簡易檢索 / 詳目顯示

研究生: 黃安立
Huang, An-Li
論文名稱: 以基於DDANP-mV之品質機能展開法定義汽車零組件生產之精實策略
The DDANP-mV Based Quality Function Deployment for Defining the Lean Production Strategy of Automobile Components
指導教授: 呂有豐
Lue, Yeou-Feng
口試委員: 呂有豐
Lue, Yeou-Feng
羅乃維
Lo, Nai-Wei
黃日鉦
Huang, Jih-Jeng
口試日期: 2021/08/07
學位類別: 碩士
Master
系所名稱: 工業教育學系科技應用管理碩士在職專班
Department of Industrial Education_Continuing Education Master's Program of Technological Management
論文出版年: 2021
畢業學年度: 109
語文別: 英文
論文頁數: 154
中文關鍵詞: 精實生產多準則決策分析以決策實驗室法為基礎之網路流程折衷排序法品質機能展開萃思法
英文關鍵詞: Lean Production, Multi-Criteria Decision-Making (MCDM), Decision Making Trial and Evaluation Laboratory-Based Analytic Network Process (DANP), VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR), Quality Function Deployment (QFD), TRIZ
研究方法: 多準則決策分析法
DOI URL: http://doi.org/10.6345/NTNU202101769
論文種類: 學術論文
相關次數: 點閱:60下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 汽車零組件產業為汽車工業發展之重要推手,近年來,由於需求波動劇烈,不易預測,而且多數訂單少量多樣,客戶且不斷嘗試壓低採購價格,對零組件廠商造成極大壓力,因此需要異於傳統的生產方式。為因應潮流,精實生產逐漸盛行,以期使用最低成本,達到最佳品質。雖然將精實生產方式導入零組件生產線,極為重要,但是相關研究甚少,引此本研究擬定義一基於多準則決策模式之分析架構,整合品質機能展開法,分析客戶需要,及其與零件規格之關聯性之後,以萃思法推衍適合之發明策略,改善產線。
    首先,本研究依據實際狀況,歸納客戶需要,並且將差速器之規格,導入品質屋。其次,本研究以決策實驗室法 (Decision Making Trial And Evaluation Laboratory,DEMATEL)推衍客戶需要間之影響關係,並以決策實驗室法為基礎之網路流程 (DEMATEL based Analytic Network Process,DANP),計算各需要之權重,其後,以折衷排序法 (VlseKriterijuska Optimizacija I Komoromisno Resenje,VIKOR),邀集專家,計算需要優先改善之規格參數。最後以萃思法,針對衝突嚴重之優先參數,訂定精實生產策略。
    本研究以台灣某上市汽車零組件公司之差速器為個案,邀集專家,針對客戶臨時變更訂單,要求大幅增加產量,並降低採購單價之特殊情境,以所發展之分析架構,訂定精實策略。本研究將精實生產原則應用於汽車差速器生產線,以達提高產能並降低生產成本且能維持生產良率,並達成客戶需求。以本決策架構選出,適合個案分析中差速器之法精實策略包括「全面預防維護」 (Total Preventive Maintenance, TPM)、「標準作業」(Standard Work)和面向製造和裝配的設計 (Design for Manufacturing and Assembly, DFMA)為最適合用於個案差速器之精實策略。研究結果顯示,混合多準則分析模型用於精實生產的可行性。完整驗證之架構,也可作為其他產品訂定精實生產策略之用。

    The automotive component industry is an important driving force for the development of the automotive industry. In recent years, due to fluctuations in demand and small and diverse orders of most automotive components, accurate predictions of demands have been difficult. Customers are constantly trying to negotiate for lower purchase prices, which has caused great pressure on component manufacturers. Therefore, a different approach from the traditional ways of manufacturing automotive components is required. In response to the trend, lean production strategies have widely been adopted to achieve the best quality with the lowest cost. Although the introduction of lean production methods into the production lines of automotive components is extremely important, there are few related studies. Therefore, this study aims to define an analysis framework based on the quality function deployment (QFD) method with multi-criteria decision-making frameworks. Based on customer needs and the parameters of the automotive component, appropriate lean manufacturing strategies can be derived using the TRIZ method.
