高科技產業是高度資本密集與技術密集的產業，隨著電子產品的廣泛應用而快速進步，各家企業為了提高競爭力而專注在本身的核心專業，形成完整的供應鏈與各種經營模式。然而，無論是製造或是管理相關的問題都相當複雜，資訊會受到供應鏈中的長鞭效應（Bullwhip Effect）所影響，需求的不確定性和波動嚴重。各廠內不同的技術、產品組合、尺寸大小、人力技術，再加上顧客指定的製造流程，甚至明確要求產品須在哪個廠生產，這些種種導致實際產出小於建置產能的因素有可能使得公司無法有足夠的能力滿足顧客的需求。製程的複雜性讓規劃問題的難度也大幅提高，有鑑上述的原因，資訊系統也大量被導入產業來協助處理種種的問題。
針對高科技產業的重要營運資源，本研究發展一套營運最佳化支援系統建置流程，協助決策者在多變的環境下可以快速取得穩健的最佳化決策建議。首先利用PDCCCR架構與影響圖釐清相關決策元素的關係，接著依據影響圖與網路圖建立最佳化概念模型，再一一建立營運最佳化支援系統的個別模組，企業一方面可以利用營運最佳化支援系統達到營運問題的資源最佳化配置，讓公司維持獲利能力；另一方面利用營運最佳化支援系統加速處理營運問題，減少企業中各部門反覆的處理程序，更進一步達到決策流程再造。本研究並以兩個綠能產業的實證研究驗證效度，第一個案例為一家太陽能板垂直整合生產公司，案例中針對需求滿足問題建立營運最佳化支援系統，考量收益、利潤、毛利率這三個常見的財務指標進行最佳化決策；第二個是發光二極體封裝廠（Light Emitting Diode, LED）的採購與生產問題，考量生產過程中的變異所造成的產出分佈情況決定需要的晶片與製程組合所對應的投片數量，使得公司利潤達到最大化。

High-tech industry is highly capital intensive and technology-intensive industry, and each company focus on their core business to improve their competitiveness. Therefore, complete supply chain is composed of these company and new business models is builded. However, interal manufacturing problems and extendal management issues are complex. The demand uncertainty is serious since information in supply chain will be affect by the bullwhip effect. There are many different techniques, product portfolio, size, tools, process. The actual output is less than the built capacity since the constraints and relative ship in the fab. Therefore, the information systems are implemented to deal with these complex problem.
For the enterprise resource, this study provided a process for developing business optimization support system (BOSS) for high-tech industry. First, we clarified the relationship between the decision elements by PDCCCR framework and influence diagram. Then we build mathematical programming model for conceptual model. The decision makers can optimize the allocation of resoure and Improve the decision flow of company to push decision process reengineering. The first empirical study is a case of vertically integrated solar panel manufacturing company. We built a multi-objective business optimization support systems for demand fulifullment problem. The system considered different business models, specifications and customers to optimize three common financial indicators, renvene, profits, and margin rate. The second one is chip purchasing and production planning in LED industry. The model considered the deviation of the output distribution during the production to allocate the amount of the combination of chip and process to maximize profit.

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