Integrating the Theory of Inventive Problem Solving with Discrete Event Simulation in Supply Chain Management

  • Fatima Zahra BenMoussaEmail author
  • Sébastien Dubois
  • Roland De Guio
  • Ivana Rasovska
  • Rachid Benmoussa
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 541)


Supply chain challenges require not only effective management, but also a new innovative strategy to reduce costs and maximize its efficiency. Traditional problem-solving methodologies specific to the areas of supply chain management (SCM) find their limits when confronted with an inventive problem or a problem containing a contradiction. TRIZ (theory of inventive problem solving) is an effective theory for systematizing innovation and solving complex problems containing contradictions. Thus, the use of the theory TRIZ can be considered as a way to meet future challenges in SCM fields and get innovative solutions. This paper presents a method for solving supply chain problems and achieving a low-cost, based on complementarities between TRIZ and discrete event simulation and specific methods for solving supply chain problems. In the proposed model, a witness simulation model of the initial problem is developed to optimize the problem and find the system limits. Then, specific problem solving methods are applied to change the original description of problem and move towards a space in which a solution can be found. The discrete event simulation allows for experiments on the system to be created and analyzed. Thus, an experimental design was developed to establish the cause-and-effect relationships between the parameters of the system in order to formulate a generalized system of contradictions. And finally, ARIZ 85C, the most mature meta-methods of TRIZ, is used to address related contradictions for searching for an innovative solution, which must be subsequently implemented and evaluated in the discrete event simulation. The suitability of this new approach is finally proven through an industrial case study conducted in a company specialized in the manufacture of electronic devices for automobiles.


Theory of inventive problem solving (TRIZ) Algorithm for inventive problem solving (ARIZ) Supply chain Discrete event simulation Design of experiments Generalized system of contradictions 


  1. 1.
    van den Berg, J.P., Zijm, W.H.M.: Models for warehouse management: classification and examples. Int. J. Prod. Econ. 59, 519–528 (1999). Scholar
  2. 2.
    Dharmapriya, U.S.S., Kulatunga, A.K.: New strategy for warehouse optimization – lean warehousing. In: International Conference on Industrial Engineering and Operations Management, pp. 513–519 (2011)Google Scholar
  3. 3.
    de Koster, R., Le-Duc, T., Jan Roodbergen, K., Koster, D.: Design and control of warehouse order picking: a literature review. Eur. J. Oper. Res. 182, 481–501 (2007)CrossRefGoogle Scholar
  4. 4.
    Ben Moussa, F.Z., Rasovska, I., Dubois, S., De Guio, R., Benmoussa, R.: Reviewing the use of the theory of inventive problem solving (TRIZ) in green supply chain problems. J. Clean. Prod. 142, 2677–2692 (2017). Scholar
  5. 5.
    Ilevbare, I.M., Probert, D., Phaal, R.: A review of TRIZ, and its benefits and challenges in practice. Technovation 33, 30–37 (2013). Scholar
  6. 6.
    Fiorineschi, L., Frillici, F.S., Rissone, P.: A comparison of classical TRIZ and OTSM-TRIZ in dealing with complex problems. Procedia Eng. 131, 86–94 (2015). Scholar
  7. 7.
    Altshuller, G.S.: Algorithme of inventive problem solving (1985).
  8. 8.
    Russo, D., Montecchi, T., Ying, L.: Knowledge based approach for identifying TRIZ contradictions. In: Proceedings of 2012 Design Engineering Workshop (DEWS 2012), pp. 134–140 (2012)Google Scholar
  9. 9.
    Fresner, J., Jantschgi, J., Birkel, S., Bärnthaler, J., Krenn, C.: The theory of inventive problem solving (TRIZ) as option generation tool within cleaner production projects. J. Clean. Prod. 18, 128–136 (2010). Scholar
  10. 10.
    Khomenko, N., De Guio, R., Cavallucci, D.: Enhancing ECN’s abilities to address inventive strategies using OTSM-TRIZ. Int. J. Collab. Eng. 1, 98–113 (2009)CrossRefGoogle Scholar
  11. 11.
    Dubois, S., Rasovska, I., De Guio, R.: Interpretation of a general model for inventive problems, the generalized system of contradictions. In: Proceedings of the 19th CIRP Design Conference-Competitive Design (2009)Google Scholar
  12. 12.
    Dubois, S., De Guio, R., Rasovska, I.: Different ways to identify generalized system of contradictions, a strategic meaning. Procedia Eng. 9, 119–125 (2011). Scholar
  13. 13.
    Ramakrishnan, S., Tsai, P.-F., Srihari, K., Foltz, C.: Using Design of Experiments and Simulation Modeling to Study the Facility Layout for a Server Assembly Process (2008)Google Scholar
  14. 14.
    Wang, F., Lai, X., Shi, N.: A multi-objective optimization for green supply chain network design. Decis. Support Syst. 51, 262–269 (2011). Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Fatima Zahra BenMoussa
    • 1
    Email author
  • Sébastien Dubois
    • 2
  • Roland De Guio
    • 2
  • Ivana Rasovska
    • 3
  • Rachid Benmoussa
    • 1
  1. 1.SyLPRO/ENSA MarrakechUniversity of Cadi AyyadMarrakechMorocco
  2. 2.LGECOINSA StrasbourgStrasbourgFrance
  3. 3.ICUBEUniversity of StrasbourgStrasbourgFrance

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