臺灣博碩士論文加值系統

最佳競標案的選擇通常涉及複雜且高風險的決策流程。市場的不確定性、資訊的不對稱、以及競爭對手的動態策略對競標專案的成功與否有著決定性的影響。然而對資本設備製造商而言，由於產業特性的關係，每個專案的競標過程往往都必須耗費大量的時間與資源投入，因此錯誤的競標案選擇，對企業的損失是相當巨大的。針對這項議題，本研究以一家國際半導體資本設備製造商為研究對象，運用優勢關係約略集合理論針對該公司過去四年的198個競標案作歷史資料分析，探討其各種決策因素與競標案成功率之間的因果關係。根據不同的競標結果，本研究歸納出數十筆決策規則 (Decision Rules) 供決策者做事前評估參考。研究發現價格的競爭力、顧客的資金狀況、顧客參與專案討論的層級與積極度、技術對顧客的商業影響力、競爭者與顧客的政商關係對多數競標案的成功與否有關鍵性的影響。但是在大多數的狀況下，這些因素的水準並不容易直接被察覺，因此組織高層的投入、業務員的情報蒐集與動態應變能力、以及即時的環境監控在競標案事前與事中階段扮演著舉足輕重的腳色。

The project bidding usually involves a complicated and high-risk decision making process. In the high face of a highly competitive and fast-changing semiconductor capital equipment market, capital equipment suppliers must not only provide high-quality products but react appropriately to changes in customer needs. Many resources in research and development (R&D) process before and after bidding have to be invested on a project. A wrong bidding decision would cause great loss to a capital equipment supplier, either in the perspective of time consuming or money wasting. Unlike the previous studies, we utilized a more intuitive data mining technique called dominance based rough set theory (DRST) for decision rule induction that could provide more straightforward aids for bid/ no bid decision. The 4-year 198 historical data of the sales department of a multinational semiconductor capital equipment supplier were collected. With this technique, the features between 118 successful-bidden projects and 80 unsuccessful-bidden projects in Asia-pacific region were compared and the specific decision rules for the two conditions were generalized. We found that the low level of the price position of the case company and the low level of customer’s funding status leaded to the primary failure of project bidding. The other relatively implicit factors such as the degree of customer’s involvement, the degree of executive sponsorship, influence to customer’s business impact and competitor political leverage influence should be carefully inspected and evaluated in advance.

1. Introduction 6
2. Literature Review 8
2.1 Key Success Factor in Capital Equipment Project Competition 8
2.1.1 Key Success Factors for Project Bidding 9
2.1.2 Project evaluation models 10
2.2 Rough Set Theory 13
2.2.1 Basic concepts of rough set theory 14
2.2.2 Indiscernibility relation and classification 16
2.2.3 Attribute dependence and approximation accuracy 17
2.2.4 Reduct and core attribute sets 18
2.2.5 Decision rules 19
2.3 Dominance-based Rough Set Approach 20
2.3.1 Data Table 21
2.3.2 Dominance Relations and Rough Approximation 21
2.3.3 Decision Rules 24
3. Research Methodologies 25
3.1 Empirical Study: Semiconductor Equipment Project Competition under Uncertainty 25
3.2 Preparation of the information table for bid/ no bid decision 29
4. Research Findings 30
4.1 Rules for win the bid 30
4.2 Rules for lose the bid 33
5. Conclusions and Remarks 33
References 36

Ahmad, Irtishad, & Minkarah, Issam. (1988). Questionnaire survey on bidding in construction. Journal of Management in Engineering, 4(3), 229-243.
Aragones-Beltran, P, Chaparro-Gonzalez, F, Pastor-Ferrando, JP, & Rodriguez-Pozo, F. (2010). An ANP-based approach for the selection of photovoltaic solar power plant investment projects. Renewable and Sustainable Energy Reviews, 14(1), 249-264.
Cagno, Enrico, Caron, Franco, & Perego, Alessandro. (2001). Multi-criteria assessment of the probability of winning in the competitive bidding process. International Journal of Project Management, 19(6), 313-324.
Chang, Ping-Teng, & Lee, Jung-Hua. (2012). A fuzzy DEA and knapsack formulation integrated model for project selection. Computers & Operations Research, 39(1), 112-125.
Chen, Yumin, Miao, Duoqian, & Wang, Ruizhi. (2010). A rough set approach to feature selection based on ant colony optimization. Pattern Recognition Letters, 31(3), 226-233.
Cheng, Eddie WL, & Li, Heng. (2005). Analytic network process applied to project selection. Journal of Construction Engineering and Management, 131(4), 459-466.
Chua, DKH, & Li, D. (2000). Key factors in bid reasoning model. Journal of Construction Engineering and Management, 126(5), 349-357.
