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研究生: 曾雯欣
Tseng, Wen-Hsin
論文名稱: 基於模糊多目標規劃之網路資料包絡分析評估面板產業之供應網路績效
Fuzzy Multi-Objective Programming Based Network Data Envelopment Analysis for Evaluating the Performance of TFT-LCD Panel Industry Supply Networks
指導教授: 黃啟祐
Huang, Chi-Yo
學位類別: 碩士
Master
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2018
畢業學年度: 106
語文別: 英文
論文頁數: 79
中文關鍵詞: 多目標規劃網路資料包絡分析供應網路績效評估面板產業
英文關鍵詞: Multiple Objective Decision Making, Network Data Envelopment Analysis, Supply Network, Performance Evaluation, TFT-LCD Panel Industry
DOI URL: http://doi.org/10.6345/THE.NTNU.DIE.050.2018.E01
論文種類: 學術論文
相關次數: 點閱:130下載:0
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  • 近年來受到全球化及快速變遷的市場環境影響,企業的生存除仰賴有效的運用資源外,更必須成功地與供應鏈上下游夥伴配合以達到整體的最大利益。因此供應鏈網路成為公司之間信息與技術流動的關鍵。而本研究將以網路資料包絡法來探究供應鏈網路間之績效評估。
    網路資料包絡法改良自傳統資料包絡法,考慮組織或供應網路的結構,探討系統內部結構與內部流程之間的互動及影響以評估績效,且可用以分析無明確關聯的因子之間的效率,為近年來新興之方法。但因供應網路組成公司的投入、產出之資訊未必完整揭露。故本研究將提出一個多目標規劃網路資料包絡分析法模型,將可以(1)解決供應網路之投入、產出資訊揭露不完全問題;(2)分析供應網路內部生產活動;(3) 以供應網路整體的觀點來評估效率。
    本研究以我國面板產業包含上游關鍵零組件供應、中游面板生產及下游產品組裝之完整供應網路實證本研究之可行性,實證研究之結果,可提供企業有利的供應鏈策略與績效改善之參考外,亦可作為投資者評估投資標的與投資組合時之依據。

    In recent years, the global environment and rapid changes in the market environment, the survival of enterprises in addition to relying on the effective use of resources, but must be successful with the supply chain partners to achieve the overall best interests. So, the supply chain network has become the key to the flow of information and technology between companies. In this study, the network data envelopment method will be used to explore the performance evaluation between supply chain networks.
    The network data envelopment analysis method improvement from the traditional network data envelopment analysis (DEA). It considers the structure of the organization or supply network, discusses the interaction and influence between the internal structure and the internal process of the system to evaluate the performance, and analyzes the efficiency among the unrelated factors. The input and output information of the supply network is not necessarily exposed. Therefore, this study will use a multiple objective decision making formula which can be used to analyze the uncertain values and provide: (1) to solve the supply network composed of input and output, the problem of incomplete information disclosing; (2) analysis of supply network internal production activities; (3) to evaluate efficiency in terms of the overall view of the supply network.
    This research will include the TFT-LCD industry includes component providers, TFT-LCD panel manufacturers, and end product designers/assemblers the complete supply network the empirical study in Taiwan’s TFT-LCD industry. The results of empirical research can provide a useful supply network strategy and performance improvement of the enterprise reference, but also can be used as investors evaluate investment or portfolio basis.

    摘要 i Abstract ii Table of Content v List of Table vi List of Figure viii Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Research Motivations 2 1.3 Research Purposes 3 1.4 Research Process 4 1.5 Research Limitations 5 1.6 Research Structure 6 Chapter 2 Literature review 7 2.1 Supply Chain 7 2.2 Supply Network 8 2.3 Performance Evaluation 12 2.4 Performance Evaluation of Network DEA 13 2.5 Performance Evaluation of Supply Network 15 Chapter 3 Research Methods 19 3.1 Network DEA 19 3.2 Definitions of model parameters 24 3.3 Multi-objective programming based Network DEA model 26 3.4 Modified Delphi Method 33 3.5 Network DEA Structure 36 3.6 Research Structure 37 Chapter 4 Empirical Study 39 4.1 Industry Background and Research Problem Description 39 4.2 Definitions of Variables by the Modified Delphi Method 42 4.3 The Efficiency Value of Supply Network by Network DEA 53 Chapter 5 Discussion and Implication 67 5.1 The Efficiency of DMUs 67 5.2 Managerial Implication 68 Chapter 6 Conclusion 71 Reference 73