    First, this research summarizes the needs of customers based on an actual scenario, and it introduces the specifications of differential gear into the House of Quality (HOQ). Second, this research uses the Decision Making Trial and Evaluation Laboratory (DEMATEL) to derive the influence relationship between customer needs, and it uses the MATEL-based Analytic Network Process (DANP) as the basis to derive the weight versus each need. Then, the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method is introduced to derive the parameters that should be improved first. In the end, lean production strategies are formulated according to the priority parameters with serious conflicts using the TRIZ method.
    This research takes the differential gear of a listed automobile component company in Taiwan as the empirical case. Experts are invited to provide opinions regarding the special scenario of temporarily changing orders, greatly improved capacity, and lowering the unit cost of purchasing. The proposed analytic framework is used for decision-making. This research applies the principle of lean production to the production line of automobile differentials to increase production capacity and reduce production costs, maintain production yield, and meet customer needs. Based on the analytic results, differential gear total preventive maintenance (TPM) and standard work and design for manufacturing and assembly (DFMA) are the most suitable lean strategies for the empirical case. The research results demonstrate the feasibility of the proposed hybrid multi-criteria analysis model for lean production. The fully verified analytic framework can also be used for formulating lean production strategies of other components.

    Chapter 1 Introduction 1 1.1 Research Backgrounds 1 1.2 Research Motivations 3 1.3 Research Scope and Framework 4 1.4 Research Limitation 6 1.5 Thesis Structure 7 Chapter 2 Literature review 9 2.1 Lean Production 9 2.2 QFD 16 2.3 The Combined QFD and Lean Production 20 Chapter 3 Research Method 23 3.1 Modified Delphi Method 23 3.2 DANP Method 27 3.3 VIKOR Method 28 3.4 TIRZ Method 30 Chapter 4 Empirical Study 33 4.1 Industry Background and Research Problem Description 33 4.2 Confirmation the Criteria by Modified Delphi Method 34 4.3 The Causal Relationships by the DEMATEL 42 4.4 The Criteria Weight Derivations by the DANP 71 4.5 Calculating the Compromise Ranking by the VIKOR 85 4.6 Analyze the HOQ HOWs Matrix Conflict by the TRIZ 90 4.7 QFD Model 101 Chapter 5 Discussion 105 5.1 Results of DEMATEL 105 5.2 Results of DANP 112 5.3 Results of VIKOR 116 5.4 Results of TRIZ 119 Chapter 6 Conclusion 124 References 125 Appendix 139

    Alagaraja, M., & Egan, T. (2013). The strategic value of HRD in lean strategy implementation. Human Resource Development Quarterly, 24(1), 1-27.
    Alaskari, O., Ahmad, M. M., & Pinedo-Cuenca, R. (2016). Development of a
    methodology to assist manufacturing SMEs in the selection of
    appropriate lean tools. International Journal of Lean Six Sigma, 7(1), pp. 62-84.
    Altshuller, G. S. (1984). Creativity as an exact science: The theory of the solution of inventive problems. New York, Gordon and Breach Science Publishers.
    Altshuller, G. S. (1999). The innovation algorithm: TRIZ, systematic innovation and technical creativity. MA, Technical innovation center.
    AMS2175. (2010). Aerospace material specification-Casting, Classification and inspection. PA, SAE international.
    Anglin, G.L. (1991), Instructional Technology: past, present and future, Libraries unlimited, Inc, Englewood, Co, pp. 259-266
    ASME. (2018). Dimensioning and Tolerancing, ASME Y14.5-2018. New York, American Society of Mechanical Engineers.
    ASTM Standard. (2017). Standard Digital Reference Images for Inspection of Aluminum Castings. PA, ASTM International.
