A Neural Network Model for Determining the Success or Failure of High-tech Projects Development: A Case of Pharmaceutical industry
This addresses the risk assessment barrier for high-tech project financing in the pharmaceutical industry, but appears incremental as it applies an existing ANN method to a new domain.
The study tackled the problem of evaluating high-tech projects by developing an Artificial Neural Network (ANN) to predict success or failure, specifically applied to the pharmaceutical industry, but no concrete performance numbers were provided in the abstract.
Financing high-tech projects always entails a great deal of risk. The lack of a systematic method to pinpoint the risk of such projects has been recognized as one of the most salient barriers for evaluating them. So, in order to develop a mechanism for evaluating high-tech projects, an Artificial Neural Network (ANN) has been developed through this study. The structure of this paper encompasses four parts. The first part deals with introducing paper's whole body. The second part gives a literature review. The collection process of risk related variables and the process of developing a Risk Assessment Index system (RAIS) through Principal Component Analysis (PCA) are those issues that are discussed in the third part. The fourth part particularly deals with pharmaceutical industry. Finally, the fifth part has focused on developing an ANN for pattern recognition of failure or success of high-tech projects. Analysis of model's results and a final conclusion are also presented in this part.