Multiple regression models for electronic product success prediction

Author

Lo, Frank Cheong-Wah ; Foo, Say-Wei ; Bauly, John A.

Author_Institution

Singapore Polytech., Singapore

Volume

1

fYear

2000

fDate

2000

Firstpage

419

Abstract

As the cost of failure in new product development is very high, product developers are looking for good product success/failure prediction models. The general direction of search is towards knowledge based systems (KBS) that incorporate the wisdom of experienced developers and extracts from data of past projects. In this paper, results of investigation using multiple regression models are reported. It is found that 90% accuracy may be achieved in success/failure prediction of electronic product development using a multiple regression model based on six critical factors

Keywords

electronic engineering computing; failure analysis; knowledge based systems; product development; statistical analysis; electronic product development; electronic product success prediction; knowledge based systems; multiple regression models; new product development failure cost; product success/failure prediction models; success/failure prediction; Application software; Costs; Data mining; Electronic equipment testing; Fuzzy logic; Input variables; Knowledge based systems; Neural networks; Predictive models; Product development;

fLanguage

English

Publisher

ieee

Conference_Titel

Management of Innovation and Technology, 2000. ICMIT 2000. Proceedings of the 2000 IEEE International Conference on

Print_ISBN

0-7803-6652-2

Type

conf

DOI

10.1109/ICMIT.2000.917374

Filename

917374