    Azizi, A., Aikhuele, D. O., & Souleman, F. S. (2015). A fuzzy TOPSIS model to rank automotive suppliers. Procedia Manufacturing, 2, 159-164.
    Barnabè, F., & Giorgino, M. C. (2017). Practicing Lean strategy: Hoshin Kanri and X-Matrix in a healthcare-centered simulation. The TQM Journal, 29(4), pp. 590-609.
    Benner, M., Linnemann, A., Jongen, W., & Folstar, P. (2003). Quality Function Deployment (QFD)—can it be used to develop food products? Food Quality and Preference, 14(4), 327-339.
    Bengisu, M., & Nekhili, R. (2006). Forecasting emerging technologies with the aid of science and technology databases. Technological Forecasting and Social Change, 73(7), 835-844.
    Bhuvanesh Kumar, M., & Parameshwaran, R. (2018). Fuzzy integrated QFD, FMEA framework for the selection of lean tools in a manufacturing organisation. Production Planning & Control, 29(5), 403-417.
    Browning, T. R., & Heath, R. D. (2009). Reconceptualizing the effects of lean on production costs with evidence from the F-22 program. Journal of operations management, 27(1), 23-44.
    Büyüközkan, G., & Çifçi, G. (2012). A new incomplete preference relations based approach to quality function deployment. Information Sciences, 206, 30-41.
    Carnevalli, J. A., de Sousa, J. E. R., de Benedicto, S. C., Medeiros, C. G. A., & Georges, M. R. R. (2018). Use of QFD to define requisites necessaries for the application of the modularity strategy in the first level suppliers of the automotive sector/Uso do QFD para definir requisitos necessaries para a aplicacao da estrategia da modularidade nos fornecedores de primeiro nivel do setor automotivo. Revista Exacta, 16(4), 149-164.
    Cherrafi, A., Elfezazi, S., Chiarini, A., Mokhlis, A., & Benhida, K. (2016). The integration of lean manufacturing, Six Sigma and sustainability: A literature review and future research directions for developing a specific model. Journal of Cleaner Production, 139, 828-846.
    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.
    Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458-467.
    Delano G., Parnell G. S., Smith C. & Vance M., (2000). Quality function deployment and decision analysis: A R&D case study, International Journal of Operations & Production Management, 20(5), pp. 591-609.
    Delbecq, A. L., Van de Ven, A. H., & Gustafson, D. H. (1975). Group Techniques for Program Planning. Glenview, Scott, Foresman, and Co.
    Delgado‐Hernandez, D. J., Bampton, K. E., & Aspinwall, E. (2007). Quality function deployment in construction. Construction Management and Economics, 25(6), 597-609.
    Devadasan, S.R., Kathiravan, N. and Thirunavukkarasu, V. (2006), “Theory and practice of total quality function deployment: a perspective from a traditional pump-manufacturing environment”, The TQM Magazine, 18(2), pp. 143-161.
    Dikmen, I., Birgonul, M. T., & Kiziltas, S. (2005). Strategic use of quality function deployment (QFD) in the construction industry. Building and environment, 40(2), 245-255.
    El-Khalil, R. (2018). Classification, purpose, enablers of lean dimensions at automotive manufacturing industry.
    Emiliani, B. (2008). Practical lean leadership: A strategic leadership guide for executives. Wethersfield, Center for Lean Business Management.
    Freimer, M. and Yu, P. (1976). Some New Results on Compromise Solutions for Group Decision Problems. Management Science, 22(6), 688-693.
    Gabus, A. and Fontela, E. (1972). World Problems, an Invitation to Further Thought within the Framework of Dematel. Geneva, Battelle Geneva Research Centre.
    Gonçales Filho, M., Campos, F. C. d., & Assumpção, M. R. P. (2016). Revisão sistemática da literatura com análise bibliométrica sobre estratégia e Manufatura Enxuta em segmentos da indústria. Gestão & Produção, 23(2), 408-418.
