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題名 供應鏈風險評估模式建立之研究-以資訊電子產業為例
The Research on Developing a Risk Assessment Model - using Information and Electronics Industry as an example作者 陳有慶 貢獻者 林我聰
陳有慶關鍵詞 供應鏈風險管理
模糊理論
失效模式與效應分析法
決策實驗室分析法
分析網路程序法
Supply Chain Risk Management
Fuzzy Theory
FMEA
DEMATEL
ANP日期 2012 上傳時間 2-Sep-2013 16:02:06 (UTC+8) 摘要 產業分工與全球化因素影響,使企業與顧客、供應商分布在不同國家與地區,形成一個複雜與全球化的供應鏈網路,並導致整體供應鏈的風險大幅提升。企業如何做好供應鏈風險評估,已是目前聚焦的研究重點。那整體供應鏈流程包含採購、製造、配銷與回收階段,然而先前供應鏈風險管理研究上,大部分著重於單一階段進行討論,缺乏以整個供應鏈網路模式來進行研究,且假設風險因子之間為獨立、互不關連,也並未考慮整體供應鏈網路風險因子關聯特性,因而無法正確評估風險事件發生時所帶來的衝擊影響程度,自然無法進一步有效擬定出適當因應的風險管理策略。因此,本研究將針對「如何有效評估供應鏈風險程度」主要問題進行研究。 那針對此問題來進行研究,本研究提出一個整體供應鏈風險評估模式以有效評估供應鏈的風險程度。首先透過文獻探討確認整理出供應鏈的風險因子,並以SCOR模式(Supply Chain Operations Reference Model)的將風險因子歸類於採購(Source)、製造 (Make)、配銷(Deliver)與回收(Return)各階段內;接著藉由風險因子的發生率、嚴重度、難檢度,應用模糊失效模式與效應分析法(Fuzzy Failure Modes and Effects Analysis;Fuzzy FMEA)篩選出重要的風險因子;再使用模糊決策實驗室分析法(Fuzzy Decision Making Trial and Evaluation Laboratory;Fuzzy DEMATEL)建構出供應鏈流程中重要的風險因子間之關連性;由於風險因子間具有相互影響關係,本研究並採用分析網路程序法(Analytic Network Process;ANP)計算風險因子的影響權重。最後結合上述Fuzzy FMEA、Fuzzy DEMATEL與ANP方法,建立出整體供應鏈風險評估模式,並就由計算出風險因子對企業供應鏈的影響度,排列出風險因子的影響度大小,讓企業能夠知道風險因子的優先順序。這將協助企業有效進行供應鏈風險的評估,讓企業瞭解目前所處供應鏈潛在的風險,並據以研擬相關的因應策略,以降低其對供應鏈整體與成員的衝擊。
As part of the division of labor and globalization, enterprises, customers and suppliers are located in various countries and regions. Complex, globalized supply chain network have greatly increased total supply chain risks. Therefore, improving the management of risk associated with the supply chain has become important to many enterprises. And whole supply chain have include four stage, source, make, deliver and return, but previous supply chain risk management research has focused mainly on single-stage risk factors and assumed all risk factors to be mutually as independent and also not consider the whole supply chain risk factors associated characteristics. The lack of consideration for these relationships will lead to incorrect measuring risks and applying improper risk management strategies to solve these risks. Therefore this research concentrates on a topic: How to effectively assess risks. About the main issue, this research will propose an effectively risk assessment model. First, the research will consider whole supply chain risk factors from literature and then classify these factors into source stage’s factors, make stage’s factors, deliver stage’s factors and return stage’s factors in accordance with SCOR Model (Supply Chain Operations Reference Model) framework. Second, Fuzzy Failure Modes and Effects Analysis (Fuzzy FMEA), Fuzzy Decision Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) and Analytic Network Process (ANP) will be adopted and integrated to develop a supply chain risk assessment model. The results in this research will enable enterprises to determine in timely manner the effects of various risk events, enabling them to develop strategies to reduce their effect on the all members of the supply chain.