none

• Main Authors: Ya-Hsien Lin, 林亞嫺
• Other Authors: Jen-Ming Chen
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/s6yg4q

碩士 === 國立中央大學 === 工業管理研究所在職專班 === 102 === With the development of the consumer electronics industry, semiconductor industry in recent decades plays a very important role in the field of science and technology now. DRAM (Dynamic Random Access Memory) and DRAM memory products are highly performing the outstanding segment in semiconductor industry, and DRAM industry supply chain relatively has more pivotal position. The causes of price fluctuation of DRAM products are complicated, such as new comer of DRAM manufacture or DRAM players leave, the new technology lead-in, the market cycle and the upgrade of consumer electronics products... etc. may affect the price fluctuation of DRAM products respectively. In this study, taking the DRAM industry for example, is intending to compile a comprehensive factor of DRAM spot prices and use data mining analysis to investigate the accuracy of DRAM spot prices, thereby providing a more accurate cost forecasts of procurement. If we can effectively predict DRAM spot prices, purchasers can increase profitability by reducing the risk of investment and declining the inventory loss, and then take the corresponding procurement strategy. It would further provide the basis for the high-end management strategy of the company. The results concluded that the minimum average square error (ASE) of prediction of DRAM spot prices is revealed from the neural network analysis, on behalf of its highest accuracy of prediction explain that data mining method could be used to predict the DRAM spot price movements, and it is considerable tool to predict the market price respectively.