    Green, K. C., Armstrong, J. S., & Graefe, A. (2007). Methods to elicit forecasts from groups: Delphi and prediction markets compared. Foresight—The International Journal of Applied Forecasting, 8(2007), pp. 17-20.
    Gupta, S., & Jain, S. K. (2013). A literature review of lean manufacturing. International Journal of Management Science and Engineering Management, 8(4), 241-249.
    Harikumar, P., & Saleeshya, P. (2019). Integrating FMEA, QFD and Lean for Risk management in hospitals. IOP Conference Series: Materials Science and Engineerin, 577(1), 12-40.
    Harris, R., Harris, C., & Wilson, E. (2003). Making materials flow: a lean material-handling guide for operations, production-control, and engineering professionals. MA, Lean Enterprise Institute.
    Hauser, J. R., & Clausing, D. (1988). The house of quality, 34(3), 63–73
    Hicks, B. J. (2007). Lean information management: Understanding and eliminating waste. International journal of information management, 27(4), 233-249.
    Hwang, C.-L. and Yoon, K. (1981). Multiple Attribute Decision Making. London, Sage Publications.
    Herrmann, A., Huber, F., Algesheime, R., & Tomczak, T. (2006). An empirical study of quality function deployment on company performance. International Journal of Quality & Reliability Management, 23(4), pp. 345-366.
    Huang, C.-Y., Chung, P.-H., Shyu, J. Z., Ho, Y.-H., Wu, C.-H., Lee, M.-C., & Wu, M.-J. (2018). Evaluation and selection of materials for particulate matter MEMS sensors by using hybrid MCDM methods. Sustainability, 10(10), 3451.
    Jacobs, J. M. (1996). Essential Assessment Criteria for Physical Education Teacher Education Programs: A Delphi study. Morgantown, West Virginia University,
    Karsak, E. E., Sozer, S., & Alptekin, S. E. (2003). Product planning in quality function deployment using a combined analytic network process and goal programming approach. Computers & industrial engineering, 44(1), 171-190.
    Kerlinger, F. N. (1973). Foundations of behavioral research. NewYork, Holt, Rinehart, and Winston, Inc.
    Koushki, P. A., & Kartam, N. (2004). Impact of construction materials on project time and cost in Kuwait. Engineering, Construction and Architectural Management, 11(2), pp. 126-132 .
    Kumar, A., Antony, J., & Dhakar, T. S (2006) Integrating quality function deployment and benchmarking to achieve greater profitability. Revista Benchmarking, 13(3), 290-311.
    Kumar, G. V., & Robinson, Y. (2016). A Cost Based Correlated Logical Methodology for Choosing Optimum Lean Tools and Techniques based on Lean Survey in Garment Manufacturing Industries. Asian Journal of Research in Social Sciences and Humanities, 6(5), 576-588.
    Lager, T. (2005). The industrial usability of quality function deployment: a literature review and synthesis on a meta‐level. R&D Management, 35(4), 409-426.
    Lee, S., & Ko, A. S. O. (2000). Building balanced scorecard with SWOT analysis, and implementing “Sun Tzu’s The Art of Business Management Strategies” on QFD methodology. Managerial Auditing Journal, 15(1/2), pp. 68-76.
    Lee, C. C., & Ou-Yang, C. (2009). A neural networks approach for forecasting the supplier’s bid prices in supplier selection negotiation process. Expert systems with Applications, 36(2), 2961-2970.
    Liang, G.-S. (2010). Applying fuzzy quality function deployment to identify service management requirements for customer quality needs. Quality & Quantity: International Journal of Methodology, 44, 47-57.
    Lowe, A., & Ridgway, K. (2000). UK user's guide to quality function deployment. Engineering Management Journal, 10(3), 147-155.
    Ludwig, B. G. (1994). Internationalizing Extension: An exploration of the characteristics evident in a state university Extension system that achieves internationalization. Ohio, The Ohio State University.
    Martins, A., & Aspinwall, E. M. (2001). Quality function deployment: An empirical study in the UK. Total Quality Management, 12(5), 575-588.