參考文獻 英文部份 [1] Baloi, D. and Price, A. D. F., Modeling global risk factors affecting construction cost performance. International Journal of Project Management, Vol. 21, No. 4, 2003, pp. 261-269. [2] Blos, M. F., Quaddus, M. and Wee, H. M. Supply chain risk management (SCRM): a case study on the automotive and electronic industries in Brazil. Supply Chain Management: An International Journal, Vol. 14, No. 4, 2009, pp. 247-252. [3] Braglia, M., Frosolini, M. and Montannari, R., Fuzzy criticality assessment model for failure modes and effect analysis, International Journal Quality and Reliability Management, Vol. 20, No. 4, 2003, pp. 503-524. [4] Chang, B., Chang, C. H. and Wu, C. H., Fuzzy DEMATEL method for developing supplier selection criteria, Expert Systems with Application, Vol. 38, No.3 ,2011, pp. 1850-1858. [5] Chen, S. J. and Hwang C.L., Fuzzy Multiple Attribute Decision Making Methods and Applications, Berlin, New York, 1992. [6] Chin, K. S., Chen, A. and Yang, J. B., Development of a fuzzy FMEA based product design system, International Journal Advanced Manufacturing Technology, Vol. 36, No. 7-8, 2008, pp. 633-649. [7] Chin, K. S., Wang, Y. M., Poon, G. K. and Yang, J. B., Failure mode and effects analysis by data envelopment analysis, Decision support Systems, Vol. 48, No. 1, 2009, pp. 246-256. [8] Chong, A. Y. L., Ooi, K. B. and Sohal, A., The relationship between supply chain factors and adoption of e-Collaboration tools: An empirical examination, International Journal of Production Economics, Vol. 122, No.1, 2009, pp. 150-160. [9] Chopra, S. and Sodhi, M. S., Managing risk to avoid supply-chain breakdown, MIT Sloan Management Review, Vol. 46, No. 1, 2004, pp. 53-61. [10] Dong, C., Failure mode and effects analysis based on fuzzy utility cost estimation, International Journal of Quality and Reliability Management, Vol. 24, No. 9, 2007, pp. 114-136. [11] Gaudenzi, B. and Borghesi, A., Managing risks in the supply chain using the AHP method, The International Journal of Logistics Management, Vol. 24, No. 9, 2007, pp. 958-971. [12] Gilchrist, W., Modeling Failure Modes and Effects Analysis, International Journal of Quality and Reliability Management, Vol. 10, No. 5, 1993, pp. 16-24. [13] Haji, A. and Assadi, M., Fuzzy expert systems and challenge of new product pricing, Computer and Industrial Engineering, Vol. 56, No. 2, 2009, pp. 616-630. [14] Hallikas, J., Karvonen, I., Pulkkinen, U. and Virolainen, V. M., Risk management processes in supplier network, International journal of production economics, Vol. 90, No. 1, 2004, pp. 47-58. [15] Hallikas, J., Virolaien, V. M. and Tuominen, M., Risk analysis and assessment in network environments: A dyadic case study, International journal of production economics, Vol. 78, No. 1, 2002, pp. 45-55. [16]Harland, C., Brenchely, R. and Walker, H., Risk in supply networks, Journal of Purchasing and Supply Management, Vol. 9, No. 2, 2003, pp. 51-62. [17] Huan, S. H., Sheoran, S. K. and Keskar, H., Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model, Computers & Industrial