Chua, DKH, Li, DZ, & Chan, WT. (2001). Case-based reasoning approach in bid decision making. Journal of Construction Engineering and Management, 127(1), 35-45.
Conejo, Antonio J, Nogales, Francisco Javier, & Arroyo, Jose Manuel. (2002). Price-taker bidding strategy under price uncertainty. Power Systems, IEEE Transactions on, 17(4), 1081-1088.
Dozzi, SP, AbouRizk, SM, & Schroeder, SL. (1996). Utility-theory model for bid markup decisions. Journal of Construction Engineering and Management, 122(2), 119-124.
Dulaimi, Mohammed Fadhil, & Shan, Hon Guo. (2002). The factors influencing bid mark-up decisions of large-and medium-size contractors in Singapore. Construction Management & Economics, 20(7), 601-610.
El-Sayegh, Sameh Monir. (2008). Risk assessment and allocation in the UAE construction industry. International Journal of Project Management, 26(4), 431-438.
Fang, Sheng-Kai, Shyng, Jhieh-Yu, Lee, Wen-Shiung, & Tzeng, Gwo-Hshiung. (2012). Exploring the preference of customers between financial companies and agents based on TCA. Knowledge-Based Systems, 27, 137-151.
Friedman, Lawrence. (1956). A competitive-bidding strategy. Operations research, 4(1), 104-112.
Greco, Salvatore, Matarazzo, Benedetto, & Słowinński, Roman. (2005). Decision rule approach Multiple criteria decision analysis: state of the art surveys (pp. 507-555): Springer.
Greco, Salvatore, Matarazzo, Benedetto, & Slowinski, Roman. (1998). A new rough set approach to evaluation of bankruptcy risk Operational tools in the management of financial risks (pp. 121-136): Springer.
Herawan, Tutut, Deris, Mustafa Mat, & Abawajy, Jemal H. (2010). A rough set approach for selecting clustering attribute. Knowledge-Based Systems, 23(3), 220-231.
Iamratanakul, Supachart, Patanakul, Peerasit, & Milosevic, Dragan. (2008). Project portfolio selection: From past to present. Paper presented at the Management of Innovation and Technology, 2008. ICMIT 2008. 4th IEEE International Conference on.
Khazaeni, Garshasb, Khanzadi, Mostafa, & Afshar, Abas. (2012). Fuzzy adaptive decision making model for selection balanced risk allocation. International Journal of Project Management, 30(4), 511-522.
Kuo, Yao-Chen, & Lu, Shih-Tong. (2012). Using fuzzy multiple criteria decision making approach to enhance risk assessment for metropolitan construction projects. International Journal of Project Management.
Kusiak, Andrew. (2000). Decomposition in data mining: an industrial case study. Electronics Packaging Manufacturing, IEEE Transactions on, 23(4), 345-353.
Kusiak, Andrew. (2001). Rough set theory: a data mining tool for semiconductor manufacturing. Electronics Packaging Manufacturing, IEEE Transactions on, 24(1), 44-50.
Lai, Yu-Ting, Wang, Wei-Chih, & Wang, Han-Hsiang. (2008). AHP-and simulation-based budget determination procedure for public building construction projects. Automation in Construction, 17(5), 623-632.
Li, Yan, Shiu, Simon CK, Pal, Sankar K, & Liu, James NK. (2006). A rough set-based case-based reasoner for text categorization. International journal of approximate reasoning, 41(2), 229-255.
Li, Zongzhi, & Madanu, Sunil. (2009). Highway project level life-cycle benefit/cost analysis under certainty, risk, and uncertainty: methodology with case study. Journal of Transportation Engineering, 135(8), 516-526.
Liesio, Juuso, Mild, Pekka, & Salo, Ahti. (2007). Preference programming for robust portfolio modeling and project selection. European Journal of Operational Research, 181(3), 1488-1505.
Lin, Ching-Torng, & Chen, Ying-Te. (2004). Bid/no-bid decision-making–a fuzzy linguistic approach. International Journal of Project Management, 22(7), 585-593.
Liou, James JH, & Tzeng, Gwo-Hshiung. (2010). A dominance-based rough set approach to customer behavior in the airline market. Information Sciences, 180(11), 2230-2238.
Medaglia, Andres L, Graves, Samuel B, & Ringuest, Jeffrey L. (2007). A multiobjective evolutionary approach for linearly constrained project selection under uncertainty. European journal of operational research, 179(3), 869-894.
Nieto-Morote, A, & Ruz-Vila, F. (2011). A fuzzy approach to construction project risk assessment. International Journal of Project Management, 29(2), 220-231.
Pastor-Ferrando, JP, Aragones-Beltran, P, Hospitaler-Perez, A, & Garcia-Melon, M. (2010). An ANP-and AHP-based approach for weighting criteria in public works bidding. Journal of the Operational Research Society, 61(6), 905-916.
