Prediction of the life cycle cost using statistical and artificial neural network methods in conceptual product design

Kwang K. Seo, Ji Hyung Park, Dong Sik Jang, David Wallace

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

During the early design stages, over 70% of the total life cycle cost (LCC) of a product is committed and there may be competing concepts with dramatic differences. Additionally, both the lack of detailed information, and the overhead in developing parametric LCC models for a range of concepts make the application of traditional LCC models impractical. This paper describes the development of predictive models for the product LCC during conceptual design. An artificial neural network (ANN) model to predict the product LCC is developed and compared with a conventional statistical model - a regression model. The results show that the ANN model outperforms the traditional regression model used for predicting the product LCC.

Original languageEnglish
Pages (from-to)541-554
Number of pages14
JournalInternational Journal of Computer Integrated Manufacturing
Volume15
Issue number6
DOIs
Publication statusPublished - 2002 Nov 1
Externally publishedYes

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Conceptual design
Product design
Life cycle
Neural networks
Costs
Life cycle cost
Prediction
Artificial neural network
Product lifecycle

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

Prediction of the life cycle cost using statistical and artificial neural network methods in conceptual product design. / Seo, Kwang K.; Park, Ji Hyung; Jang, Dong Sik; Wallace, David.

In: International Journal of Computer Integrated Manufacturing, Vol. 15, No. 6, 01.11.2002, p. 541-554.

Research output: Contribution to journalArticle

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