本研究提出一個建置企業整合型資料庫的設計架構，它藉由資料選擇性複製策略的方式，將分散於各地方的資料庫萃取並彙整至企業營運總部，建立一個可以更有效提供企業管理及決策所需資訊的整合型企業資料庫。本研究並以我國衛生署疾病管制局預防接種管理系統(National Immunization Information System, NIIS)之實驗性中央資料庫設計及建立為研究之實作案例，將分散於各縣市衛生局、衛生所之預防接種相關資料庫進行彙集並整合建立一個可有效支援建立全國預防接種管理及決策系統所需資料來源之中央資料庫。為實證中央資料庫建置可更有效支援NIIS決策支援之真實價值，本研究乃進一步嘗試應用本研究所建立的中央資料庫作為資料來源，建立可支援我國年度疫苗接種採購預測模式，提供疾病管制局作為年度各類型疫苗最適採購量之依據，用以取代原有以人工經驗計算之方式。在此一決策應用的研究中，第一階段推估分別應用灰色預測模式及倒傳遞類神經網路預測模式，根據歷年地方衛生所疫苗施打人數、疫苗施打完成率及新年度疫苗施打的目標人數，建構新年度疫苗施打人數預測模式，實驗結果顯示，倒傳遞應用類神經網路比灰色理論可以獲得較佳的預測值。第二階段應用第一階段所得之新年度疫苗施打人數預測量，進一步合併考量疫苗耗損率、保留庫存及上年度之疫苗庫存量等因素進行全國性新年度預防接種疫苗最適採購量之計算。實驗性中央資料庫系統建置完成後，由實證結果得知，應用中央資料庫為資料來源所建立之年度疫苗採購量預測模式相較於傳統人工計算模式能夠得到更精準的下年度疫苗採購量。它不僅可對疫苗採購之最佳化規劃提供最完整的資料來源，更可提供快速支援全國性預防接種管理性統計分析報表及資訊產生之功能。

In this research we present an architectural design model of building an enterprise integrated database by using the approach of partial replication of data to extract and aggregate the databases dispersed at different locations to effectively provide the required information for business management and decision making. We take the design and creation of an experimental central database for National Immunization Information System (NIIS), Center for Disease Control (CDC) of Department of Health (DOH), as a case of practical study to investigate how an integrated central database can be built by making use of the distributed database located at each county's and city's health bureau so that the capabilities of building decision-making applications and the administration for the operations of national immunization can be supported. In order to verify the true value of being able to more effectively support NIIS decision support through the implementation of central database, in this research we also attempt to create a prediction model for yearly national vaccine procurement as a basis for CDC to purchase the best-fit amount of vary types of vaccine so that the current approach of using human experience to compute the yearly vaccine purchasing amount can be substituted. In this study of building the application of decision making, in the first stage we use Grey Prediction Theory and Back-Propagation Neural Network separately to build the prediction model to predict the number of immunization population for the next year based on the major factors of the number of immunization, population and the completion rate, and the objective immunization population of next year of each city's bureau of health. In the second stage, we take the prediction result of the next-year from the first stage and further consider the yearly vaccine waste rage, the amount of reservation stock, and last year stock amount to compute the amount of yearly national vaccine procurement. After completing the experimental system building of central database system, the experimental result shows that taking the data resources from the central database to build the prediction model for the yearly national vaccine procurement which can be more accurately to compute the next year's vaccine procurement, and it also can efficiently generate the information and report for the management statistical analysis.