Seo, KK Park, JH Jang, DS Wallace, D 2024-01-21T09:43:36Z 2024-01-21T09:43:36Z 2022-01-10 2002-11 0951-192X https://pubs.kist.re.kr/handle/201004/139083 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. English TAYLOR & FRANCIS LTD CONCURRENT Prediction of the life cycle cost using statistical and artificial neural network methods in conceptual product design Article 10.1080/09511920210143417 1 INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, v.15, no.6, pp.541 - 554 INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 15 6 541 554 scie scopus 000179045300006 2-s2.0-0036851687 Computer Science, Interdisciplinary Applications Engineering, Manufacturing Operations Research & Management Science Computer Science Engineering Operations Research & Management Science Article CONCURRENT life cycle costing