A Causal Dependencies Identification and Modelling Approach for Redesign Process

  • Thierno M. L. DialloEmail author
  • Marc Zolghadri
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 540)


Systems and products are changed throughout their lifecycle to adapt to users’ needs changes or to technological advances, among other reasons. The redesign process consists in modifying one or several parameters to reach the awaited redesign targets (better performance for instance). However, due to dependencies among parameters, changing one parameter may have unintended impacts on others. The problem we study in the redesign process concerns its underlying process of change propagation through the so called dependency model. The dependencies among parameters are correlation or causal. As a first contribution, the paper argues that the most interesting links to identify, model and work on are causalities. Therefore, the challenge to overcome is to identify the existing causal links among parameters using data exploration or expert knowledge mappings. The second contribution discusses a Causal dependencies identification and modelling approach for Redesign process, CaRe in short, which uses the Bayesian Network theory. CaRe is made to generate a causal Bayesian Network that allows evidential and causal inferences, supporting redesign decision-makings. The steps of CaRe are discussed in detail and future research works are presented at the end of the paper.


Redesign Dependency model Change propagation Causality Bayesian Networks 


  1. 1.
    Zolghadri, M., et al.: Complex systems renewal: positioning, concepts and architectural issues. IFAC Proc. 47(3), 8731–8736 (2014). Scholar
  2. 2.
    Clarkson, P.J., Simons, C., Eckert, C.: Predicting change propagation in complex design. J. Mech. Des. (Trans. ASME) 126(5), 788–797 (2004). Scholar
  3. 3.
    Cohen, T., Navathe, S.B., Fulton, R.E.: C-FAR, change favorable representation. Comput. Aided Des. 32(5), 321–338 (2000). Scholar
  4. 4.
    Koh, E.C., Caldwell, N.H., Clarkson, P.J.: A method to assess the effects of engineering change propagation. Res. Eng. Des. 23(4), 329–351 (2012). Scholar
  5. 5.
    Giffin, M., et al.: Change propagation analysis in complex technical systems. J. Mech. Des. 131(8), 081001 (2009). Scholar
  6. 6.
    Ulrich, K.: The role of product architecture in the manufacturing firm. Res. Policy 24(3), 419–440 (1995). Scholar
  7. 7.
    Huang, G.Q., Mak, K.L.: Current practices of engineering change management in UK manufacturing industries. Int. J. Oper. Prod. Manag. 19(1), 21–37 (1999). Scholar
  8. 8.
    Mooz, H., Forsberg, K., Cotterman, H.: Communicating Project Management: The Integrated Vocabulary of Project Management and Systems Engineering. John Wiley & Sons, Hoboken (2003)Google Scholar
  9. 9.
    Aldrich, J.: Correlations genuine and spurious in Pearson and Yule. Stat. Sci. 10, 364–376 (1995)CrossRefGoogle Scholar
  10. 10.
    Holland, P.W.: Statistics and causal inference. J. Am. Stat. Assoc. 81(396), 945–960 (1986). Scholar
  11. 11.
    Pearl, J.: Causality. Cambridge University Press, Cambridge (2009)CrossRefGoogle Scholar
  12. 12.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Franciso (1988). Scholar
  13. 13.
    Spirtes, P., Glymour, C.N., Scheines, R.: Causation, Prediction, and Search. MIT Press, Cambridge (2000). Scholar
  14. 14.
  15. 15.
    Conrady, S., Jouffe, L.: Bayesian Networks and BayesiaLab: A Practical Introduction for Researchers. Bayesia, USA (2015).
  16. 16.
    Tsamardinos, I., Brown, L., Aliferis, C.: The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 65(1), 31–78 (2006). Scholar
  17. 17.
    Pearl, J.: Bayesian networks. Department of Statistics, UCLA (2011).
  18. 18.
    Engel, A., Reich, Y.: Advancing architecture options theory: six industrial case studies. Syst. Eng. 18(4), 396–414 (2015). Scholar
  19. 19.
    Lee, J., Hong, Y.S.: Bayesian network approach to change propagation analysis. Res. Eng. Des. 28(4), 437–455 (2017). Scholar
  20. 20.
    Scutari, M., Denis, J.B.: Bayesian Networks: With Examples in R. Taylor & Francis, Boca Raton (2014). Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.Quartz LaboratorySupmeca-Superior Engineering Institute of ParisSaint-OuenFrance

Personalised recommendations