    Alarcón, F., Alemany, M., & Ortiz, A. (2009). Conceptual framework for the characterization of the order promising process in a collaborative selling network context. International Journal of Production Economics, 120(1), 100-114.
    Alonso-Rasgado, T., Thompson, G., & Elfström, B.-O. (2004). The design of functional (total care) products. Journal of engineering design, 15(6), 515-540.
    Amindoust, A., Ahmed, S., Saghafinia, A., & Bahreininejad, A. (2012). Sustainable supplier selection: A ranking model based on fuzzy inference system. Applied Soft Computing, 12(6), 1668-1677.
    Baines, T. S., Lightfoot, H. W., Evans, S., Neely, A., Greenough, R., Peppard, J., . . . Tiwari, A. (2007). State-of-the-art in product-service systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 221(10), 1543-1552.
    Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
    Bellamy, M. A., Ghosh, S., & Hora, M. (2014). The influence of supply network structure on firm innovation. Journal of Operations Management, 32(6), 357-373.
    Boltic, Z., Ruzic, N., Jovanovic, M., Savic, M., Jovanovic, J., & Petrovic, S. (2013). Cleaner production aspects of tablet coating process in pharmaceutical industry: problem of VOCs emission. Journal of Cleaner Production, 44, 123-132.
    Bowlin, W. F. (1987). Evaluating the efficiency of US Air Force real-property maintenance activities. Journal of the Operational Research Society, 34(1), 127-135.
    Brooks, K. W. (1979). Delphi technique: Expanding applications. North Central Association Quarterly, 53(3), 377-385.
    Charnes, A., Cooper, W. W., Golany, B., Seiford, L., & Stutz, J. (1985). Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of econometrics, 30(2), 91-107.
    Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
    Chen, C., & Yan, H. (2011). Network DEA model for supply chain performance evaluation. European journal of operational research, 213(1), 147-155.
    Chern, C.-C., Chou, T.-Y., & Hsiao, B. (2016). Assessing the efficiency of supply chain scheduling algorithms using data envelopment analysis. Information Systems and e-Business Management, 14(4), 823-856.
    Chopras, M. (2004). sodhis. Managing risk to avoid supply chain breakdown. MIT Sloan Management Review, 46, 53-61.
    Chung, W. W., Yam, A. Y., & Chan, M. F. (2004). Networked enterprise: A new business model for global sourcing. International Journal of Production Economics, 87(3), 267-280.
    Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management science, 9(3), 458-467.
    Dües, C. M., Tan, K. H., & Lim, M. (2013). Green as the new Lean: how to use Lean practices as a catalyst to greening your supply chain. Journal of Cleaner Production, 40, 93-100.
    Dyer, J. H., & Nobeoka, K. (2000). Creating and managing a high-performance knowledge-sharing network: the Toyota case. Strategic Management Journal, 34,345-367.

    Eschenbächer, J., & Zwegers, A. (2003). Collaboration in value creating networks: the concept of collaborative commerce Collaborative systems for production management. Berlin, Germany: Springer.
    Estampe, D., Lamouri, S., Paris, J.-L., & Brahim-Djelloul, S. (2013). A framework for analysing supply chain performance evaluation models. International Journal of Production Economics, 142(2), 247-258.
    Färe, R., & Grosskopf, S. (2000). Network dea. Socio-economic planning sciences, 34(1), 35-49.
    Färe, R., Grosskopf, S., Lovell, C. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. The review of economics and statistics, 20(1), 90-98.
    Galagedera, D. U., Roshdi, I., Fukuyama, H., & Zhu, J. (2018). A new network DEA model for mutual fund performance appraisal: An application to US equity mutual funds. Omega, 77, 168-179.
    Guiffrida, A. L., & Nagi, R. (2006). Cost characterizations of supply chain delivery performance. International Journal of Production Economics, 102(1), 22-36.
    Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance measures and metrics in a supply chain environment. International journal of operations & production Management, 21(2), 71-87.
    Hameri, A.-P., & Paatela, A. (2005). Supply network dynamics as a source of new business. International Journal of Production Economics, 98(1), 41-55.
    Harland, C. M. (1996). Supply chain management: relationships, chains and networks. British Journal of management, 7(1), 22-25.
    Hearnshaw, E. J., & Wilson, M. M. (2013). A complex network approach to supply chain network theory. International journal of operations & production Management, 33(4), 442-469.
    Huang, C.-Y., Shyu, J. Z., & Tzeng, G.-H. (2007). Reconfiguring the innovation policy portfolios for Taiwan's SIP Mall industry. Technovation, 27(12), 744-765.
    Huang, C.-Y., Tzeng, G. H., Chen, Y. T., & Chen, H. (2012). Performance evaluation of leading fabless integrated circuit design houses by using a multiple objective programming based data envelopment analysis approach. International Journal of Innovative Computing, Information and Control, 8(8), 5899-5916.
    Izadikhah, M., & Saen, R. F. (2016). Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data. Transportation Research Part D: Transport and Environment, 49, 110-126.
    Janvier-James, A. M. (2012). A new introduction to supply chains and supply chain management: Definitions and theories perspective. International Business Research, 5(1), 194.
    Jones, J., & Hunter, D. (1995). Consensus methods for medical and health services research. BMJ: British Medical Journal, 311(7001), 376.
    Judd, R. C. (1972). Forecasting to Consensus Gathering, Delphi Grows Up to College Needs. College and University Business, 23(1),400.
    Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European journal of operational research, 192(3), 949-962.
    Kao, H.-Y., Chan, C.-Y., & Wu, D.-J. (2014). A multi-objective programming method for solving network DEA. Applied Soft Computing, 24, 406-413.
    Khodakarami, M., Shabani, A., Saen, R. F., & Azadi, M. (2015). Developing distinctive two-stage data envelopment analysis models: An application in evaluating the sustainability of supply chain management. Measurement, 70, 62-74.

