Managing a complex project using a Risk-Risk Multiple Domain Matrix
Keywords:
Clustering, Risk interdependency, Complex Project Management, Multiple Domain MatrixAbstract
This communication aims at presenting a clustering methodology applied to a complex project consisting of the delivery of three interdependent sub-systems. This enables small and complementary task forces to be constituted, enhancing the communication and coordination on transverse issues related to the complexity of the whole system. The problem is to gather and exploit data for such systems, with numerous and heterogeneous risks of different domains (product, process, organization). The method consists in regrouping actors through the clustering of the risks they own. The result is a highlight on important and transverse risk interdependencies, within and between projects. These should not be neglected in order to avoid potential severe issues, whether during the project or during the exploitation of its deliverable. An application on a real program of plant implementation in the CEA-DAM is presented, with a sensitivity analysis of the clustering results to the inputs and chosen configurations of the problem.
References
Ben-Arieh, D., & Sreenivasan, R. (1999). Information Analysis in a Distributed Dynamic Group Technology Method. International Journal of Production Economics, 60-61, 427–432.
Blondel, V., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, P10008.
Bühler, T., & Hein, M. (2009). Spectral clustering based on the graph p-Laplacian. In Proceedings of the 26th Annual International Conference on Machine Learning (pp. 81–88). ACM.
Clauset, A., Moore, C., & Newman, M. (2006). Structural Inference of Hierarchies in Networks. In Proceedings of the 23rd International Conference on Machine Learning. Pittsburgh, PA.
Clauset, A., Newman, M. E. J., & Moore, C. (2004). Finding community structure in very large networks. Phys. Rev. E, 70(066111), 1–6.
Dong, Y., Zhuang, Y., Chen, K., & Tai, X. (2006). A hierarchical clustering algorithm based on fuzzy graph connectedness. Fuzzy Sets and Systems, 157, 1760–1774.
Filippone, M., Camastra, F., Masulli, F., & Rovetta, S. (2008). A survey of kernel and spectral methods for clustering. Pattern Recognition, 41.
Fredrik Borjesson, & Katja Holtta-Otto. (2012). IMPROVED CLUSTERING ALGORITHM FOR DESIGN STRUCTURE MATRIX. In ASME 2012 International Design Engineering Technical Conferences & Computers and Information in Engineering ConferencePhysical Review.
Freeman, L. (1977). Set of measures of centrality based on betweenness. Sociometry, 40, 35–41.
Girvan, M., & Newman, M. (2002). Community structure in social and biological networks. Proceedings of National Academy of Science (PNAS), 99(12), 7821– 7826. doi:doi:10.1073/pnas.122653799
Gomez, C., Sanchez-Silva, M., & Duenas-Osorio, L. (2011). Clustering methods for risk assessment of infrastructure network systems. Applications of Statistics and Probability in Civil Engineering, 1389–1397.
Gusfield, D. (1997). Algorithms on Strings, Trees and Sequences. Cambridge University Press.
Gutierrez-Fernandez, C. I. (1998). Integration Analysis of Product Architecture to Support Effective Team Co-Location. Massachussets Institute of Technology.
Helmer, R., Yassine, A., & Meier, C. (2010). Systematic Module and Interface Definition using Component Design Structure Matrix. Journal of Engineering Design, 21(6), 647–675.
Hennig, C., & Hausdorf, B. (2006). A Robust Distance Coefficient Between Distribution Areas Incorporating Geographic Distances. Systematic Biology, 55(1), 170–175.
Idicula, J. (1995). Planning for Concurrent Engineering. Gintic Institute, Singapore.
Kannan, R., Vempala, S., & Vetta, A. (2001). On Clusterings : Good, Bad and Spectral. Working paper.
Karp, R. M. (1976). Probabilistic analysis of partitioning algorithms for the travelling salesman problem in the plane. Mathematics of Operations Research, 2(3), 209–224.
Kim, S. (2003). Graph theoretic sequence clustering algorithms and their applications to genome comparison. In J. T. L. Wu, C.H., Wang, P., Wang (Ed.), Chapter 4 in Computational Biology and Genome In-formatics (in Wu, C.H.). Singapore: World Scientific.
Leicht, E. A., & Newman, M. E. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.
Marle, F., & Vidal, L. (2014). Forming risk clusters in projects to improve coordination between risk owners. Journal of Management in Engineering.
McQuenn, J. (1967). Some methods for classification and analysis of multivariate observations. Computers and Chemistry, 4, 257–272.
Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Proc. Natl. Acad. Sci. USA, 103, 8577–8582.
Newman, M. E. J., & Web, W. (2003). Properties of highly clustered networks, 1–7.
Sherali, H., & Desai, J. (2005). A global optimization RLT-based approach for solving the hard clustering problem. Journal of Global Optimization, 32(2), 281–306.
Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888–905.
Sosa, M., & Marle, F. (2013). Assembling Creative Teams in NPD Using Creative Team Familiarity. Journal of Mechanical Design, 135.
Tan, M. P. M., Broach, J. R. J., & Floudas, C. a. C. (2007). A novel clustering approach and prediction of optimal number of clusters: global optimum search with enhanced positioning. Journal of Global Optimization, 39(3), 323–346. doi:10.1007/s10898-007-9140-6
Thebeau, R. E. (2001). Knowledge Management of System Interfaces and Interactions for Product Development Process. Massachussets Institute of Technology.
Whitfield, R., Smith, J., & Duffy, A. (2002). Identifying Component Modules. In Proceedings of the 7th international conference on artificial intelligence in design AID’02 (pp. 571–592).
Yu, T. L., Yassine, A., & Goldberg, D. (2007). An Information Theoretic Method for Developing Modular Architectures using Genetic Algorithms. Research in Engineering Design, 18, 91–109.
Zotteri, G., Kalchschmidt, M., & Caniato, F. (2005). The Impact of Aggregation Level on Forecasting Performance. International Journal of Production Economics, 93-94, 479–491.
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