研究生: |
吳宜嘉 Wu, Yi-Chia |
---|---|
論文名稱: |
基於模糊多目標規劃之網路資料包絡分析法評估汽車產業之供應網路績效 Evaluating the Performance of Automotive Industry Supply Network Using the Fuzzy Multiple Objective Programming Based Network DEA Method |
指導教授: | 呂有豐 |
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: | 中文 |
論文頁數: | 104 |
中文關鍵詞: | 網路資料包絡分析 、供應鏈網路 、績效評估 、汽車產業 |
英文關鍵詞: | Network Data Envelopment Analysis, Supply Network, Performance Evaluation, Automotive Industry |
DOI URL: | http://doi.org/10.6345/THE.NTNU.DIE.051.2018.E01 |
論文種類: | 學術論文 |
相關次數: | 點閱:123 下載:0 |
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汽車產業是一種高技術密集以及高附加價值之產業,由於近年來汽車逐漸走向智慧化、環保化、自動化,因其本身製造及組裝流程複雜,故供應商彼此之間的關聯性及影響層面很大。若只針對汽車供應鏈上的單一組織做績效評估,無法針對不同的影響因子提供完整的分析結果,而供應網路能夠考慮供應鏈中各個環節的問題。以資料包絡分析法導入投入、產出值評估績效,無法分析供應鏈網路的績效。本研究將以多目標規劃網路資料包絡分析法,並藉由模糊理論,考慮了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,分析無明確關聯的因子之間效率。本研究以我國汽車業包含零組件的生產、汽車製造與銷售之完整供應網路實證研究之可行性,並以產業整體觀點來評估供應鏈或供應網路績效,分析公司內部經營效率並找出影響效率的關鍵因素,解決汽車產業公司網路的投入、產出之資訊不完整揭露的問題。實證研究之結果,在汽車產業供應網路中,下游績效表現最好,再來是中游,最後是上游。研究結果可提供公司改善績效參考外,也可作為投資者在評估標的或選擇投資組合時的依據。
The automotive industry is a high technology intensive and high val-ue-added industries. In recent years, automobiles have gradually become in-telligent, environmental protection and automated. Due to its manufacturing and assembly process complex, the suppliers have a great correlation and in-fluence on each other. If the decision makers only evaluate the performance of a single organization in the automotive supply chain, they cannot provide a complete analysis result for different factors. The data envelopment analysis method is used to evaluate the performance of the input and output values. The performance of the whole supply chain network cannot be evaluated. This study based on the fuzzy multi-objective programming network data envel-opment analysis method, considering the structure of the organization or supply network, discussing the interaction and influence between the internal structure and the internal process of the system to evaluate the performance, and ana-lyzing the efficiency among the unrelated factors. This study is based on an empirical study of the feasibility of a complete supply network in automotive industry, including part’s production, automotive manufacturing and sales. This study evaluates the efficiency in terms of the overall view of supply chain or supply network, analysis internal production activities and understands the impact on output as a result of reduced productivity and to solve the supply network composed of input and output, the problem of incomplete information disclosing. The results of this study, downstream has the best efficiency value, then the midstream, and the worst is upstream in automotive supply network. The results can provide companies to improve their performance, and can also be used as a basis for investors to evaluate investment or portfolio.
References
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.
Anderson, D. L., Britt, F. F., & Favre, D. J. (2007). The 7 principles of supply chain management. Supply Chain Management Review, 11(3), 41-46.
Automotive Research & Testing Center (2018). Analysis and development trend of global car market. From: https://www.artc.org.tw/index.aspx
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.
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.
Boukherroub, T., Ruiz, A., Guinet, A., & Fondrevelle, J. (2015). An integrated approach for sustainable supply chain planning. Computers & Operations Research, 54, 180-194.
Bozarth, C. C., Warsing, D. P., Flynn, B. B., & Flynn, E. J. (2009). The impact of supply chain complexity on manufacturing plant performance. Journal of Operations Management, 27(1), 78-93.
Braziotis, C., Bourlakis, M., Rogers, H., & Tannock, J. (2013). Supply chains and supply networks: distinctions and overlaps. Supply Chain Management: An International Journal, 18(6), 644-652.
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.
Chen, T., & Gong, X. (2013). Performance evaluation of a supply chain network. Procedia Computer Science, 17, 1003-1009.
Christopher, M. (2016). Logistics & supply chain management. Pearson UK.
Custer, R. L., Scarcella, J. A., & Stewart, B. R. (1999). The Modified Delphi Technique-A Rotational Modification. Journal of vocational and technical education, 15(2), 50-58.
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.
