Data Mining from Simulation of Six Sigma in Manufacturing Company

With the quickly development of the information system and the applications of six sigma in manufacturing company, six sigma faces the problem how to analyze the vast of data effectively. Data mining from simulation outputs is performed in this paper; it focuses on techniques for extracting knowledge from simulation outputs for a production and optimizing devices and labors with certain target. This paper first gives a brief definition of six sigma. Then we set up one simulation model for the production process and construct optimization objective. Then we set up one data mining model based on WITNESS Miner. It then explains six sigma project modeling with WITNESS incorporating the calculation of sigma ratings for processes and the export of key statistics. The WITNESS optimizer six sigma algorithm is explained and an example project illustrating the use of WITNESS Miner (data mining) is included. The mining results show that the model is able to fund important information affecting target, make manager diagnose the bottlenecks of the beer production process, and help manager to make decisions rapidly under uncertainty.