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Abstract
This paper presents a neural expert system approach to designing an intelligent strategic planning system. The main recipe of the proposed neural expert system is an inference mechanism capable of performing forward inference. Four strategic planning portfolio models are considered such as BCG matrix, Growth/Gain matrix, GE matrix, and Product/Market Evolution Portfolio matrix. The proposed neural expert system could provide 'what-if' functions, which prove to be very useful for unstructured decision making problems such as strategic planning problems. What-if functions are accomplished through the forward inference mechanism, enabling the neural expert system to provide appropriate outputs with respect to the given inputs. This study developed a prototype system, named Strat Planner, running on Windows 2000. Using the Korean automobile data, we performed experiments under experimentally designed competitive situations. Results support our supposition that the neural expert systems approach is useful for performing competitive analyses. Further research topics associated with the current research are discussed.
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Abstract
This paper presents a neural expert system approach to designing an intelligent strategic planning system. The main recipe of the proposed neural expert system is an inference mechanism capable of performing forward inference. Four strategic planning portfolio models are considered such as BCG matrix, Growth/Gain matrix, GE matrix, and Product/Market Evolution Portfolio matrix. The proposed neural expert system could provide 'what-if' functions, which prove to be very useful for unstructured decision making problems such as strategic planning problems. What-if functions are accomplished through the forward inference mechanism, enabling the neural expert system to provide appropriate outputs with respect to the given inputs. This study developed a prototype system, named Strat Planner, running on Windows 2000. Using the Korean automobile data, we performed experiments under experimentally designed competitive situations. Results support our supposition that the neural expert systems approach is useful for performing competitive analyses. Further research topics associated with the current research are discussed.