臺灣博碩士論文加值系統

專案管理(Project Management，PM)最重要的三項管理目標「時程、效能、成本」，一個成功的專案涉及到專案管理十大知識領域及五大流程，專案執行時需做好全面性的監控，才可以真正落實專案管理的目標。然而，通常專案小組會用百分比的方式回報專案經理任務完成的進度，由於專案任務性質不同，定義任務完工的百分比的方式也不同，所以專案經理很難依據百分比來掌控每一專案任務的實際進度狀況，因此利用實獲值方式把所有專案工期及進度都轉化為“金額”，再與初期計畫預算金額做比較，來表示專案任務實際進度。
實獲值管理(Earned Value Management，EVM)雖然可以提供整合評估時程績效及成本績效衡量的一種指標，但是在實用上仍然存在著幾個缺點例如：(1)會增加管理成本(2)實獲值無法提供預測採購專案的成功或失敗(3)評估的指標無法分辨其重要程度。針對上述缺點部分，本研究提出了整合性實獲值管理觀點，可以預先評估採購專案成功的機率以及影響評估變項的重要程度排序。
為了驗證本研究整合實獲值管理的模式，先以實獲值管理方法導入於企業專案採購評估原物料第二商源，探討其專案時程進度是否超前或落後，以及專案預算執行超支或符合預算並建立對策。接著再利用羅吉斯迴歸分析(Logistic Regression Analysis)建立預測模型；並配合專家權重法(Expert Weighting Method)，選擇分析影響專案採購評估五項變項之重要度排序。研究結論發現變項中成本績效指標(Cost Performance Index，CPI)影響預測成功的機率極大，在執行材料第二商源評估單獨考慮CPI=1時，其預測成功的機率高達71.26%。影響評估變項之重要度以CPI為重要程度最高，表示在材料第二商源評估時，採購專家比較重視成本績效指標，結果可作為未來提升採購專案績效相關工作之推動。

There are three important management objectives of Project Management: Schedule, Performance and Cost. The success of a project involves ten knowledge Areas and five process groups, when the project is executed, comprehensive monitoring is required to truly implement the project management objectives. However, project team will report the progress of task completion to project manager in percentage way. Because of the different property of the project task, the percentage of the completion of the task is defined in different ways. Therefore, it is difficult for the project manager to control the actual progress of each project task based on the percentage. As a result, all project of schedules and progress are converted to “amounts” using the Earned Value Management (EVM). Compare with the initial budget amount to mean the actual progress of the project task.
The EVM can provide an indicator for integrating assessment of schedule performance and cost performance index. But there are still several defects in practicality, such as: (1) will increase management costs. (2) The Earned Value does not provide a forecast of the success or failure for the procurement project. (3) The assessment of index cannot distinguish importance. To handle these defects, the research proposed an integrated EVM perspective which can predict the success rate of procurement projects and rank the importance of the evaluation variables.
In order to verify the model of integrated EVM in this study. Introduced by the EVM to evaluation second source of raw materials into the enterprise project procurement. Exploring whether the project schedule is ahead or behind and the project budget is overrun or meets the budget and establishes countermeasures. Then it can use the Logistic Regression Analysis to build a prediction model. With the Expert Weighting Method (EWM), select and analyze the importance ranking of the five variables affecting the project procurement evaluation. The conclusion of this study that the Cost Performance Index "CPI" in the variable affect the probability of successful forecast greatly. When CPI=1 is considered separately in the material second source of evaluation of the implementation. The probability of predicting success is as high as 71.26%. The importance of impact assessment variables is most important with CPI. Purchasing experts pay more attention to CPI in the second source of material assessment. The results can be used as a catalyst for future work related to improving the performance of procurement projects.

ABSTRACT(CHINESE)...................................I
ABSTRACT(ENGLISH)...................................II
CONTENTS............................................III
LIST OF TABLES......................................IV
LIST OF FIGURES......................................V
CHAPTER 1 INTRODUCTION...............................1
1.1 Research Background and Motives..................1
1.2 Research Objectives..............................4
1.3 Research Scope and Limitations...................6
1.4 Research Structure...............................7
CHAPTER 2 LITERATURE REVIEW..........................8
2.1 Project Management...............................8
2.2 Earned Value Management.........................12
2.3 Logistic Regression Analysis....................20
2.4 Expert Weights Method...........................24
CHAPTER 3 RESEARCH METHODS..........................28
3.1 Earned Value Management Process.................30
3.2 Steps for Logistic Regression Analysis..........36
3.3 Analysis Steps for Expert Weights...............40
CHAPTER 4 CASE STUDY................................43
4.1 An Introduction to Industry Background..........43
4.2 EVM Application in This Case....................45
4.3 The Predition for Cost Reduction Effects........61
4.4 The Results of the Expert Wieghted Analysis.....71
CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS...........75
5.1 Research Results................................75
5.2 Suggestions for Future Research.................77
References..........................................79

Acebes, F., Pajares, J., Galán, J. M., & López-Paredes, A. (2013). Beyond earned value management: A graphical framework for integrated cost, schedule and risk monitoring. Procedia-Social and Behavioral Sciences, 74, 181-189.
