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.
1. Introduction
Development project of high-tech products is always influenced by several risks neglecting each of which will dramatically undermine the success rate of such a project. Likewise, because of the fact that investment on development projects of high-tech products require the utilization of different resources (i.e. both physical assets & intellectual capitals) and will not always result in desired predictions, failure of such projects will doubtlessly inflict massive economic costs on organizations. Therefore, if project planners are enabled to measure and analyze the risk of such projects, they can forecast their success or failure more confidently.
5.3. Conclusion
Investing on high-tech products doesn't always yield the predicted results and organizations will suffer massive losses if their efforts in developing high-tech projects fail. To manage high-tech product development projects more confidently, managers need to have reliable information about their risk values in advance. The ANN proposed in this paper is aimed at helping managers to have such a precious information. Based on a Risk Assessment Index System (RAIS) that has been extracted from valid resources and constructed by Principal Component Analysis (PCA) method, an Artificial Neural Network (ANN) has been designed for enabling project managers to recognize the success or failure of each high-tech project before starting investing on it. The heighted level of model's accuracy and reliability makes it a very reliable mechanism for recognizing the success or failure of hightech projects.
