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The Value of Information in Clustering Dense Matrices: When and How to Make Use of Information

Published online by Cambridge University Press:  26 May 2022

F. Endress ,
T. Kipouros ,
T. Buker ,
S. Wartzack  and
P. J. Clarkson
F. Endress*
Affiliation:
Department of Engineering, University of Cambridge, United Kingdom TUM School of Engineering and Design, Technical University of Munich, Germany
T. Kipouros
Affiliation:
Department of Engineering, University of Cambridge, United Kingdom
T. Buker
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
S. Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
P. J. Clarkson
Affiliation:
Department of Engineering, University of Cambridge, United Kingdom
*
[email protected]

Abstract

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Characterising a socio-technical system by its underlying structure is often achieved by cluster analyses and bears potentials for engineering design management. Yet, highly connected systems lack clarity when systematically searching for structures. At two stages in a clustering procedure (pre-processing and post-processing) modelled and external information were used to reduce ambiguity and uncertainty of clustering results. A holistic decision making on 1) which information, 2) when, and 3) how to use is discussed and considered inevitable to reliably cluster highly connected systems.

Keywords

cluster analysisdesign structure matrix (DSM)socio-technical systemsdense matricesdesign models
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 703 - 712
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

References

Baldwin, C. Y. and Clark, K. B. (2006), “Modularity in the design of complex engineering systems”, In: Braha, D., Minai, A.A. and Bar-Yam, Y., Complex Engineered Systems. Understanding Complex Systems, Springer, Berlin, Heidelberg, Germany, pp. 175–205. 10.1007/3-540-32834-3_9Google Scholar
Behncke, F., Maurer, D., Schrenk, L., Schmidt, D., and Lindemann, U. (2015), “Clustering technique for DSMs”, Risk and change management in complex systems. Proceedings of the 16th International DSM Conference Paris, France, 2–4 July 2014, Carl Hanser Verlag, Munich, Germany, pp. 177–186. 10.3139/9781569904923.018Google Scholar
Browning, T. R. (2015), “Design structure matrix extensions and innovations: a survey and new opportunities”, IEEE Transactions on Engineering Management, Vol. 63 No. 1, pp. 27–52. 10.1109/TEM.2015.2491283CrossRefGoogle Scholar
Chiriac, N., Hölttä-Otto, K., Lysy, D., and Suh, E. S. (2011), “Level of modularity at different levels of system granularity”, Journal of Mechanical Design, Vol. 133 No. 10, pp. 329–339. 10.1115/1.4005069Google Scholar
Eppinger, S. D. and Browning, T. R. (2012), Design structure matrix methods and applications, MIT press, Cambridge, MA, USA. 10.7551/mitpress/8896.003.0003CrossRefGoogle Scholar
Goldberg, M. K., Hayvanovych, M., and Magdon-Ismail, M. (2010), “Measuring similarity between sets of overlapping clusters”, SocialCom '10: Proceedings of the 2010 IEEE Second International Conference on Social Computing, IEEE Computer Society, Minneapolis, MN, USA, pp. 303–308. 10.1109/SocialCom.2010.50Google Scholar
Heisig, P., Clarkson, P. J., Hemphälä, J., Wadell, C., Bergendahl, M. N., et al. . (2009), Challenges and future fields of research for modelling and management of engineering processes, MMEP White Paper - Report from Workshops with Industry and Academia, Design Society, Cambridge, Munich, Stockholm.Google Scholar
Helmer, R., Yassine, A., and Meier, C. (2010), “Systematic module and interface definition using component design structure matrix”, Journal of Engineering Design, Vol. 21 Nr. 6, pp. 647–675. 10.1080/09544820802563226Google Scholar
Hölttä-Otto, K., Chiriac, N. A., Lysy, D., and Suk Suh, E. (2012), “Comparative analysis of coupling modularity metrics”, Journal of Engineering Design, Vol. 23 No. 10-11, pp. 790–806. 10.1080/09544828.2012.701728Google Scholar
Jung, S. and Simpson, T. W. (2014), “A clustering method using new modularity indices and genetic algorithm with extended chromosomes”, DSM '14: Risk and change management in complex systems. Proceedings of the 16th International DSM Conference Paris, France, 2–4 July 2014, Carl Hanser Verlag, Munich, Germany, pp. 167–176. 10.3139/9781569904923.017Google Scholar
Keller, R., Eckert, C. M., and Clarkson, P. J. (2006), “Matrices or node-link diagrams: Which visual representation is better for visualising connectivity models?”, Information Visualization, Vol. 5 Nr. 1, pp. 62–76. 10.1057/palgrave.ivs.9500116CrossRefGoogle Scholar
Kreimeyer, M. F. (2009), A Structural Measurement System for Engineering Design Processes, Technical University of Munich.Google Scholar
Lancichinetti, A., Fortunato, S., and Kertész, J. (2009), “Detecting the overlapping and hierarchical community structure in complex networks”, New Journal of Physics, Vol. 11 Nr. 3, pp. 033015. 10.1088/1367-2630/11/3/033015CrossRefGoogle Scholar
Little, J. D. C. (1970), “Models and managers: The concept of a decision calculus”, Management Science, Vol. 16 Nr. 8, pp.466–485. 10.1287/mnsc.16.8.b466CrossRefGoogle Scholar
Maier, J. F., Eckert, C. M. and Clarkson, P. J. (2017), “Model granularity in engineering design–concepts and framework”, Design Science, Vol. 3 No. e1. 10.1017/dsj.2016.16Google Scholar
Newman, M. E. J. (2010), Networks: An introduction, second edition, Oxford University Press, Oxford, UK. 10.1093/oso/9780198805090.001.0001Google Scholar
Pimmler, T. U. and Eppinger, S. D. (1994), “Integration analysis of product decompositions”, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers, Minneapolis, MN, USA, pp. 343–351. 10.1115/detc1994-0034Google Scholar
Sarkar, S., Dong, A., Henderson, J. A., and Robinson, P. A. (2014), “Spectral characterization of hierarchical modularity in product architectures”, Journal of mechanical design, Vol. 136 Nr. 1, pp. 0110061–01100612. 10.1115/1.4025490Google ScholarPubMed
Sarkar, S., Henderson, J. A., and Robinson, P. A. (2013), “Spectral characterization of hierarchical network modularity and limits of modularity detection”, PLOS ONE, Vol. 8 No.1, pp. e54383. 10.1371/journal.pone.0054383CrossRefGoogle ScholarPubMed
Schöttl, F. and Lindemann, U. (2015), “Quantifying the complexity of sociotechnical systems – a generic, interdisciplinary approach”, Procedia Computer Science, Vol. 44, pp. 1–10. 10.1016/j.procs.2015.03.019CrossRefGoogle Scholar
Sharman, D. M. and Yassine, A. A. (2004), “Characterizing complex product architectures”, Systems Engineering, Vol. 7 No. 1, pp. 35–60. 10.1002/sys.10056Google Scholar
Thebeau, R. E. (2001), Knowledge management of system interfaces and interactions from product development processes, Massachusetts Institute of Technology.Google Scholar
Wynn, D. C. and Clarkson, P. J. (2018), “Process models in design and development”, Research in Engineering Design, Vol. 29 Nr. 2, pp. 161–202. 10.1007/s00163-017-0262-7Google Scholar
Yu, T.-L., Yassine, A. A., and Goldberg, D. E. (2007), “An information theoretic method for developing modular architectures using genetic algorithms”, Research in Engineering Design, Vol. 18 Nr. 2, pp. 91–109. 10.1007/s00163-007-0030-1Google Scholar