Akkus, Z., Satici, O., Sanisoglu, S. Y., Akyol, M., & Keskin, S. (2012). Determination of Medicine and Dentistry Students? Market Choice and Their Attıtudes in Preferring Brand Name Products Using Binary Logistics Regression Method. Journal Of International Dental And Medical Research, 5(3), 173-178.
Anbari, F. T. (2003). Earned value project management method and extensions. Project management journal, 34(4), 12-23.
Attarzadeh, I., & Ow, S. H. (2008). Modern project management: essential skills and techniques. Communications of the IBIMA, 2, 1-9.
Bao, C. P., Wei, N. C., & Chen, S. C. (2015). Elevating the Quality of Staffs At Jewelry Boutique Stores: Implementing Chink less-teaching Concept for Course Training and Planning. International Journal of Organizational Innovation, 8(1), 186–205.
Bao, C. P., Wei, N. C., Lin, T. J., Lai, H. C., & Yang, A. L. (2018). Theoretical Proof and Application of Experts Weighting Method. International Journal of Organizational Innovation (Online), 10(4), 44-53.
Cheng, D., Cheng, F., Zhou, Z., & Wang, J. (2017). Group prioritization with unknown expert weights in incomplete linguistic context. International Journal of Systems Science, 48(12), 2633-2643.
Colin, J., & Vanhoucke, M. (2015). A comparison of the performance of various project control methods using earned value management systems. Expert Systems with Applications, 42(6), 3159-3175.
Creamer, G. G., Ren, Y., Sacamoto, Y., & Nickerson, J. V. (2013). News and sentiment analysis of the european market with a hybrid expert weighting algorithm. In Social Computing (SocialCom), 2013 International Conference on IEEE, 391-396.
Czemplik, A. (2014). Application of earned value method to progress control of construction projects. Procedia Engineering, 91, 424-428.
Dodson, M., Defavari, G., & de Carvalho, V. (2015). Quality: the third element of earned value management. Procedia Computer Science, 64, 932-939.
Dutta, A., Bandopadhyay, G., & Sengupta, S. (2012). Prediction of stock performance in indian stock market using logistic regression. International Journal of Business and Information, 7(1).
Fisher, C. (2014). New Techniques in Project Management. American Journal of Industrial and Business Management, 4(12), 739.
Fleming, Q. W., & Koppelman, J. M. (2006). Earned Value Project Management A Powerful Tool for Software Projects. Software Management, 16, 337.
Gollner, J., Andersen, R., Gollner, K., & Webster, T. (2015). A study of the usefulness of deploying a questionnaire to identify cultural dynamics potentially affecting a content management project. IEEE Transactions on Professional Communication, 58(3), 289-308.
Gong, J., & Sun, S. (2009). A new approach of stock price prediction based on logistic regression model. In New Trends in Information and Service Science, 2009. NISS''09. International Conference on IEEE, 1366-1371.
Guide, A. (2001). Project management body of knowledge (pmbok® guide). In Project Management Institute.
Gumus, S., Egilmez, G., Kucukvar, M., & Park, Y. S. (2016). Integrating expert weighting and multi-criteria decision making into eco-efficiency analysis: the case of US manufacturing. Journal of the Operational Research Society, 67(4), 616-628.
Han, Y., Yang, M., Qi, H., He, X., & Li, S. (2009). The Improved Logistic Regression Models for Spam Filtering. In Asian Language Processing, 2009. IALP''09. International Conference on IEEE, 314-317.
Hans, R. T. (2015). Using a web-based application to teach students Earned Value Management concepts. In Computer, Communications, and Control Technology (I3CT), 2015 International Conference on IEEE, 375-378.
Hesse, C. A., Ofosu, J. B., & Oduro, F. T. (2016). Logistic Regression Approach to Modelling Road Traffic Casualties in Ghana. Research Journal of Mathematics and Statistics, 8(1), 1-7.
Huang, F., Jiang, Z., Zhang, S., & Gao, S. (2010). Reliability evaluation of wireless sensor networks using logistic regression. In Communications and Mobile Computing (CMC), 2010 International Conference on IEEE, 334-338.
Huang, T. T. (2007). Evaluating customer satisfaction using fuzzy model based on flexible expert weight. In Industrial Engineering and Engineering Management, 2007 IEEE International Conference on IEEE, 1700-1703.
