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題名：知識管理與分享之DMAIC方法論
作者：許加文
作者(外文)：Calvin Sheu
校院名稱：元智大學
系所名稱：管理研究所
指導教授：鄭雅穗
學位類別：博士
出版日期：2007
主題關鍵詞：財務知識管理系統；DMAIC；知識管理；知識分享；FKMS；DMAIC；Knowledge Management；Knowledge Sharing
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現今企業內部的商業智慧應用，涵蓋了企業報表、知識體、內部專屬之查詢分析、統計分析、資料探勘以及主動式報表的產製、遞送與警示。而商業智慧大部分複雜且高度的應用是著重在統計分析與資料探勘這二大部分，並且聚焦在數據資料的數學與統計處理上，像是相關分析、趨勢分析、假說檢定和預測分析等等。而本研究則是提出了一個DMAIC方法論，針對前項商業智慧的應用，具體建模完整的知識發掘流程，並著重在統計分析與資料探勘之中。本研究並實作了DMAIC方法論，完整的建構在研究團隊既有的財務知識管理系統平台之上，有效的改善了知識的產出與流程的品質，並進而使得金融產業經理人對於財務知識管理系統系統中近似無缺陷財務知識的產出，達到了高度的滿意程度。
具體而言，本研究架構係針對專屬知識提出了一個全新的觀點，也就是「每一個知識集都可以型塑完整的知識發掘流程，並記錄所有的具體經驗資訊」，亦針對知識發掘後的結果¬¬¬作了更深一層的解析，將知識集的生命週期定義成四個階段，從知識集的建立階段、使用階段、維護/更新階段，到最後的拋棄階段，進而確保知識能夠完整的交付到專業領域專家的手中，並能滿足全方位的需求。本研究更著重以知識工程觀點來發展每一個知識集，並彙集成一個有具體專業知識意涵的歷史參考決策資料庫，進而在整個知識發掘流程中構成了完整的知識管理與分享架構，而這也在本研究的設計以及實作過程中，彰顯了DMAIC方法論在此架構中的重要性。本研究並明確定義了一個完整知識集的組成，是由資料、模式、應用和報告所構成，並被一個知識管理系統所儲存、分享與傳播，進而提供有意義的決策制定與輔助。最後，本研究並運用資料探勘技術，來實作DMAIC方法論在公司債信評等與消費金融信用風險的具體流程應用。
Business intelligence (BI) applications within an enterprise range over enterprise reporting, cube and ad-hoc query analysis, statistical analysis, data mining, and proactive report delivery and alerting. The most sophisticated applications of BI are statistical analysis and data mining, which involve mathematical and statistical treatment of data for correlation analysis, trend analysis, hypothesis testing, and predictive analysis. They are used by relatively small groups of users consisting of information analysts and power users, for whom data and analysis are their primary jobs. We present an embedded DMAIC methodology to modeling knowledge discovery process for BI applications, specifically in statistical analysis and data mining. We implemented the DMAIC methodology embed into existed Financial Knowledge Management System (FKMS), which is able to be used as an effective means to dramatically improve knowledge product and process quality. It is meant for achieving total financial industry manager satisfaction by producing near defect-free financial knowledge.
In the framework of this research an extreme position on domain knowledge would be: “Any knowledge set that shapes the knowledge discovery process and experiences recorded.” We refined the full lifecycle of a knowledge set of knowledge discovery results consists of Build, Use, Maintenance/Update, and Desertion phases, to ensure that the knowledge that is delivered to production meets the domain expert’s needs. We have emphasized the knowledge engineering perspective for developing knowledge sets into historic reference decision to formed knowledge management and sharing architecture in knowledge discovery process, and the importance of leveraging structures and DMAIC methodology in design as well as implementation. The resulting knowledge from each experiment defined as a knowledge set consisting of strings of data, model, parameters, and reports are stored, shared, disseminated, and thus helpful to support decision making. We finally illustrate the above claims with a process of applying data mining techniques to support corporate bonds classification and credit risk in consumer banking.
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