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系統識別號 U0026-1208201413053000
論文名稱(中文) 商業智慧之研究-以國內某化工公司為例
論文名稱(英文) A Study of Business Intelligence – Using the Chemical Company as an Example
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系碩士在職專班
系所名稱(英) Department of Industrial and Information Management (on the job class)
學年度 102
學期 2
出版年 103
研究生(中文) 王瀅竣
研究生(英文) Vince Wang
電子信箱 luvbaby0129@gmail.com
學號 R37011011
學位類別 碩士
語文別 英文
論文頁數 53頁
口試委員 指導教授-李昇暾
口試委員-林清河
口試委員-耿伯文
中文關鍵字 風險管理  商業智慧  線上分析處理  資料倉儲 
英文關鍵字 Business intelligence  Cause of failure analysis  Risk management  OLAP 
學科別分類
中文摘要 在複雜的商業流程中風險管理是個關鍵且複雜的工作,尤其當流程是變化快速的。因此,在企業裡如何達到預防勝於治療是十分關鍵的。現今,有許多描述和分析流程的工具已經存在且有效應用在各領域,以解決流程動態變化的問題。
風險管理不僅為一種可定性的描述,如何有效利用以往工廠執行各項EHS (Environment Health and Safety)活動的成果,將大量的結構與非結構資料有效地管理與分析,再根據分析後的新見解做基礎,進而做出改變與改善,也就是利用既有的資料進行統計分析,進而提供營運決策參考。
目前,大部份的企業所存在的系統,無法滿足使用者對大量即時資料運算及多維度統計分析的需求,各式複雜與日常的報表仍高度依賴資訊人員人工開發,常常未能符合資訊即時性及相關統計資訊擷取的需求,且常受限於使用者的專業技能。在急速變換的環境中,企業內外部的資訊累積成等比發展。近年來,許多企業已經導入商業智慧系統來管理和萃取這些大量的資料。有效地利用商業智慧系統能支持主管做更快、更好的決策,。本研究使用商業智慧技術來分析大量的結構化與非結構化的報告,為使各層級決策者得到即時的意見回饋,且有效運用累積之營運資料,提供各項營運管理分析以及多項重大決策之預測建置統計模型,規劃建立「EHS績效管理系統」,透過全盤檢視並分析EHS活動之歷史資訊,以得知環境風險管理之優勢與劣勢,並進一步擬訂改善措施,在資源有效利用的狀況下,提升企業整體環境發展能量
因此,本研究使用失敗學、資料倉儲與商業智慧來克服這些問題。實驗結果顯示,使用這些方法調整原因歸屬方式後,能讓分析者更快速得到相關資訊,並且能根據原因拓墣來達到預防的效果。
英文摘要 It is not a simple task to depict cause and effect relations in complex business processes, especially when they are rapidly changing. However, this task is critical to achieve “prevention is better than cure” in modern business environments. Recently, a large number of tools used to describe and analysze the causes and effects in these business processes have been developed to facilitate this effort that also have been used effectively in various fields.
Risk management is not only a qualitative description of how to effectively use a factory to implement previous EHS (Environment Health and Safety) activity results, but it also allows a large number of structured and unstructured reports to be effectively managed and analyzed. Then, according to new insights, with the analysis as a foundation, changes and improvements can be made that make use of existing data for statistical analysis and provide operational decision-making capability.
Currently, most of the existing systems in enterprises are unable to meet user demand for a large number of real-time computing and multi-dimensional data statistical analysis, and a wide variety of complex and routine reports are still highly dependent on IT staff for development and maintenance that often fail to meet the requirement of real-time information and to provide relevant statistical information retrieval needs. They are often also limited by the level of expertise of the user.
In this study, we use business intelligence technology to analyze large amounts of both structured and unstructured reports in order for decision-makers at all levels to get real-time feedback and to make more effective use of the data accumulated in the operation, thus providing various analyses and operational management of a number of major decisions and building a predictive statistical model. We plan to establish an "EHS performance Management system" through a comprehensive view and analysis of historical information related to EHS activities in order to evaluate the strengths and weaknesses related to environmental risk management and to further develop improvements in efficient use of resources under conditions of overall environmental risk in the area of online energy management development, thus enhancing corporate performance.
Therefore, this study uses cause of failure analysis, data warehousing and business intelligence to overcome these problems. The experimental results show that using these methods after adjustment in the reason attribution mode, allow analysts to obtain information more quickly, and a preventive effect can be achieved depending on the cause topology.
論文目次 摘 要 I
Abstract II
誌 謝 IV
Table of Contents V
List of Tables VIII
List of Figures IX
Chapter 1 Introduction 1
1.1 Research background and motivations 1
1.2 Research objectives 2
1.3 The process of the research 5
Chapter 2 Literature review 6
2.1 The Campbell award 6
2.2 The failure theory 6
2.2.1 Failure definition and management 7
2.2.2 Cause of failure analysis 8
2.2.3 Structure of failure knowledge database 10
2.3 Business reporting and business intelligence 11
2.3.1 The business routine report 12
2.3.2 The report in BI system 12
2.3.3 The architecture of BI 13
2.3.4 Document analysis and classification 15
2.3.5 Data warehouse 16
2.3.6 On-line analytical processing 18
Chapter 3 Research method 21
3.1 Management and decision-making support 21
3.1.1 Solve business and operational needs 21
3.1.2 Relevant information introduction 23
3.1.3 Format instructions and presentation 24
3.1.4 KPI selection and setting 24
3.2 Cause of Failure Analysis 25
3.2.1 Documents Collection and Generation 25
3.2.2 Failure Understandings and Failure Reverse Inference 25
3.2.3 Failure Factor Fictionalized and Normalization 26
3.2.4 Failure knowledge databases Construction 27
3.3 Business intelligence system implement 27
3.3.1 Documents collection and generation 28
3.3.2 Data warehouse 28
3.3.3 On-line analysis processing 29
3.4 The process of design of BI system 29
3.4.1 Establish core competence issues 30
3.4.2 Selection of business processes 30
3.4.3 Determine the granularity. 30
3.4.4 Choose the dimension facets and schema construction 31
3.4.5 Establish key metrics 34
3.4.6 Data warehouse bus 35
Chapter 4 Experiment and analysis 35
4.1 The result of system process 36
4.1.1 Real-time and ad-hoc query 36
4.1.2 Visualizing chart and graph interface output 37
4.1.3 Business dashboard and KPI output 41
4.2 System limits description 43
4.3 System implement assessment 44
Chapter 5 Conclusion and future work 47
5.1 Conclusions and limitations 47
5.1.1 Conclusions 47
5.1.2 Limitations of the study 48
5.2 Management implications 48
5.2.1 Multi-dimensional analysis and systematic real-time reporting 48
5.2.2 The KPI presented 49
5.2.3 Business continuity management and plan 49
5.3 Future work 49
References 51
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