribed and analyzed in order to identify the obstacles related to the definition and the use of selection criteria. The analysis highlights two issues: the interdisciplinarity of system architecture makes criteria interdependent and the lack of information makes it impossible to define an exhaustive set of criteria. Finally, this study provides recommendations for selecting appropriate selection criteria and insights for future selection support tools dedicated to system architecture design.

Keywords

Decision MakingEarly Design PhasesProduct ArchitectureSelection Criteria
Type
Special Issue Articles
Information
AI EDAM , Volume 30 , Special Issue 3: System Architecture Design , August 2016 , pp. 250 - 262
Copyright
Copyright © Cambridge University Press 2016 

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

Antonsson, E., & Cagan, J. (2001). Formal Engineering Design Synthesis. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Archer, N., & Ghasemzadeh, F. (1999). An integrated framework for project portfolio selection. International Journal of Project Management 17(4), 207–216.CrossRefGoogle Scholar
Brans, J.-P., Vincke, P., & Mareschal, B. (1986). How to select and how to rank projects: the PROMETHEE method. European Journal of Operational Research 24(2), 228–238.CrossRefGoogle Scholar
Byron, L., & Wattenberg, M. (2008). Stacked graphs—geometry & aesthetics. IEEE Transactions on Visualization and Computer Graphics 14(6), 1245–1252.CrossRefGoogle ScholarPubMed
Crawley, E., Weck de, O., Eppinger, S., Magee, C., Moses, J., Seering, W., Schindall, J., Wallace, D., & Whitney, D. (2004). The influence of architecture in engineering systems. Proc. Engineering Systems Monograph, Cambridge, MA.Google Scholar
Fixson, S.K. (2005). Product architecture assessment: a tool to link product, process, and supply chain design decisions. Journal of Operations Management 23(3–4), 345–369.Google Scholar
Girod, M., Elliott, A.C., Burns, N.D., & Wright, I.C. (2003). Decision making in conceptual engineering design: an empirical investigation. Journal of Engineering Manufacture 217(9), 1215–1228.Google Scholar
Henig, M.I., & Buchanan, J.T. (1996). Solving MCDM problems: process concepts. Journal of Multi-Criteria Decision Analysis 5(1), 3–21.Google Scholar
Keeney, R.L., & Gregory, R.S. (2005). Selecting attributes to measure the achievement of objectives. Operations Research 53(1), 1–11.CrossRefGoogle Scholar
Kihlander, I. (2011). Managing concept decision making in product development practice. PhD Thesis, Royal Institute of Technology, Stockholm.Google Scholar
Mingers, J., & Rosenhead, J. (2004). Problem structuring methods in action. European Journal of Operational Research 152(3), 530–554.Google Scholar
Moullec, M.L. (2014). Towards decision support for complex system architecture design with innovation integration in early design stages. Accessed at https://tel.archives-ouvertes.fr/tel-00994935/documentGoogle Scholar
Moullec, M.L., Bouissou, M., Jankovic, M., Bocquet, J.C., Réquillard, F., Maas, O., & Forgeot, O. (2013). Towards system architecture generation and performances assessment under uncertainty using Bayesian networks. Journal of Mechanical Design 135(4), 041002–041013.Google Scholar
Okudan, G.E., & Tauhid, S. (2009). Concept selection methods: a literature review from 1980 to 2008. International Journal of Design Engineering 1(3), 243–277.Google Scholar
Olausson, D., & Berggren, C. (2010). Managing uncertain, complex product development in high tech firms: in search of controlled flexibility. R&D Management 40(4), 383–399.Google Scholar
Pahl, G., Beitz, W., Feldhusen, J., & Grote, K.-H. (2007). Engineering Design: A Systematic Approach (Wallace, K., & Blessing, L., Eds.). London: Springer–Verlag.CrossRefGoogle Scholar
Roy, B., & Bouyssou, D. (1991). Decision-aid: an elementary introduction with emphasis on multiple criteria. Unpublished manuscript, Université de Paris Dauphine, Laboratoire d'analyze et modélisation de systèmes pour l'aide à la décision.Google Scholar
Saaty, T.L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences 1(1), 83–98.CrossRefGoogle Scholar
Scaravetti, D. (2004). Formalisation préalable d'un système de conception. Unpublished manuscript.Google Scholar
Simon, H.A. (1956). Rational choice and the structure of the environment. Psychological Review 63(2), 129.CrossRefGoogle ScholarPubMed
Sinclair, S., Rockwell, G., & Voyant Tools, T. (2012). Voyant Tools. Accessed at http://voyant-tools.org/ on May 10, 2014.Google Scholar
Summers, J.D., & Shah, J.J. (2010). Mechanical engineering design complexity metrics: size, coupling, and solvability. Journal of Mechanical Design 132(2), 21004.Google Scholar
Ullman, D.G. (2001). Robust decision-making for engineering design. Journal of Engineering Design 12(1), 3–13.Google Scholar
Ullman, D.G. (2002). The ideal engineering decision support system. Unpublished manuscript.Google Scholar
Ullman, D.G. (2006). Making Robust Decisions: Decision Management for Technical, Business and Service Teams. London: Trafford Publishing.Google Scholar
Weiss, M.P., & Hari, A. (1997). Problems of concept selection in real industrial environment. Proc. Int. Conf. Engineering Design, ICED'97, pp. 723–728. Tampere, Finland: Design Society.Google Scholar
Whitney, D.E. (2004). Mechanical Assemblies: Their Design, Manufacture, and Role in Product Development. New York: Oxford University Press.Google Scholar