| Summary: | 碩士 === 國立高雄科技大學 === 工業工程與管理系 === 107 === In the face of competition among the peers and ensuring the competitive advantage, the improvement of the factory has become increasingly important. In addition to the focus on product quality and cost reduction, the concept of delivery on-time is increasingly important. In order to improve competitive advantage, these are important such as increasing the speed of delivery, improving the production system, reducing production time to be less than other competitors, and achieving the best order fill rate. Therefore, this study uses system simulation software to improve the current plants own planning to obtain the best production capacity, which is a excellent choice for the industry to strengthen their own essence, to make a rapid response to customers, and to enhance competitiveness.
This study takes the embossing wheel industry as a research case, analyzing the process of its production line. It provides the best factor level allocation suggestions in the research results. Firstly, the simulation construction of case production mode is carried out with Arena system simulation software, using six long time workstations are as experimental factors, also choosing the order fill rate and production time as the two reaction variables of the study. Secondly, using the factor configuration of the 2^(6-2) factor experimental design to simulate the production line, and collect the data. Thirdly, the output data is analyzed by the Minitab statistical analysis software to get the best configuration of the factor level. Finally, let the data verified by extending the number of simulations for various configurations. In order to assess the reliability of the simulation model is adequated, adding the sensitivity analysis is an important part. The mode’s stability is proved by lengthening the simulation time and increasing the number of simulations. Then, if you use this study’s optimal level configuration of the experimental factors , the output performance values of the reaction variables Y1 (order fill rate) and reaction variable Y2 (average production time) will also be the best output values.
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