Engineering, Vol. 48, No. 5, 2005, pp.377-394. [18] Inderfurth, K., Impact of uncertainties on recovery behavior in a remanufacturing environment a numerical analysis, International Journal of Physical Distribution and Logistics Management, Vol. 35, No. 5, 2005, pp. 318-336. [19] Juttner, U., Supply chain risk management understanding the business requirements from a practitioner perspective, The International Journal of Logistics Management, Vol. 16, No. 1, 2005, pp. 120-141. [20] Khan, O. and Burnes, B., Risk and supply chain management: creating a research agenda, The International Journal of Logistics, Vol. 18, No. 2, 2007, pp. 197-216. [21] Knemeyer, A. M., A qualitative examination of factors affecting revise logistics systems for end-of-life computers, International Journal of Physical Distribution and Logistics Management, Vol. 32, No. 6, 2002, pp. 455-479. [22] Kocabasoglu, C., Prahinski, C. and Klassen, R. D., Linking forward and revise supply chain investments, The role of business uncertainty, Journal of Operations Management, Vol. 25, No. 6, 2007, pp. 1141-1160. [23] Lambert, D. M. and Cooper, M. C., Issues in supply chain management, Internal Marketing Management, Vol. 29, No. 1, 2000, pp. 65-83. [24] Liou, J. J. H., Yen, L. and Tzeng, G. H., Building an effective safety management system for airlines, Journal of Air Transport Management, Vol. 14, No. 1, 2008, pp. 20-26. [25] Manuj, I. and Mentzer, J. T., Global supply chain risk management, Journal of Business Logistics, Vol. 29, No. 1, 2008, pp. 133-155. [26] Manuj, I. and Mentzer, J. T., Global supply chain risk management, International Journal of Physical Distribution and Logistics Management, Vol. 38, No. 3, 2008, pp. 192-223. [27] Min, H. and Zhou, G., Supply Chain modeling: past, present and future, Computer and Industrial Engineering, Vol. 43, No. 1, 2002, pp. 231-249. [28] Norrman, A. and Jansson, U., Ericsson’s proactive supply chain risk management approach after a serious sub-supplier accident, International Journal of Physical Distribution and Logistics Management, Vol. 34, No. 5, 2004, pp. 434-456. [29] Olson, D. L. and Wu, D. D., A review of enterprise risk management in supply chain, Kybernetes, Vol. 39, No. 5, 2010, pp. 694-706. [30] Opricovic, S. and Tzeng, G. H., Defuzzification within a multi criteria decision model, International Journal of Uncertainty, Vol. 11, No. 5, 2003, pp. 635-652. [31] Ritchie, B. and Brindley, C., Supply chain risk management and performance: A guiding framework for future development, International Journal of Operation and Production Management, Vol. 27, no. 3, 2007, pp. 303-322. [32] Saaty, T. L., The Analytic Hierarchy process – Planning, Priority Setting, Resource Allocation, New York, McGraw-Hill, 1980. [33] Saaty, T. L., Decision Making with Dependence and Feedback: The Analytic Network Process, 2nd edition, PA:RWS Publication, 2001. [34] Schoenherr, T., Tummala, V. M. R. and Harrision, T. P., Assessing supply chain risk with the analytic hierarchy process: Providing