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

2009
Sergejs Paršutins, Ludmila Aleksejeva, Arkādijs Borisovs

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
Modular Neural Networks, Self-Organizing Maps, Product Life Cycle, Forecasting Transition Points
DOI
10.1007/978-3-642-03067-3_9
Hyperlink
http://link.springer.com/chapter/10.1007/978-3-642-03067-3_9

Paršutins, S., Aleksejeva, L., Borisovs, A. Forecasting Product Life Cycle Phase Transition Points with Modular Neural Networks Based System. In: Advances in Data Mining. Applications and Theoretical Aspects: Lecture Notes in Computer Science. Vol.5633. Berlin: Springer Berlin Heidelberg, 2009. pp.88-102. ISBN 9783642030666.

Publication language
English (en)