    Meesapawong, P., Rezgui, Y., & Li, H. (2014). Planning innovation orientation in public research and development organizations: using a combined Delphi and analytic hierarchy process approach. Technological Forecasting and Social Change, 87, 245-256.
    Morrissey, B., & Pittaway, L. (2004). A study of procurement behaviour in small firms. Journal of small business and enterprise development. 11(2), pp. 254-262.
    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.
    Nguyen, R. T., Imholte, D. D., Rios, O. R., Weiss, D., Sims, Z., Stromme, E., & McCall, S. K. (2019). Anticipating impacts of introducing aluminum-cerium alloys into the United States automotive market. Resources, Conservation and Recycling, 144, 340-349.
    Ohno, T. (1982). How the Toyota production system was created. Japanese Economic Studies, 10(4), 83-101.
    Onar, S. Ç., Büyüközkan, G., Öztayşi, B., & Kahraman, C. (2016). A new hesitant fuzzy QFD approach: an application to computer workstation selection. Applied Soft Computing, 46, 1-16.
    Opricovic, S. (1998). Multicriteria Optimization of Civil Engineering Systems. Faculty of Civil Engineering, Belgrade. 2(1), 5-21.
    Opricovic, S. and Tzeng, G.H. (2003). Fuzzy Multicriteria Model for Post Earthquake Land-Use Planning. Natural Hazards Review. 4(2), 59-64.
    Palazzo, J., & Geyer, R. (2019). Consequential life cycle assessment of automotive material substitution: replacing steel with aluminum in production of north American vehicles. Environmental Impact Assessment Review, 75, 47-58.
    Partovi, F. Y., & Corredoira, R. A. (2002). Quality function deployment for the good of soccer. European journal of operational research, 137(3), 642-656.
    Pavnaskar, S. J., Gershenson, J. K., & Jambekar, A. B. (2003). Classification scheme for lean manufacturing tools. International Journal of Production Research, 41(13), 3075-3090.
    Peter, K., & Lanza, G. (2011). Company-specific quantitative evaluation of lean production methods. Production Engineering, 5(1), 81-87.
    Phillips-Wren, G. (2010). Advances in Intelligent Decision Technologies: Proceedings of the Second Kes International Symposium Idt 2010 (Vol. 4). MD, Springer Science & Business Media.
    Psomas, E., & Antony, J. (2019). Research gaps in Lean manufacturing: a systematic literature review. International Journal of Quality & Reliability Management, 36(5), pp. 815-839.
    Ramakrishnan, V., & Nallusamy, S. (2017). Optimization of production process and machining time in CNC cell through the execution of different lean tools. International Journal of Applied Engineering Research, 12(23), 13295-13302.
    Ramasamy, N. R., & Selladurai, V. (2004). Fuzzy logic approach to prioritise engineering characteristics in quality function deployment (FL‐QFD). International Journal of Quality & Reliability Management, 21(9), pp. 1012-1023.
    Rother, M., & Shook, J. (2003). Learning to see: value stream mapping to add value and eliminate muda. Cambridge, MA: Lean Enterprise Institute.
    Rossouw, A., Hacker, M., & de Vries, M. J. (2011). Concepts and contexts in engineering and technology education: An international and interdisciplinary Delphi study. International Journal of Technology and Design Education, 21(4), 409-424.
    Saaty, T.L. (1996). Decision Making with Dependence and Feedback: The Analytic Network Process. P.A, RWS Publication.
    Scott, D. G., Washer, B. A., & Wright, M. D. (2006). A Delphi Study to Identify Recommended Biotechnology Competencies for First-Year/Initially Certified Technology Education Teachers. Journal of Technology Education, 17(2), 43-55.
    Shah, S. R., & Ganji, E. N. (2017). Lean production and supply chain innovation in baked foods supplier to improve performance. British Food Journal, 119(11), 2421-2447.