decision support for the offshoring decision by a US manufacturing company, Journal of Purchasing and Supply Management, Vol. 14, No. 2, 2008, pp. 100-111. [35] Seuring, S. and Muller, M., From a literature review to a conceptual framework for sustainable supply chain management, Journal of Cleaner Production, Vol. 16, No. 15, 2008, pp. 1699-1710. [36] Shinichiro, H. and Yujiro, S., Designing methods of human interface for supervisory control systems, Control Engineering Practice, Vol. 7, No. 11, 1999, pp. 1413-1419. [37] Stamatis, D. H., Failure mode and effect analysis: FMEA from theory to execution, ASQC Ouality Press, Wisconsin, 1995. [38] Stefan, M., Multiple-supplier inventory models in supply chain management: A review, International Journal of Production Economics, Vol. 81-82, No. 11, 2003, pp. 265-279. [39] Sterman, J., Business Dynamic, New York, McGraw-Hill, 2000. [40] Stock, J. R., Boyer, S. L. and Harrmon, T., Research opportunities in supply chain management, Journal of the Academy of Marketing Science, Vol. 38, No. 1, 2010, pp. 32-41. [41] Tamura, M., Nagata, H., and Akazawa, K., Extraction and systems analysis of factors that prevent safety and security by structural model, Proceedings of the 41st SICE Annual Conference, 2002, pp.1752-1759. [42] Tang, C. S., Perspectives in supply chain risk management, International Journal of Production Economics, Vol. 103, No.2, 2006, pp. 451-488. [43] Tang, O. and Musa, S. N., Identifying risk issues and research advancement in supply chain risk management, International Journal Production Economics, Vol. 133, No. 1, 2011, pp. 25-34. [44] Thun, J. H. and Hoenig, T., An empirical analysis of supply chain risk management in the German automotive industry, International Journal Production Economics, Vol. 131, No. 1, 2011, pp. 242-249. [45] Towill, D. R., Industrial dynamics modeling of supply chains, International Journal of Physical Distribution and Logistics, Vol. 26, No. 2, 1996, pp. 23-42. [46] Trkman, P. and McCormack, K., Supply chain risk in turbulent environment – A conceptual model for managing supply chain network risk, International Journal of Production Economics, Vol. 119, No. 2, 2009, pp. 247-258. [47] Tseng, M. L. and Lin, Y. H., Application of fuzzy DEMATEL to development a case and effect model of municipal solid waste management in Metro Manila, Earth and Environmental Science, Vol. 158, No. 1-4, 2009, pp. 519-533. [48] Tuncel, G. and Alpan, G., Risk assessment and management for supply chain network: A case study, Computer in Industry, Vol. 61, No. 3, 2010, pp. 250-259. [49] Wu, T., Blackhurst, J. and Chidambaram, V., A model inbound supply risk analysis, Computers in Industry, Vol. 57, No. 4, 2006, pp. 350-365. [50] Wu, W. W. and Lee, Y. T., Development global managers’ competencies using the fuzzy DEMATEL method, Expert Systems with Application, Vol. 32, No. 2, 2007, pp. 499-507. [51] Zsididen, G. A., A grounded definition of supply risk, Journal of Purchasing and Supply Management, Vol. 9, No. 5-9, 2003, pp. 217-224. [52] Zsidisin, G. A., Managerial perceptions of supply risk, The Journal of Supply Chain Management, Vol. 39, No. 1, 2003, pp. 14-25. [53] Law, Averill M. and Kelton, W. David, Simulation Modeling and Analysis 3/e, 2000. 