應用貝氏信賴網路方法為基之工作流程風險分析系統

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研究生：	蔡名峰 Rock Ming-Feng Tsai
論文名稱：	應用貝氏信賴網路方法為基之工作流程風險分析系統 BBN-Based Analytical Workflow Management System Using Risk Management Approach
指導教授：	張瑞芬 Amy J.C. Trappey
口試委員:
學位類別：	碩士 Master
系所名稱：	工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management
論文出版年：	2005
畢業學年度：	93
語文別：	英文
論文頁數：	68
中文關鍵詞：	工作流程管理、風險管理、計畫評核術、貝氏信賴網路、風險因子
外文關鍵詞：	Project Risk Management, Workflow Management System, PERT, Bayesian Belief Network, risk factors
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中文摘要
近年來，新產品的設計與開發需要越來越多的複雜設計與全球協同研發，為了達到有效率的產品研發設計，專案管理的工作流程管理(WfMS)扮演了一個相當重要的腳色，協助產品設計研發人員來管理、分析整個工作流程的狀況，迅速掌握工作進度與成本。而風險管理是屬於專案管理的另一部份，其針對工作流程分析、收集在各工作節點的風險因子，並應用相關風險管理理論來進行風險的分析、計算與預測，主要在處理及減少造成工作流程失敗的不確定因素，而達到降低成本、減少研發時間或增加利潤。
在本篇論文中，利用計畫評核術(Program Evaluation and Review Technique, PERT)的專案管理方法來對產品的工作時間與成本進行預測與計算，並且使用貝氏信賴網路理論(Bayesian Belief Network, BBN)來建構工作流程之風險因子的關係圖與風險分析，在論文中主要先收集與記錄工作流程節點的各類風險因子，然後以貝氏信賴網路的分析為基準，利用機率與網路圖來表示節點與風險因子的關聯，然後藉由期望值推算結果表示對於工作流程的影響，並調整計畫評核術中使用的資料數據，最後以常態機率圖表示預定完工時間達成機率與計算工作流程的工具與人力成本，提供設計者決策是否執行該設計的依據。如果執行該工作流程，在執行完成後會將風險因子的相關數據回饋到資料庫，調整風險因子的成本、時間與機率。而當決策者拒絕執行該流程設計，設計人員則必須使用MS Visio來重新設計與上傳新的流程圖，並重新進行風險評估直到達成專案的預設目標才可被執行。

關鍵字：工作流程管理、風險管理、計畫評核術、貝氏信賴網路、風險因子

Abstract
In recent years, new product development becomes more complex and needs more collaborative work than before. In order to support efficient product design, Workflow Management System (WfMS) plays an important role to help manager to control and analyze the status of workflow. Also, Project Risk Management (PRM) is used to collect and analyze the risk factors of tasks in the projects.
This thesis proposes Bayesian Belief Network (BBN) methodology for building risk factors relationship graph based on quantitative risk analysis. Program Evaluation and Review Technique (PERT) is used to estimate the probability of completion time and the sum of cost. Therefore, factors of the tasks in the workflow are collected, analyzed, and recorded in the database. Bayesian Belief Network use probabilities and network map to display the relationships between each node, and infer the influences of the workflow by calculating expected values of time and cost. PERT uses the result from BBN analysis to adjust processing time of tasks. PERT displays the probability of completed days by normal distribution charts and calculates the cost of manpower and tools. A project manager can use the report of risk analysis to make decision about execution or rejection of workflow designs. If a workflow is executed, executing members of each task needs to input data of factors and update the record to database. Else, a project manager must redesign the workflow and upload a new workflow to make analysis till to conform the requirements of projects.

Keywords: Project Risk Management (PRM), Workflow Management System (WfMS), PERT, Bayesian Belief Network (BBN), risk factors

Contents
中文摘要    I
Abstract    II
致謝辭    III
1. Introduction    1
1.1 Background and Motive    2
1.2 Thesis Objective    3
1.3 System Methodology and Structure    3
1.4 Thesis Structure    4
2. Literature Review    5
2.1 Workflow Management    5
2.1.1 Definition of Workflow Management    5
2.1.2 Research of Workflow Management    8
2.2 Risk Management    10
2.2.1 Definition    11
2.2.2 Risk Management Method    12
2.3 Bayesian Belief Network    17
2.3.1 Definition of Bayesian Theory    17
2.3.2 Definition of Bayesian Belief Network    18
3. System Methodology    20
3.1 Procedure of Risk Analysis    20
3.2 Collect Risk Factors    21
3.2.1 Collect Data    23
3.2.2 Analyze Factors    23
3.2.3 Save Data    24
3.3 Risk Analysis Model    24
3.3.1 BBN-based Analysis Model    24
3.3.2 PERT    27
3.4 Report    28
4. System Analysis and Design    30
4.1 Risk Analysis Model    30
4.2 System Environment    31
4.2.1 Analysis Tool    31
4.2.2 Development Tool    31
4.2.3 Hardware and Software    32
4.3 System Framework    33
4.3.1 User Interface    34
4.3.2 Application    35
4.3.3 Data    35
4.3.4 Function List    36
4.4 Function Analysis    36
4.4.1 Account Management    37
4.4.2 Project Management    38
4.4.3 Factor Maintenance    38
4.4.6 Workflow Execution    40
4.5 System Database Analysis    40
4.5.1 System Information    40
4.5.2 Project Data    41
4.5.3 Project Variable    41
5. System Implementation    43
5.1 Stages of Demonstration    43
5.2 Case Introduction    44
5.3 System Demonstration    54
6. Conclusion    61
6.1 Functional Comparison    61
6.2 Research Results    62
6.3 Future Works    64
References    65









Figure Contents
Figure 1. System methodology and structure.    4
Figure 2. Workflow system components and phases (Chen, Lin and Chen, 2003).    8
Figure 3. Architecture of Woflan.    9
Figure 4. Typical risk cycle (Doernemann, 2002).    13
Figure 9. The concept of Bayesian Belief Network.    18
Figure 10. Concept of approach.    20
Figure 11. Procedure of risk analysis.    21
Figure 12. Steps of collecting risk factors.    23
Figure 13. Simple example of BNN.    24
Figure 14. Independent relation diagram.    26
Figure 15. Factors condition probability.    26
Figure 16. Probability of days or time.    28
Figure 17. Risk analysis model    31
Figure 18. System framework.    34
Figure 19. System function module.    37
Figure 20. Account management.    38
Figure 21. Project management.    38
Figure 22. Risk factor maintain.    39
Figure 23. Risk analysis.    39
Figure 24. Workflow control.    40
Figure 25. ER-model.    42
Figure 26. Stages of demonstration.    43
Figure 27. SOC design process.    45
Figure 28. Cell design process.    49
Figure 29. Full custom design process.    52
Figure 30. Workflow design by using MS Visio.    54
Figure 31. Tasks of workflow.    55
Figure 32. BBN relationship map.    55
Figure 33. Probabilities of completed days and cost.    56
Figure 34. Estimation of risk analysis.    57
Figure 35. Status of execution tasks.    57
Figure 36. Result of execution.    58


Table Contents
Table 1. Probability of variable number and loop number    24
Table 2. Condition probability of variable number.    25
Table 3. Condition probability of loop number.    25
Table 4. Function list.    36
Table 5. SOC factors.    46
Table 6. Cell factors.    50
Table 7. Full custom factors.    53
Table 8. Output comparison sheet.    59
Table 9. Functional comparison.    61

                                

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