A Method to Quantify the Power Distribution in Collaborative Non-hierarchical Networks

  • Beatriz Andrés
  • Raúl Poler
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 408)


Collaborative networks are characterised by the establishment of relations in more or less hierarchical power structures. The hierarchy of the network is defined by the partners’ power degree. Hierarchical structures and associated barriers limit the decision making and discourage collaboration within partners. This paper focuses on proposing a method to allow researchers to identify the power degree of each network partner, through Markov Chains. Knowing the power distribution, helps researchers to diagnose the power balance, reconsider the status in the network and have a better view of power interaction and collaboration. Therefore, the power distribution analysis is a key issue to understand the partners’ behaviour and achieve sustainable networks.


networks power distribution Markov Chain sustainable network 


  1. 1.
    Poler, R.: Intelligent Non-Hierarchical Manufacturing Networks (iNet-IMS). Intelligent Manufacturing Systems (2010),
  2. 2.
    Schneeweiss, C.: Distributed decision making - a unified approach. European Journal of Operational Research 150, 23–252 (2003)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Andrés, B., Poler, R.: Relevant Problems in Collaborative Processes of Non-Hierarchical Manufacturing Networks. In: Prado, J.C., García, J., Comesaña, J.A., Fernández, A.J. (eds.) 6th International Conference on Industrial Engineering and Industrial Management, Vigo, pp. 90–97 (2012)Google Scholar
  4. 4.
    Liu, Y., Zolghadri, M.: Power Interaction in Non-hierarchical Supply Chain Network. In: Proceedings of the 2011 17th International Conference on Concurrent Enterprising, pp. 1–8 (2011)Google Scholar
  5. 5.
    CONVERGE: Work Package Name: System Requirements & Reference Model Definition. Collaborative Communication Driven Decision Management in Nonhierarchical Supply Chains of the Electronic Industry. D2.2 Decision-Making Model, Data Mapping and Integration Roadmap (2010)Google Scholar
  6. 6.
    Emerson, R.M.: Power-dependence relations. American Sociological Review 27, 31–41 (1962)CrossRefGoogle Scholar
  7. 7.
    Cho, D.S., Chu, W.: Determinants of Bargaing power in OEM Negotiations. Industrial Marketing Management 23, 343–355 (1994)CrossRefGoogle Scholar
  8. 8.
    Pfeifer, P.E., Carraway, R.L.: Modeling customer relationships as Markov Chains. Journal of Interactive Marketing 14(2), 43–55 (2000)CrossRefGoogle Scholar
  9. 9.
    Netzer, O., Lattin, J.M., Srinivasan, V.: A Hidden Markov Model of Customer Relationship Dynamics. Marketing Science 27(2), 185–204 (2008)CrossRefGoogle Scholar
  10. 10.
    Madhusudanan, P.V., Chandrasekharan, M.P.: An absorbing Markov chain model for production systems with rework and scrapping. Computers & Industrial Engineering 55, 695–706 (2008)CrossRefGoogle Scholar
  11. 11.
    Xia, J.C., Zeephongsekul, P., Arrowsmith, C.: Modelling spatio-temporal movement of tourists using finite Markov chains. Mathematics and Computers in Simulation 79, 154–1553 (2009)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Llorca, J., Delgado-Rodríguez, M.: Competing risks analysis using Markov chains: impact of cerebrovascular and ischaemic heart disease in cáncer mortality. International Journal of Epidemiology 30, 99–101 (2001)CrossRefGoogle Scholar
  13. 13.
    Crossman, R.J., Škulj, D.: Imprecise Markov chains with absorption. International Journal of Approximate Reasoning 51, 1085–1099 (2010)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Beatriz Andrés
    • 1
  • Raúl Poler
    • 1
  1. 1.Research Centre on Production Management and Engineering (CIGIP)Universitat Politècnica de València (UPV)AlcoySpain

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