Toward an Agile Adaptation of Supply Chain Planning: A Situational Use Case

  • Sanaa TissEmail author
  • Caroline Thierry
  • Jacques Lamothe
  • Christophe Rousse
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 568)


The project CAASC “Cloud Adaptation for an Agile Supply Chain” (French ANR project) aims to develop monitoring services in multi actors supply chain, by integrating uncertainties in supply chain planning and developing adaptation functions to environment changes.

In this paper we present a use case in the form of a serious game that aim to emerge and validate the required functionalities for the project. The game simulates a collaborative rolling horizon mid-term planning process. By analyzing its processes and results, we identify the central role of deviations analysis of plans to qualify uncertainties, assess robustness and propose response strategies.


Collaborative supply chain planning Uncertainties and robustness Serious game 



Authors want to acknowledge ANR for the funding of the CAASC project.


  1. 1.
    Jiang, Z., Lamothe, J., Bénaben, F.: Meta-modeling of collaborative supply chain. In: Mertins, K., Jardim-Gonçalves, R., Popplewell, K., Mendonça, J.P. (eds.) Enterprise Interoperability VII. PIC, vol. 8, pp. 307–320. Springer, Cham (2016). Scholar
  2. 2.
    van Donselaar, K., van den Nieuwenhof, J., Visschers, J.: The impact of material coordination concepts on planning stability in supply chains. Int. J. Prod. Econ. 68, 169–176 (2000)CrossRefGoogle Scholar
  3. 3.
    Kadipasaoglu, S.N., Sridharan, V.: Alternative approaches for reducing schedule instability in multistage manufacturing under demand uncertainty. J. Oper. Manag. 13, 193–211 (1995)CrossRefGoogle Scholar
  4. 4.
    Koca, E., Yaman, H., Aktürk, M.S.: Stochastic lot sizing problem with nervousness considerations. Comput. Oper. Res. 94, 23–37 (2018)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Guillaume, R., Thierry, C., Zieliński, P.: Robust material requirement planning with cumulative demand under uncertainty. Int. J. Prod. Res. 55, 6824–6845 (2017)CrossRefGoogle Scholar
  6. 6.
    Birge, J.R., Louveaux, F.: Introduction to Stochastic Programming. Springer, New York (2011). Scholar
  7. 7.
    Gholamian, N., Mahdavi, I., Tavakkoli-Moghaddam, R.: Multi-objective multi-product multi-site aggregate production planning in a supply chain under uncertainty: fuzzy multi-objective optimisation. Int. J. Comput. Integr. Manuf. 29, 149–165 (2016)Google Scholar
  8. 8.
    Dubois, D., Prade, H.: Representation and combination of uncertainty with belief functions and possibility measures. Comput. Intell. 4, 244–264 (1988)CrossRefGoogle Scholar
  9. 9.
    Fargier, H., Thierry, C.: The use of possibilistic decision theory in manufacturing planning and control: recent results in fuzzy master production scheduling, p. 15Google Scholar
  10. 10.
    Grabot, B., Geneste, L., Reynoso-Castillo, G., Vérot, S.: Integration of uncertain and imprecise orders in the MRP method. J. Intell. Manuf. 16, 215–234 (2005)CrossRefGoogle Scholar
  11. 11.
    Sun, G., Liu, Y., Lan, Y.: Fuzzy two-stage material procurement planning problem. J. Intell. Manuf. 22, 319–331 (2011)CrossRefGoogle Scholar
  12. 12.
    Genin, P., Thomas, A., Lamouri, S.: How to manage robust tactical planning with an APS (Advanced Planning Systems). J. Intell. Manuf. 18, 209–221 (2007)CrossRefGoogle Scholar
  13. 13.
    Hauge, J.B., Tundys, B., Rzeczycki, A., Lim, T.: Deploying serious games for supply chain management: lessons learned and good practices, p. 18 (2016)Google Scholar
  14. 14.
    Muratet, M., Torguet, P., Jessel, J.-P., Viallet, F.: Towards a serious game to help students learn computer programming. Int. J. Comput. Games Technol. 2009, 1–12 (2009)CrossRefGoogle Scholar
  15. 15.
    Kaminsky, P.: A new computerized beer game: a tool for teaching the value of integrated supply chain management, p. 19Google Scholar
  16. 16.
  17. 17.
    Nonaka, T., Miki, K., Odajima, R., Mizuyama, H.: Analysis of dynamic decision-making underpinning supply chain resilience: a serious game approach. IFAC-PapersOnLine. 49, 474–479 (2016)CrossRefGoogle Scholar
  18. 18.
    Hauser, F., Pomponne, V., Jiang, Z., Lamothe, J., Benaben, F.: Processes orchestration for preventing and managing shortages in a supply chain a dermo-cosmetics use case. In: 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1227–1234 (2017)Google Scholar
  19. 19.
    Chapman, C.: Introduction to Materials Management. Pearson India (2007)Google Scholar
  20. 20.

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Sanaa Tiss
    • 1
    Email author
  • Caroline Thierry
    • 2
  • Jacques Lamothe
    • 1
  • Christophe Rousse
    • 3
  1. 1.Universite de Toulouse, Centre Génie Industriel, IMT Mines AlbiAlbiFrance
  2. 2.Universite de Toulouse, IRIT, Université Toulouse Jean JaurèsToulouseFrance
  3. 3.Supply Chain Direction Pierre Fabre Dermo-CosmeticsLavaurFrance

Personalised recommendations