Izadpanah, H., Arbabi, H., & Kord, B. (2012). The Reality of Project Management Office for Construction Organization in the Oil, Gas and Petrochemical Industry of Iran. Research Journal of Applied Sciences, Engineering and Technology, 4(15), 2515-2522.
Khalid, T. A., & Yeoh, E. T. (2015). Controlling software cost using fuzzy Quality based EVM. In Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE), 2015 International Conference on IEEE, 275-280.
Kuhl, M. E., & Graciano, M. K. P. (2014). Project planning and predictive earned value analysis via simulation. In Simulation Conference (WSC), 2014 Winter, 3377-3387.
Li, W., Liang, W., Zhang, L., & Tang, Q. (2015). Performance assessment system of health, safety and environment based on experts'' weights and fuzzy comprehensive evaluation. Journal of Loss Prevention in the Process Industries, 35, 95-103.
Li, Y., Zhu, Y., & Wu, J. (2017). Research on expert weighting method based on DS evidence theory. IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2017 International Conference on IEEE, 992-996.
Lu, T., Dunyao, Z., Lixin, Y., & Pan, Z. (2015). The traffic accident hotspot prediction: Based on the logistic regression method. In Transportation Information and Safety (ICTIS), 2015 International Conference on IEEE, 107-110.
Ma, X., & Yang, B. (2012). Optimization study of Earned Value Method in construction project management. In Information Management, Innovation Management and Industrial Engineering (ICIII), 2012 International Conference on IEEE, 201-204.
Moebes, T. A. (2011). Lithium battery analysis: probability of failure assessment using logistic regression. In Reliability and Maintainability Symposium (RAMS), 2011 Proceedings-Annual on IEEE, 1-5.
Mohammadjafari, M., Ahmed, S., Dawal, S., & Zayandehroodi, H. (2012). A framework for sharing knowledge in product development by e-collaboration through project management. Research Journal of Applied Sciences, Engineering and Technology, 4(6), 649-659.
Morad, M., & El-Sayegh, S. M. (2016). Use of Earned Value Management in the UAE Construction Industry. In Industrial Engineering, Management Science and Application (ICIMSA), 2016 International Conference on IEEE, 1-5.
Naderpour, A., & Mofid, M. (2011). Improving construction management of an educational center by applying earned value technique. Procedia engineering, 14, 1945-1952.
Naeni, L. M., Shadrokh, S., & Salehipour, A. (2014). A fuzzy approach for the earned value management. International Journal of Project Management, 32(4), 709-716.
Nguyen, Q. T., & Chua, D. K. (2014). Criticality of Schedule Constraints--Classification and Identification for Project Management. Journal of Engineering, Project & Production Management, 4(1).
Pokharel, S., Yeo, K. T., & Wang, T. Y. (2006). Value of project management in high-tech and manufacturing operations. Journal of the Chinese Institute of Industrial Engineers, 23(6), 443-448.
Sidawi, B. (2012). Potential use of communications and project management systems in remote construction projects: the case of Saudi Electric Company. Journal of Engineering, Project, and Production Management, 2(1), 14.
Wan, J., & Liang, L. (2012). Risk management of IT service management project implementation with killer assumptions. Technology and Investment, 3(01), 48.
Warburton, R. D. (2011). A time-dependent earned value model for software projects. International Journal of Project Management, 29(8), 1082-1090.
Wei, N. C., Bao, C. P., Yao, S. Y., & Wang, P. S. (2016). Earned Value Management Views on Improving Performance of Engineering Project Management. International Journal of Organizational Innovation 8(4), 93-111.
Xu, Z., & Cai, X. (2012). Minimizing group discordance optimization model for deriving expert weights. Group Decision and Negotiation, 21(6), 863-875.
Ying, H. (2016). The Highway Construction Cost Control Model Based on the Improved Earned Value Method Theory. In Smart City and Systems Engineering (ICSCSE), International Conference on IEEE, 461-464.
Yue, D., Wu, X., Shen, N., & Chu, C. H. (2009). Logistic regression for detecting fraudulent financial statement of listed companies in China. In Artificial Intelligence and Computational Intelligence, 2009. AICI''09. International Conference on IEEE 2, 104-108.
Zhang, Q., & Fu, Q. (2009). Performance Evaluation Model of Engineering Project Management Based on Improved Wavelet Neural Network. Journal of Service Science and Management, 2(01), 10.
Zhang, X., & Xu, Z. (2014). Deriving experts'' weights based on consistency maximization in intuitionistic fuzzy group decision making. Journal of Intelligent & Fuzzy Systems, 27(1), 221-233.
Zhong, S., & Wang, X. (2011). Improvement and application of earned value analysis in coal project management. Procedia Engineering, 26, 1983-1989.