中文部份 [1] 小野寺勝重,民90,實踐FMEA手法,張書文譯,台北:長榮國際股份有限公司。 [2] 孫宗瀛、楊英魁,民94,Fuzzy控制:理論、實作與理論,台北:全華圖書股份有限公司。 [3] Lin, C. J. and Wu W. W., A fuzzy extension of the DEMATEL method for group decision making, 第一屆作業研究學會學術研討會論文集,2004,台灣,台北科技大學。 [4] 胡雪琴,民92,企業問題複雜度之探討及量化研究-以DEMATEL為分析工具,私立中原大學企業管理研究所碩士論文。 [5] 林宗明,民94,管理問題因果複雜度分析模式建立之研究-以DEMATEL為方法論,私立中原大學企業管理研究所碩士論文。 [6] 紀岱玲,民94,供應商績效評估研究-結合ANP及DEMATEL之應用,國立政治大學資訊管理學系研究所碩士論文。 [7] 林盈君,民97,綠色供應鏈中風險評估之研究-以國內某主機板廠商為例,國立政治大學資訊管理學系研究所碩士論文。 [8] 李佳芳,民97,綠色供應鏈中供應商評選之研究,國立政治大學資訊管理學系研究所碩士論文。 [9] 張瓊云,民99,建構信任模型探討合作夥伴間資訊分享之意願,國立政治大學資訊管理學系研究所碩士論文。 [10] 謝長宏,民69,系統動態學-理論.方法與應用,台北:中興管理顧問公司。 [11] 陳璟鴻,民96,紡織業全球運籌績效指標架構,國立政治大學資訊管理學系研究所碩士論文。 [12] 潘俊宏,民94,一衡量供應鏈績效之整合性架構,國立中央大學工業管理研究所碩士論文。 描述 碩士
國立政治大學
資訊管理研究所
100356038
101資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100356038 資料類型 thesis dc.contributor.advisor 林我聰 zh_TW dc.contributor.author (Authors) 陳有慶 zh_TW dc.creator (作者) 陳有慶 zh_TW dc.date (日期) 2012 en_US dc.date.accessioned 2-Sep-2013 16:02:06 (UTC+8) - dc.date.available 2-Sep-2013 16:02:06 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2013 16:02:06 (UTC+8) - dc.identifier (Other Identifiers) G0100356038 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/59301 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理研究所 zh_TW dc.description (描述) 100356038 zh_TW dc.description (描述) 101 zh_TW dc.description.abstract (摘要) 產業分工與全球化因素影響,使企業與顧客、供應商分布在不同國家與地區,形成一個複雜與全球化的供應鏈網路,並導致整體供應鏈的風險大幅提升。企業如何做好供應鏈風險評估,已是目前聚焦的研究重點。那整體供應鏈流程包含採購、製造、配銷與回收階段,然而先前供應鏈風險管理研究上,大部分著重於單一階段進行討論,缺乏以整個供應鏈網路模式來進行研究,且假設風險因子之間為獨立、互不關連,也並未考慮整體供應鏈網路風險因子關聯特性,因而無法正確評估風險事件發生時所帶來的衝擊影響程度,自然無法進一步有效擬定出適當因應的風險管理策略。因此,本研究將針對「如何有效評估供應鏈風險程度」主要問題進行研究。 那針對此問題來進行研究,本研究提出一個整體供應鏈風險評估模式以有效評估供應鏈的風險程度。首先透過文獻探討確認整理出供應鏈的風險因子,並以SCOR模式(Supply Chain Operations Reference Model)的將風險因子歸類於採購(Source)、製造 (Make)、配銷(Deliver)與回收(Return)各階段內;接著藉由風險因子的發生率、嚴重度、難檢度,應用模糊失效模式與效應分析法(Fuzzy Failure Modes and Effects Analysis;Fuzzy FMEA)篩選出重要的風險因子;再使用模糊決策實驗室分析法(Fuzzy Decision Making Trial and Evaluation Laboratory;Fuzzy DEMATEL)建構出供應鏈流程中重要的風險因子間之關連性;由於風險因子間具有相互影響關係,本研究並採用分析網路程序法(Analytic Network Process;ANP)計算風險因子的影響權重。最後結合上述Fuzzy FMEA、Fuzzy DEMATEL與ANP方法,建立出整體供應鏈風險評估模式,並就由計算出風險因子對企業供應鏈的影響度,排列出風險因子的影響度大小,讓企業能夠知道風險因子的優先順序。這將協助企業有效進行供應鏈風險的評估,讓企業瞭解目前所處供應鏈潛在的風險,並據以研擬相關的因應策略,以降低其對供應鏈整體與成員的衝擊。 zh_TW dc.description.abstract (摘要) As part of the division of labor and globalization, enterprises, customers and suppliers are located in various countries and regions. Complex, globalized supply chain network have greatly increased total supply chain risks. Therefore, improving the management of risk associated with the supply chain has become important to many enterprises. And whole supply chain have include four stage, source, make, deliver and return, but previous supply chain risk management research has focused mainly on single-stage risk