Abstract
Six Sigma has been widely adopted in a variety of industries in the world and it has become one of the most important subjects of debate in quality management. Six Sigma is a well-structured methodology that can help a company achieve expected goal through continuous project improvement. Some challenges, however, have emerged with the execution of the Six Sigma. For examples, how are feasible projects generated? How are critical Six Sigma projects selected given the finite resources of the organization? On the other hand, semiconductor manufacturing industries in Taiwan have had significant growth over the last decade in the world and also make up for the bulk of economic benefits. Semiconductor foundries are multi-layer organizations with complex systems and complicated division of labor. Due to the organization structure is huge; when a semiconductor foundry executes the Six Sigma methodology is ineffective and difficult to achieve the organization objectives. A completely and system procedure for implementing the Six Sigma methodology into semiconductor manufacturing industries is necessary and beneficial. This study aims to develop the knowledge based Six Sigma methodology which is involved macro model and micro model. The macro model is focus on the strategy of Six Sigma which is including project generation, project evaluation, project selection and so on. The objective of macro model is to effectively identify the vital project. In addition, the micro model is aimed at the practical of Six Sigma. The micro model is to implement the DMAIC cycle to achieve the objective of each project. Furthermore, we conduct the computational intelligence approach to resolve the complex project problem. This proposed procedure of macro model was successfully help the case company divided project into Black belt project, green belt project and profitless project. In addition, the micro model which is conducting the computational intelligence approach is effectively decrease the waste of manufacturing process and reaches the optimization situation.