A Neural Expert System with Goal Seekingfunctions for Strategic Planning

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 backwards. Four strategic planning portfolio models are considered: BCG matrix, Growth/Gain matrix, GE matrix, and Product/Market Evolution Portfolio matrix. The proposed neural expert system could provide "goal-seeking" functions, which prove to be very useful for unstructured decision-making problems, specifically in strategic planning. Goal seeking functions are realized through the backward inference mechanism, enabling the neural expert system to show the appropriate inputs (or conditions) to guarantee the desired level of outputs. To implement our idea, we developed a prototype system, named StratPlanner, which runs on Windows 2000. Using Korean automobile industry data, we performed experiments under competitively designed 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 also discussed.