factors and assumed all risk factors to be mutually as independent and also not consider the whole supply chain risk factors associated characteristics. The lack of consideration for these relationships will lead to incorrect measuring risks and applying improper risk management strategies to solve these risks. Therefore this research concentrates on a topic: How to effectively assess risks. About the main issue, this research will propose an effectively risk assessment model. First, the research will consider whole supply chain risk factors from literature and then classify these factors into source stage’s factors, make stage’s factors, deliver stage’s factors and return stage’s factors in accordance with SCOR Model (Supply Chain Operations Reference Model) framework. Second, Fuzzy Failure Modes and Effects Analysis (Fuzzy FMEA), Fuzzy Decision Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) and Analytic Network Process (ANP) will be adopted and integrated to develop a supply chain risk assessment model. The results in this research will enable enterprises to determine in timely manner the effects of various risk events, enabling them to develop strategies to reduce their effect on the all members of the supply chain. en_US dc.description.tableofcontents 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目的 3 1.4 論文架構 3 第二章 文獻探討 4 2.1 供應鏈管理 4 2.2 SCOR模式 5 2.3 供應鏈風險管理 7 2.3.1 供應鏈風險管理流程 9 2.3.2 供應鏈風險因子分類 11 2.4 失效模式和效果分析(Failure Mode & effects Analysis, FMEA) 15 2.5 模糊理論(Fuzzy Theory) 18 2.6 決策實驗分析法(Decision Making Trial and Evaluation Laboratory, DEMATEL) 20 2.6.1 DEMATEL簡介與應用 20 2.6.2 DEMATEL架構與運算步驟 20 2.7 網路程序分析法(Analytic Network Process, ANP) 24 2.7.1 ANP簡介與應用 24 2.7.2 ANP架構與運算步驟 25 第三章 研究方法 28 3.1 研究流程與研究對象 28 3.1.1 研究流程 28 3.1.2 研究對象 30 3.2 Fuzzy FMEA方法之篩選出供應鏈流程中重要的風險因子 30 3.3 Fuzzy DEMATEL方法找出供應鏈風險因子的因果關係 33 3.4 ANP方法決定風險因子之影響權重 38 3.5 風險評估模式建立 44 第四章 風險評估模式建立之範例 45 4.1 供應鏈風險因子之確立與定義 45 4.2 利用Fuzzy FMEA方法篩選出重要的供應鏈風險因子 45 4.3 利用Fuzzy DEMATEL 方法找出重要風險因子間的因果關係 49 4.4 利用ANP方法找出重要風險因子間的相對影響權重 53 4.5 結合Fuzzy FMEA、Fuzzy DEMATEL方法建立供應鏈風險評估模式 57 4.6 風險因子因果關係 58 第五章 模式之模擬驗證 62 5.1 模擬系統設計 62 5.1.1 模擬假設 64 5.1.2 模擬模型結構 64 5.2 模擬過程與實驗設計 68 5.3 模式之效度分析 70 5.4 模擬之結果 72 第六章 結論與建議 76 6.1 研究結論 76 6.2 未來研究方向 77 參考文獻 78 附錄A Fuzzy FMEA 問卷 83 附錄B Fuzzy DEMATEL問卷 92 附錄C ANP問卷 100 附錄D 模擬中使用的參數與定義 108 zh_TW dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100356038 en_US dc.subject (關鍵詞) 供應鏈風險管理 zh_TW dc.subject (關鍵詞) 模糊理論 zh_TW dc.subject (關鍵詞) 失效模式與效應分析法 zh_TW dc.subject (關鍵詞) 決策實驗室分析法 zh_TW dc.subject (關鍵詞) 分析網路程序法 zh_TW dc.subject (關鍵詞) Supply Chain Risk Management en_US dc.subject (關鍵詞) Fuzzy Theory en_US dc.subject (關鍵詞) FMEA en_US dc.subject (關鍵詞) DEMATEL en_US dc.subject (關鍵詞) ANP en_US dc.title (題名) 供應鏈風險評估模式建立之研究-以資訊電子產業為例 zh_TW dc.title (題名) The Research on Developing a Risk Assessment Model - using Information and Electronics Industry as an example en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 英文部份 [1] Baloi, D. and Price, A. 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