@inproceedings{23a7c9ba86c54b0e8d59eeaf393937c7,
title = "Forecasting product life cycle phase transition points with modular neural networks based system",
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.",
keywords = "Forecasting Transition Points, Modular Neural Networks, Product Life Cycle, Self-Organizing Maps",
author = "Serge Parshutin and Ludmila Aleksejeva and Arkady Borisov",
year = "2009",
doi = "10.1007/978-3-642-03067-3\_9",
language = "English",
isbn = "3642030661",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "88--102",
booktitle = "Advances in Data Mining",
note = "9th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, ICDM 2009 ; Conference date: 20-07-2009 Through 22-07-2009",
}