Forecasting Product Life Cycle Phase Transition Points with Modular Neural Networks Based System

  • Authors:
  • Serge Parshutin;Ludmila Aleksejeva;Arkady Borisov

  • Affiliations:
  • Institute of Information Technology, Riga Technical University, Latvia, LV-1658;Institute of Information Technology, Riga Technical University, Latvia, LV-1658;Institute of Information Technology, Riga Technical University, Latvia, LV-1658

  • Venue:
  • ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
  • Year:
  • 2009

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Abstract

Management of the product life cycle and of the corresponding supply network largely depends on information in which specific phase of the life cycle one or another product currently is and when the phase will be changed. Finding a phase of the product life cycle can be interpreted as forecasting transition points between phases of life cycle of these products. This paper provides a formulation of the above mentioned task of forecasting the transition points and presents the structured data mining system for solving that task. The developed system is based on the analysis of historical demand for products and on information about transitions between phases in life cycles of those products. The experimental results with real data display information about the potential of the created system.