Pawlak, Zdzisław. (1982). Rough sets. International Journal of Parallel Programming, 11(5), 341-356.
Pawlak, Zdzisław. (2002). Rough sets, decision algorithms and Bayes'' theorem. European Journal of Operational Research, 136(1), 181-189.
Pawlak, Zdzislaw, Grzymala-Busse, Jerzy, Slowinski, Roman, & Ziarko, Wojciech. (1995). Rough sets. Communications of the ACM, 38(11), 88-95.
Seydel, John, & Olson, David L. (1990). Bids considering multiple criteria. Journal of Construction Engineering and Management, 116(4), 609-623.
Shakhsi-Niaei, M, Torabi, SA, & Iranmanesh, SH. (2011). A comprehensive framework for project selection problem under uncertainty and real-world constraints. Computers & Industrial Engineering, 61(1), 226-237.
Shyng, Jhieh-Yu, Wang, Fang-Kuo, Tzeng, Gwo-Hshiung, & Wu, Kun-Shan. (2007). Rough set theory in analyzing the attributes of combination values for the insurance market. Expert Systems with Applications, 32(1), 56-64.
Singh, D, & Tiong, Robert LK. (2005). A fuzzy decision framework for contractor selection. Journal of Construction Engineering and Management, 131(1), 62-70.
Sipahi, Seyhan, & Esen, Oner. (2010). A multi-criteria model for bidding evaluation: an alternative selection of the best firms for the presentation of Istanbul 2010. Management Decision, 48(2), 296-313.
Slowinski, Roman, Greco, Salvatore, & Matarazzo, Benedetto. (2005). Rough set based decision support Search Methodologies (pp. 475-527): Springer.
Słowiński, Roman, Greco, Salvatore, & Matarazzo, Benedetto. (2007). Dominance-based rough set approach to reasoning about ordinal data Rough Sets and Intelligent Systems Paradigms (pp. 5-11): Springer.
Soroor, Javad, Tarokh, Mohammad Jafar, Khoshalhan, Farid, & Sajjadi, Sara. (2011). Coordinated supplier bid selection based on customer order placement using an autonomous F-AHP–QFD-oriented methodology. International Journal of Production Research(ahead-of-print), 1-18.
Tan, Yong-tao, Shen, Li-yin, Langston, Craig, & Liu, Yan. (2010). Construction project selection using fuzzy TOPSIS approach. Journal of modelling in management, 5(3), 302-315.
Tay, Francis EH, & Shen, Lixiang. (2002). Economic and financial prediction using rough sets model. European Journal of Operational Research, 141(3), 641-659.
Thangavel, K, & Pethalakshmi, A. (2009). Dimensionality reduction based on rough set theory: A review. Applied Soft Computing, 9(1), 1-12.
Walczak, B, & Massart, DL. (1999). Rough sets theory. Chemometrics and intelligent laboratory systems, 47(1), 1-16.
Wang, Jin, Xu, Yujie, & Li, Zhun. (2009). Research on project selection system of pre-evaluation of engineering design project bidding. International Journal of Project Management, 27(6), 584-599.
Wang, Wei-Chih, Yu, Wen-der, Yang, I-Tung, Lin, Chun-Chang, Lee, Ming-Tsung, & Cheng, Yuan-Yuan. (2013). Applying the AHP to support the best-value contractor selection–lessons learned from two case studies in Taiwan. Journal of Civil Engineering and Management, 19(1), 24-36.
Wanous, M, Boussabaine, AH, & Lewis, J. (2000). To bid or not to bid: a parametric solution. Construction Management & Economics, 18(4), 457-466.
Weingartner, H Martin. (1966a). Capital budgeting of interrelated projects: survey and synthesis. Management Science, 12(7), 485-516.
Weingartner, H Martin. (1966b). Criteria for programming investment project selection. The Journal of industrial economics, 65-76.
Weingartner, H Martin. (1967). Mathematical programming and the analysis of capital budgeting problems: Markham Publishing Company Chicago, Ill.
Wey, Wann-Ming. (2008). A multiobjective optimization model for urban renewal projects selection with uncertainty considerations. Paper presented at the Natural Computation, 2008. ICNC''08. Fourth International Conference on.
Yazgan, Harun Resit, Boran, Semra, & Goztepe, Kerim. (2009). An ERP software selection process with using artificial neural network based on analytic network process approach. Expert Systems with Applications, 36(5), 9214-9222.
Yean Yng Ling, Florence, & Liu, Min. (2005). Factors considered by successful and profitable contractors in mark-up size decision in Singapore. Building and environment, 40(11), 1557-1565.
Yeh, Ching-Chiang, Chi, Der-Jang, & Hsu, Ming-Fu. (2010). A hybrid approach of DEA, rough set and support vector machines for business failure prediction. Expert Systems with Applications, 37(2), 1535-1541.