Despotis, D. K., Koronakos, G., & Sotiros, D. (2015). A multi-objective programming approach to network DEA with an application to the assessment of the academic research activity. Procedia Computer Science, 55, 370-379.
Dzupire, N. C., & Nkansah-Gyekye, Y. (2014). A multi-stage supply chain network optimization using genetic algorithms. arXiv preprint arXiv:1408.0614.
Etebari, F., Abedzadeh, M., & Khoshalhan, F. (2011). Investigating Impact of Intelligent Agents in Improving Supply Chain Performance. International Journal of Industrial Engineering, 22(1).
Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: A frontier approach. Economics letters, 50(1), 65-70.
Fei, Z. (2018). The Analysis for the Scale and Efficiency of China’s Major Automotive Enterprises Based on DEA Model. Journal of Mathematics Research, 10(2), 129.
Huang, C. Y., & Kao, Y. S. (2015). UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP. Mathematical Problems in Engineering.
Long, Q. (2016). A novel research methodology for supply network collaboration management. Information Sciences, 331, 67-85.
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.
Jassbi, J., Seyedhosseini, S., & Pilevari, N. (2010). An adaptive neuro fuzzy inference system for supply chain agility evaluation. International Journal of Industrial Engineering & Production Research, 20(4), 187-196.
Jauhar, S. K., & Pant, M. (2017). Integrating DEA with DE and MODE for sustainable supplier selection. Journal of Computational Science, 21, 299-306.
Ji, A., Liu, H., Qiu, H.-j., & Lin, H. (2015). Data envelopment analysis with interactive variables. Management Decision, 53(10), 2390-2406.
Jones, J., & Hunter, D. (1995). Consensus methods for medical and health services research. BMJ: British Medical Journal, 311(7001), 376.
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.
Kao, C. (2014). Network data envelopment analysis: A review. European journal of operational research, 239 (1), 1-16.
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.
Brooks, K. W. (1979). Delphi technique: Expanding applications. North Central Association Quarterly, 53(3), 377-85.
Malhan, S. (2015). Impact Of Demographic And Entrepreneur’s Profile On Level Of Supply Chain Management Ractices In Selected Small Scale And Medium Entreprises (Smes) In Retail Sector. Supply Chain Management 2.
Nandy, Debaprosanna. (2011). Efficiency study of Indian automobile companies using DEA technique: A case study of select companies. IUP Journal of Operations Management 10 (4), 39.
Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management science, 9(3), 458-467.
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.
Pao, C. P (2013). Data envelopment analysis of performance evaluation. (1st ed., pp. 9-12).Taipei, Taiwan, ROC: Wu-Nan.
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.
Pelliccione, P., Knauss, E., Heldal, R., Ågren, S. M., Mallozzi, P., Alminger, A., & Borgentun, D. (2017). Automotive architecture framework: The experience of volvo cars. Journal of Systems Architecture, 77, 83-100.
Rahman, N., Habib, A., Alam, Z., Zoarder, A., & Haque, M. (2016). Route optimization in Multi Stage Supply Chain Network. Imperial Journal of Interdisciplinary Research, 2(7).
Stadtler, H., Kilger, C., & Meyr, H. (2015). Supply Chain Management and Advanced Planning: Springer Berlin Heidelberg.
Sugimori, Y., Kusunoki, K., Cho, F., & Uchikawa, S. (1977). Toyota production system and kanban system materialization of just-in-time and respect-for-human system. The International Journal of Production Research, 15(6), 553-564.
Tajbakhsh, A., & Hassini, E. (2015). A data envelopment analysis approach to evaluate sustainability in supply chain networks. Journal of Cleaner Production, 105, 74-85.
Tan, Y., Zhang, Y., & Khodaverdi, R. (2017). Service performance evaluation using data envelopment analysis and balance scorecard approach: an application to automotive industry. Annals of Operations Research, 248(1-2), 449-470.
Tavana, M., Khalili-Damghani, K., Arteaga, F. J. S., Mahmoudi, R., & Hafezalkotob, A. (2018). Efficiency decomposition and measurement in two-stage fuzzy DEA models using a bargaining game approach. Computers & Industrial Engineering, 118, 394-408.
Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42(1), 124-131.
Tzeng, G.-H., & Huang, J.-J. (2016). Fuzzy multiple objective decision making (1st ed., pp. 83-96). New York, NY: Chapman and Hall/CRC.
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.
Yang, C., & Liu, H. M. (2012). Managerial efficiency in Taiwan bank branches: A network DEA. Economic Modelling, 29(2), 450-461.
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.
Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy sets and systems, 1(1), 45-55.