在現今以「需求為導向」且變化快速的產業，若能藉由預測，掌握市場或顧客的需求，促進產銷協調，將可大大增加產業競爭力；然而，隨著技術進步，產品趨向少量多樣化，若針對每一品項進行預測，不僅所需的時間費用和人力龐大，而且增加預測複雜度。因此為有效管理大量的預測作業，「層級式預測」方法論因應產生；過去部份學者認為由上而下（top-down）預測方式較佳，因總合的預測較準，細項的變異可互相抵消掉，然而亦有部份學者認為若將個別項目加總，會損失部分資訊、產生偏誤，因而較支持由下而上（bottom-up）預測方式。總之，何種預測方式較佳，至今尚未出現一致性的結論。
　　本研究以液晶監視器為實證研究對象，液晶顯示器產業為目前政府推動兩兆雙星計劃之重點發展產業之一，亦為一高脈動速度的產業，透過層級式預測方法論建立各層級市場之需求預測，以期找出最適預測階層及預測方式。本研究提出之層級式預測方法論共包含五個階段：首先利用集群分析考量多個產品屬性，建立產品階層結構；在預測方法上，結合迴歸分析、轉移函數以及同時考量價量因果關係之聯立方程式建立各階層預測模型，並且找出影響各階層需求量之外部驅動因素；接著採用多種預測方式及比例分配法展開至各階層，獲得上、下階層一致的預測結果；最後比較不同預測方式之各階層預測誤差，以找出最適預測階層及預測方式。
　　不同於過去學者探討由上而下或由下而上為最佳預測方式，本研究結果發現：在所建立之產品階層結構及預測模型下，以中間層為最適預測階層，且採取middle-out預測方式可獲得整體最佳的預測結果，不但突破了過去層級式預測相關研究之看法，同時可作為液晶監視器廠商預測未來市場需求發展趨勢之參考。

　　For demand-oriented and fast changing industries nowadays, if enterprises are capable to forecast market demand or customers’ needs, then it can improve the coordination of supply and demand and increase the industry competition. However, with technology progress, products are trending to various types with small amounts. If enterprises forecast every individual product item, it will increase the complexity of forecasting activity. To manage a great deal of forecasting tasks efficiently, the hierarchical forecasting methodology comes into being. In the past, some studies indicated that top-down was a better forecasting approach since the variances of individual items could be canceled out by aggregating. On the contrary, other studies considered that aggregating individual items would lose some of valuable information and result in biases instead, so they preferred the bottom-up forecasting approach. As for which approach is better, there has not been consistent agreement so far.
　　TFT-LCD (thin film transistor-liquid crystal display) is one of the key industries with high clockspeed at present in Taiwan. In this study, LCD monitor is taken as empirical research subject. Through hierarchical forecasting methodology, we carry out demand forecasts at each product level, and then look for the best level and forecasting approach expectantly. The hierarchical forecasting methodology proposed in this paper consists of five stages. First, a few product attributes are considered to build product hierarchy by using cluster analysis. Second, exogenous driven factors that affect demand quantity significantly are identified at each level of product hierarchy. Third, a number of forecasting techniques are combined, including regression analysis, transfer function, and simultaneous equations model that considers causal relationship between quantity and price to forecast future demand at each level of product hierarchy. Forth, various forecasting approaches and disaggregation proportion methods are adopted to obtain consistent demand forecasts at each level of product hierarchy. Fifth, forecast errors with different forecasting approaches are assessed respectively to find out the best forecasting level and approach.
　　Different from previous studies regarding top-down or bottom-up as the best forecasting approach, the research results in this paper show that based on the built product hierarchy and forecasting models, the middle level of product hierarchy is the best level to be forecasted, and the best forecasting results of all levels can be obtained by using middle-out forecasting approach. These results not only break through the standpoints of related hierarchical forecasting studies in the past, but also could be the guide for LCD manufacturers and brands to forecast market demand in the future.

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