Neural Network Approach to R&D Project Termination
In order to help manager to make termination decision for the ongoing RD project timely and correctly, the authors review and compare the theory and methodology of termination decision from the literature. Furthermore, we discuss the considerable importance to identify attributes of an ongoing RD project. To identify attributes of an ongoing RD project, the authors examine characteristics and scopes of neural network. Thus, the feasibility of neural network, which is possibly used in RD project termination decisions, is further explored. On the basis of the theory and methodology of neural network, this study attempts to develop a mode identification method to make RD project termination decisions in an effective manner. Finally, the validity of the proposed method is verified by an example. The result shows that the method is both simple and practical.
Abstract : Experimentation Data Process: *Lockheed Martin experimentation at the Center for Innovation, -Constructive Simulations, -Human-in-the-Loop Simulation; *Two main issues; -Data Extraction/Storage, -Data Manipulation/Reduction; *Early Experimentation (2006 Processes), -Post Run extraction, -Manual reduction/consolidation; *Current Experimentation (2007 Processes), -Real-Time and Post Run extraction, -Hyperion Intelligence for Data reduction