Clustering Human Decision-Making in Production and Logistic Systems

  • Christos TsagkalidisEmail author
  • Rémy Glardon
  • Maryam Darvish
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 459)


Human decisions play an essential role in Operations and Supply Chain Management. However, these decisions are rarely integrated in simulation models of Production and Logistic Systems. One main reason for this fact is the strong dispersion of human decisions among a population, as well as the variability of a single individual’s decision over time. This work presents an experimental study of a human decision consisting in the dynamic selection of suppliers in a well-controlled laboratory environment. The analysis of the results obtained on a large population shows that individual decision behaviors can be grouped into representative clusters typifying different decision behaviors. The results obtained from this study opens up the prospect to significantly reduce the number of decision models required to simulate Production and Logistic Systems including human decisions and could also allow categorizing human decision behavior based on a set of known criteria.


Decision-making Behavioral operations management Cluster analysis 


  1. Croson, R., Donohue, K.: Impact of POS data sharing on supply chain management: an experimental study. Prod. Oper. Manage. 12, 1–11 (2003)CrossRefGoogle Scholar
  2. Croson, R., Donohue, K.: Behavioral causes of the bullwhip effect and the observed value of inventory information. Manage. Sci. 52, 323–336 (2006)zbMATHCrossRefGoogle Scholar
  3. Guru, R.R.D., Kaboli, A., Glardon, R.: The effect of coercive power on supply chain inventory replenishment decisions. In: Grabot, B., Vallespir, B., Gomes, S., Bouras, A., Kiritsis, D. (eds.) Advances in Production Management Systems, Part II. IFIP AICT, vol. 439, pp. 230–237. Springer, Heidelberg (2014)Google Scholar
  4. Hair, J., Joseph, F., Anderson, E., Tatham, R., Ronald, L., Black, W.C.: Multivariate Data Analysis: With Readings, 4th edn. Prentice-Hall Inc, Upper Saddle River (1995)Google Scholar
  5. Gino, F., Pisano, G.: Toward a theory of behavioral operations. Manuf. Serv. Oper. Manage. 10, 676–691 (2008)Google Scholar
  6. Grimm, L.G., Yarnold, P.R.: Reading and Understanding More Multivariate Statistics. American Psychological Association, Washington (2000)Google Scholar
  7. Kaboli, A., Cheikhrouhou, N., Darvish, M., Glardon, R.: An experimental study of the relationship between trust and inventory replenishment in triadic supply chain. In: Proceedings of the POMS World Conference (2012)Google Scholar
  8. Katok, E.: Using laboratory experiments to build better operations management models. Found. Trends Technol. Inf. Oper. Manage. 5(1), 1–86 (2011)Google Scholar
  9. Murtagh, F., Legendre, P.: Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? J. Classif. 31(3), 274–295 (2014)MathSciNetCrossRefGoogle Scholar
  10. Oliva, R., Gonçalves, P.: Behavioral causes of demand amplification in sypply chains: “satisficing” policies with limited information cues. In: Proceedings of the 2005 System Dynamics Conference, pp. 118–119 (2005)Google Scholar
  11. Steckel, J.H., Gupta, S., Banerji, A.: Supply chain decision making: will shorter cycle times and shared point-of-sale information necessarily help? Manage. Sci. 50, 458–464 (2004)zbMATHCrossRefGoogle Scholar
  12. Sterman, J.D.: Modeling managerial behavior: misperceptions of feedback in a dynamic decision making experiment. Manage. Sci. 35, 321–339 (1989)CrossRefGoogle Scholar
  13. Tsagkalidis, C.: Study of human decision behavior for the operational selection of suppliers in a competitive framework using a participatory simulation platform. Master Thesis, EPFL (March 2014)Google Scholar
  14. Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Christos Tsagkalidis
    • 1
    Email author
  • Rémy Glardon
    • 1
  • Maryam Darvish
    • 2
  1. 1.Laboratory for Production Management and ProcessesSwiss Federal Institute of Technology at Lausanne (EPFL)LausanneSwitzerland
  2. 2.Faculté des Sciences de l’AdministrationUniversité LavalQuébecCanada

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