Forecasting product life cycle phase transition points with modular neural networks based system

Serge Parshutin; Ludmila Aleksejeva; Arkady Borisov

doi:10.1007/978-3-642-03067-3_9

Forecasting product life cycle phase transition points with modular neural networks based system

Serge Parshutin^*
, Ludmila Aleksejeva
, Arkady Borisov

^*Corresponding author for this work

Riga Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Research › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Advances in Data Mining
Subtitle of host publication	Applications and Theoretical Aspects - 9th Industrial Conference, ICDM 2009, Proceedings
Pages	88-102
Number of pages	15
DOIs	https://doi.org/10.1007/978-3-642-03067-3_9
Publication status	Published - 2009
Externally published	Yes
Event	9th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, ICDM 2009 - Leipzig, Germany Duration: 20 Jul 2009 → 22 Jul 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5633 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, ICDM 2009
Country/Territory	Germany
City	Leipzig
Period	20/07/09 → 22/07/09

Keywords

Forecasting Transition Points
Modular Neural Networks
Product Life Cycle
Self-Organizing Maps

Access to Document

10.1007/978-3-642-03067-3_9

Cite this

Parshutin, S., Aleksejeva, L., & Borisov, A. (2009). Forecasting product life cycle phase transition points with modular neural networks based system. In Advances in Data Mining: Applications and Theoretical Aspects - 9th Industrial Conference, ICDM 2009, Proceedings (pp. 88-102). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5633 LNAI). https://doi.org/10.1007/978-3-642-03067-3_9

Parshutin, Serge ; Aleksejeva, Ludmila ; Borisov, Arkady. / Forecasting product life cycle phase transition points with modular neural networks based system. Advances in Data Mining: Applications and Theoretical Aspects - 9th Industrial Conference, ICDM 2009, Proceedings. 2009. pp. 88-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

keywords = "Forecasting Transition Points, Modular Neural Networks, Product Life Cycle, Self-Organizing Maps",

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Parshutin, S, Aleksejeva, L & Borisov, A 2009, Forecasting product life cycle phase transition points with modular neural networks based system. in Advances in Data Mining: Applications and Theoretical Aspects - 9th Industrial Conference, ICDM 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5633 LNAI, pp. 88-102, 9th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, ICDM 2009, Leipzig, Germany, 20/07/09. https://doi.org/10.1007/978-3-642-03067-3_9

Forecasting product life cycle phase transition points with modular neural networks based system. / Parshutin, Serge; Aleksejeva, Ludmila; Borisov, Arkady.
Advances in Data Mining: Applications and Theoretical Aspects - 9th Industrial Conference, ICDM 2009, Proceedings. 2009. p. 88-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5633 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Research › peer-review

TY - GEN

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AU - Parshutin, Serge

AU - Aleksejeva, Ludmila

AU - Borisov, Arkady

PY - 2009

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N2 - 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.

AB - 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.

KW - Forecasting Transition Points

KW - Modular Neural Networks

KW - Product Life Cycle

KW - Self-Organizing Maps

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SN - 9783642030666

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Parshutin S, Aleksejeva L, Borisov A. Forecasting product life cycle phase transition points with modular neural networks based system. In Advances in Data Mining: Applications and Theoretical Aspects - 9th Industrial Conference, ICDM 2009, Proceedings. 2009. p. 88-102. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-03067-3_9

Forecasting product life cycle phase transition points with modular neural networks based system

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Keywords

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