Neural network-based survey analysis of risk management practices in new product development
Editor: Anja Maier, Stanko Škec, Harrison Kim, Michael Kokkolaras, Josef Oehmen, Georges Fadel, Filippo Salustri, Mike Van der Loos
Author: Kampianakis, Andreas; Oehmen, Josef
Institution: Technical University of Denmark, Denmark
Section: Design Processes, Design Organisation and Management
The current study investigates the applicability of Artificial Neural Networks (ANNs) to analyse survey data on the effectiveness of risk management practices in product development (PD) projects, and its ability to forecast project outcomes. Moreover, this study presents the relations between risk management factors affecting the success of a PD project, such as cost. ANNs were chosen due to the fact that hidden inherent relations can be revealed through this type of quantitative analysis. Flexibility in terms of analysis and adaptability on the given dataset are the great advantages of Artificial Neural Networks. Dataset used is a filtered survey of 291 product development programs. Answers of this survey are used as training input and target output, in pattern recognition two-layer feed forward networks, using various transfer functions. Using this method, relations among 6 project practices and 13 outcome metrics were revealed. Results of this analysis are compared with existent results made through statistical analysis in prior work of one of the authors. Future investigation is needed in order to tackle the lack of data and create an easy to use platform for industrial use.