    Sheu, D. D., & Hou, C. T. (2013). TRIZ-based trimming for process-machine improvements: Slit-valve innovative redesign. Computers & Industrial Engineering, 66(3), 555-566.
    Shih, B. Y., Chen, C. Y., & Li, C. E. (2013). The exploration of the mobile mandarin learning system by the application of TRIZ theory. Computer Applications in Engineering Education, 21(2), 343-348.
    Singh, R., Kumar, S., Choudhury, A., & Tiwari, M. (2006). Lean tool selection
    in a die casting unit: a fuzzy-based decision support heuristic.
    International journal of production research, 44(7), 1399-1429.
    Singh, R. K., Choudhury, A. K., Tiwari, M. K., & Maull, R. S. (2006). An integrated fuzzy-based decision support system for the selection of lean tools: a case study from the steel industry. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 220(10), 1735-1749.
    Sriparavastu, L., & Gupta, T. (1997). An empirical study of just‐in‐time and total quality management principles implementation in manufacturing firms in the USA. International Journal of Operations & production management, 17(12), pp. 1215-1232.
    Sutrisno, A., Vanany, I., Gunawan, I., & Asjad, M. (2018). Lean waste classification model to support the sustainable operational practice. IOP Conference Series: Materials Science and Engineering, 337(1), pp. 12-67.
    Taylor, M., & Quayle, E. (2003). Child pornography: An internet crime. London, Psychology Press.
    Tendayi, T. G., & Fourie, C. J. (2013). The combined AHP-QFD approach and its use in lean maintenance. Stellenbosch, Southern African Institute of Industrial Engineering.
    Thanki, S., Govindan, K., & Thakkar, J. (2016). An investigation on lean-green implementation practices in Indian SMEs using analytical hierarchy process (AHP) approach. Journal of Cleaner Production, 135, 284-298.
    Tzeng, G.-H., & Huang, C.-Y. (2012). Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems. Annals of Operations Research, 197(1), 159-190.
    Vinodh, S., Shivraman, K., & Viswesh, S. (2012). AHP‐based lean concept selection in a manufacturing organization. Journal of Manufacturing Technology Management, 23(1), pp. 124-136.
    Weaver, W. T. (1971). The Delphi forecasting method. Phi Delta Kappan, 52 (5), 267-273.
    Winkler, J., Kuklinski, C. P. J.-W., & Moser, R. (2015). Decision making in emerging markets: The Delphi approach's contribution to coping with uncertainty and equivocality. Journal of Business Research, 68(5), 1118-1126.
    Wolniak, R. (2018). The use of QFD method advantages and limitation. Production Engineering Archives, 18, pp. 14-17.
    Womack, J., Jones, D., & Roos, D. (1990). The Machine that Changed the World: The Story of Lean Production. New York, HarperCollins Publishers.
    Womack, J. P., & Jones, D. T. (1997). Lean thinking—banish waste and create wealth in your corporation. Journal of the Operational Research Society, 48(11), 1148-1148.
    Womack, J. P., & Jones, D. T. (2003). Banish waste and create wealth in your corporation. Retrieved from http://www. kvimis. co. in/sites/kvimis. co. in/files/ebook_attachments/James.
    Yang, C. L., Yuan, B. J., & Huang, C. Y. (2015). Key determinant derivations for information technology disaster recovery site selection by the multi-criterion decision making method. Sustainability, 7(5), 6149-6188.
    Yang, C. L., Huang, C. Y., Kao, Y. S., & Tasi, Y. L. (2017). Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data. Eurasia Journal of Mathematics. Science and Technology Education, 13(8), 4553-4589.
    Yoon, K. (1987). A Reconciliation among Discrete Compromise Solutions. Journal of the Operational Research Society, 38(3), 277-286.
    Yu, P. L. (1973). A Class of Solutions for Group Decision Problems. Management Science, 19(8), 936-946.
    Zeleny, M. and Cochrane, J.L. (1982). Multiple Criteria Decision Making. New York, McGraw-Hill.

    無法下載圖示 本全文未授權公開
    QR CODE