Managing Product Life Cycle with MultiAgent Data Mining System

  • Serge Parshutin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6171)


Production planning is the main aspect for a manufacturer affecting an income of a company. Correct production planning policy, chosen for the right product at the right time, lessens production, storing and other related costs. The task of choosing a production policy in most cases is solved by an expert group, what not an every company can support. Thus a topic of having an intelligent system for supporting production management process becomes actual. The main tasks such system should be able to solve are defining the present Product Life Cycle (PLC) phase of a product as also determining a transition point - a moment of time (period), when the PLC phase is changed; as the results obtained will affect the decision of what production planning policy should be used.

The paper presents the MultiAgent Data Mining system, meant for supporting a production manager in his/her production planning decisions. The developed system is based on the analysis of historical demand for products and on the information about transitions between phases in life cycles of those products. The architecture of the developed system is presented as also an analysis of testing on the real-world data results is given.


MultiAgent system Agents Data Mining Product Life Cycle Management 


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  1. 1.
    Campbell, G.M.: Cyclic assembly schedules for dynamic demands. IIE Transactions 28(8), 643–651 (1996)Google Scholar
  2. 2.
    Campbell, G.M., Mabert, V.A.: Cyclical schedules for capacitated lot sizing with dynamic demands. Management Science 37(4), 409–427 (1991)CrossRefGoogle Scholar
  3. 3.
    Chakrabarti, S., Cox, E., Frank, E., et al.: Data Mining: Know It All. Morgan Kaufmann, San Francisco (2009)Google Scholar
  4. 4.
    Dunham, M.: Data Mining Introductory and Advanced Topics. Prentice-Hall, Englewood Cliffs (2003)Google Scholar
  5. 5.
    Gomez, J., Dasgupta, D., Nasraoui, O.: A new gravitational clustering algorithm. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp. 83–94. Society of Industrial and Applied Mathematics, Philadelphia (2003)Google Scholar
  6. 6.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)Google Scholar
  7. 7.
    Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining. The MIT Press, Cambridge (2001)Google Scholar
  8. 8.
    Kamath, N., Bhattacharya, S.: Lead time minimization of a multi-product, single-processor system: A comparison of cyclic policies. International Journal of Production Economics 106(1), 28–40 (2007)CrossRefGoogle Scholar
  9. 9.
    Kotler, P., Armstrong, G.: Principles of Marketing, 11th edn. Prentice-Hall, Englewood Cliffs (2006)Google Scholar
  10. 10.
    Merkuryev, Y., Merkuryeva, G., Desmet, B., Jacquet-Lagreze, E.: Integrating analytical and simulation techniques in multi-echelon cyclic planning. In: Proceedings of the First Asia International Conference on Modelling and Simulation, pp. 460–464. IEEE Computer Society, Los Alamitos (2007)CrossRefGoogle Scholar
  11. 11.
    Parshutin, S., Aleksejeva, L., Borisov, A.: Forecasting product life cycle phase transition points with modular neural networks based system. In: Perner, P. (ed.) Advances in Data Mining: Applications and Theoretical Aspects. LNCS (LNAI), vol. 5633, pp. 88–102. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Education, London (2006)Google Scholar
  13. 13.
    Wooldridge, M.: An Introduction to MultiAgent Systems. John Wiley & Sons, Chichester (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Serge Parshutin
    • 1
  1. 1.Institute of Information TechnologyRiga Technical UniversityRigaLatvia

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