    Lamming, R., Johnsen, T., Zheng, J., & Harland, C. (2000). An initial classification of supply networks. International Journal of Operations & Production Management, 20(6), 675-691.
    Lee, E. S., & Li, R. J. (1993). Fuzzy multiple objective programming and compromise programming with Pareto optimum. Fuzzy sets and Systems, 53(3), 275-288.
    Lee, Y.-S., Huang, J.-C., & Hsu, Y.-S. (2008). Using modified delphi method to explore the competition strategy for software companies of Taiwan. Journal of Informatics & Electronics, 13(1), 39-50.
    Lertworasirikul, S., Fang, S.-C., Joines, J. A., & Nuttle, H. L. (2003). Fuzzy data envelopment analysis (DEA): a possibility approach. Fuzzy sets and Systems, 139(2), 379-394.
    Manthou, V., Vlachopoulou, M., & Folinas, D. (2004). Virtual e-Chain (VeC) model for supply chain collaboration. International Journal of Production Economics, 87(3), 241-250.
    Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business logistics, 22(2), 1-25.
    Mirhedayatian, S. M., Azadi, M., & Saen, R. F. (2014). A novel network data envelopment analysis model for evaluating green supply chain management. International Journal of Production Economics, 147, 544-554.
    Mone, E., London, M. (2018). Employee Engagement Through Effective Performance Management. Abingdon-on-Thames, Englad: Routledge.
    Murry Jr, J. W., & Hammons, J. O. (1995). Delphi: A versatile methodology for conducting qualitative research. The Review of Higher Education, 18(4), 423-436.

    Naesens, K., Gelders, L., & Pintelon, L. (2009). A swift response framework for measuring the strategic fit for a horizontal collaborative initiative. International Journal of Production Economics, 121(2), 550-561.
    Olfat, L., Amiri, M., & Ebrahimpour Azbari, M. (2014). A Network data envelopment analysis model for supply chain performance evaluation: real case of Iranian pharmaceutical industry. International Journal of Industrial Engineering & Production Research, 25(2), 125-138.
    Park, H., Bellamy, M. A., & Basole, R. C. (2016). Visual analytics for supply network management: System design and evaluation. Decision Support Systems, 91, 89-102.
    Perrini, F., & Tencati, A. (2006). Sustainability and stakeholder management: the need for new corporate performance evaluation and reporting systems. Business Strategy and the Environment, 15(5), 296-308.
    Pournader, M., Kach, A., Fahimnia, B., & Sarkis, J. (2016). Outsourcing performance quality assessment using data envelopment analytics. International Journal of Production Economics, 22(3), 200-210.
    Rezapour, S., Srinivasan, R., Tew, J., Allen, J. K., & Mistree, F. (2018). Correlation between strategic and operational risk mitigation strategies in supply networks. International Journal of Production Economics, 201, 225-248.
    Sung, W.-C. (2001). Application of Delphi method, a qualitative and quantitative analysis, to the healthcare management. Journal of Healthcare Management, 2(2), 11-19.
    Tajbakhsh, A., & Hassini, E. (2015). A data envelopment analysis approach to evaluate sustainability in supply chain networks. Journal of Cleaner Production, 105, 74-85.
    Tavana, M., Khalili-Damghani, K., Arteaga, F. J. S., Mahmoudi, R., & Hafezalkotob, A. (2018). Efficiency decomposition and measurement in ,Computers & Industrial Engineering, 118, 394-408.
    Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European journal of operational research, 130(3), 498-509.
    Tulkens, H. (1993). On FDH efficiency analysis: some methodological issues
    and applications to retail banking, courts, and urban transit Productivity Issues in Services at the Micro Level. Berlin, Grmany: Springer.
    Van Dooren, W., Bouckaert, G., & Halligan, J. (2015). Performance management in the public sector. Abingdon-on-Thames, Englad: Routledge.
    Vidalis, M., Koukoumialos, S., Diamantidis, A., & Blanas, G. (2015). Performance evaluation of a merge supply network: A production centre with multiple different reliable suppliers. SMMSO 2015, 255-257.
    Von Hippel, E. (2007). The sources of innovation Das Summa Summarum des Management. Berlin, Germany: Springer.
    Waters, D., & Rinsler, S. (2014). Global logistics: New directions in supply chain management. London, Englad: Kogan Page Publishers.
    Zhuo, Y., Xu, J., Wei, F., Xu, L., Lin, X., & Li, Z. (2016). Design of power supply network based on 500/110 kv for load center and comprehensive accessibility evaluation. CSEE Journal of Power and Energy Systems, 2(1), 30-39.

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