Title :
Multiple regression models for electronic product success prediction
Author :
Lo, Frank Cheong-Wah ; Foo, Say-Wei ; Bauly, John A.
Author_Institution :
Singapore Polytech., Singapore
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;
Conference_Titel :
Management of Innovation and Technology, 2000. ICMIT 2000. Proceedings of the 2000 IEEE International Conference on
Print_ISBN :
0-7803-6652-2
DOI :
10.1109/ICMIT.2000.917374
