銷售預測是企業經營運作的重要工作之一，因為需求的變化與波動，使得採購作業、庫存管理、生產排程等企業內活動均受影響。透過精確的預測來掌握需求，以達到降低庫存成本、減少人力需求、提高顧客服務品質與競爭力的目的。
本研究以兩種不同的方法分別預測DRAM產業的銷售量，一種是以週期成長多代擴散模式去預測各世代的DRAM銷售量，由於DRAM產業深受價格以及景氣循環的影響，一般多代預測模式不一定有考慮景氣循環的變數，因此本研究以加入價格及GDP的變數，欲了解本模式在加入此兩變數後，是否對DRAM產業預測有貢獻，以往看到許多研究皆以 Norton與Bass (1987)中的多代模式去預測DRAM多代發展的變化，本文亦與之做比較。第二種是應用灰色預測去預測全球DRAM銷售量，灰色預測方法中以其容易處理非線性問題、少數據、小樣本的預測特性，針對DRAM產業近十年來每年之銷售量，建構一個合適的銷售量的預測模式，以期改善企業的管理效率，並做為管理者決策的參考，提高管理上的競爭力。
本研究以全球DRAM作為實證對象，從實例驗證結果發現，灰色預測對於DRAM
預測有很好的準確度，並且只使用少組數的歷史資料即可得到高準確度的預測值。而週期成長多代擴散模式在加入價格、GDP兩個變數後確實對模型有貢獻性。

Sales forecasting is one of the important events to every company. Changing in customers’ requirements and environment are related to procurement operations, inventory
management, production scheduling and other enterprise activities. Accurate demand forecasts can achieve lower inventory costs, reduce manpower requirements, improve
much customer service quality and business competitiveness.
In this study, there are two different methods to forecast sales of DRAM industry. The first is Growth-Cycle Decomposition Multi-Generational Diffusion Model that is to
forecast multi-generational DRAM sales. Because DRAM industry is easily affected by business cycle and price, General Multi-Generational Diffusion Model do not consider the business cycle variables. In this study, I use both price and GDP variables, wondering whether these variables are meaningful to the whole model after these two variables added. Norton and Bass (1987) model are extensively cited by many researchers. This study is
also compared with Norton and Bass (1987) model. The second method is the grey theory,used to forecast global DRAM sales. Grey Theory method can easily deal with nonlinear
problems, less data, small samples of forecasting. This study uses annual sales data of DRAM industry over the decade. Construct an appropriate sales forecasting model to
improve the efficiency of enterprise management and decision-making as a reference for managers to improve the competitiveness of management. In this study, use the global DRAM as an empirical subject. Results show that Grey
Theory for DRAM has good accuracy, and using only a small number of historical data set can predict well. Moreover, Growth-Cycle Decomposition Multi-Generational Diffusion
Model joins price and GDP variables together which also does contribute to the whole model.
Key

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