臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：考慮目標市場顧客滿意度之品質機能展開數學模式
作者：陳政年
作者(外文)：Cheng-NienChen
校院名稱：國立成功大學
系所名稱：工業與資訊管理學系
指導教授：陳梁軒
學位類別：博士
出版日期：2015
主題關鍵詞：品質機能展開；新產品開發；品質設計；顧客滿意度；Quality function deployment (QFD)；New Product Development (NPD)；Quality design；Customer satisfaction
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:1
顧客滿意度是反應品質程度的重要因素之一，如何在競爭市場中去迎合顧客需求是確保顧客滿意度的重要方法之一。在諸多的品質設計改善技術中，品質機能展開是一種以顧客需求為導向的概念，提供有系統的程序，將顧客的需求轉換成技術的需求，以確保顧客的最大滿意度。在相關的品質機能展開研究文獻中，以數學規劃法來尋求最大化顧客的滿意度，是其中一種重要的數量研究方法。但歸納現行品質機能展開相關的數學規劃模式所採用的邏輯，主要以資源限制的思考邏輯，在有限的預算或資源下，尋求產品可獲最大化顧客戶滿意度為其目標。因此鮮少從競爭市場的觀點出發，以達成必要的目標顧客滿意度之前提下，讓產品在眾多競爭者中具有競爭力，依此觀點規劃出符合顧客品質需求的最小化產品總設計成本。在現有文獻中，有兩種闡述品質與滿意度的概念，其一為Kano (1984) 所提出的品質與滿意度概念，另一為 Anderson 與Sullivan (1993) 所提出的品質與滿意度的概念。本論文試圖從此不同的兩個品質與滿意度概念為基礎，以品質機能展開做為品質設計平台，運用數學規劃方法，提出從市場競爭力策略思維下，考慮新產品的目標顧客滿意度及其所需相對應的產品品質水準需求下，達成最小化總設計成本之數學規劃模式。然而，在使用數學規劃法建構品質機能展開的數學規劃模式時，對於品質機能展開的資訊整合模式之應用，我們發現最常被相關研究文獻所引用的Wasserman (1993) 正規化模式，有其適用範疇的不足性以及資訊整合後的不合理性等的疑慮。因此本論文延伸出Wasserman 正規化模式的改善研究，以期能提供更一般性及合理性的品質機能展開的資訊整合模式。在研究的最後將分別提供研究案例來顯示改善後的正規化模式有其應用的一般性與整合結果的合理性；以及在不同的品質與滿意度概念下，不同的數學規劃模式所產生的品質設計結果，並做出分析比較與結論。
Customer satisfaction is an important factor related to quality performance. Catering to customer needs is one of the important ways to boost customer satisfaction in competitive markets. Among numerous quality design/improvement techniques, the concept of quality function deployment (QFD) is made customer orientation-based by providing logical processes converting customer needs into technical responses to ensure customer satisfaction. The existing QFD-related literature using quantitative approaches mainly emphasizes the viewpoint of resource limitation by achieving maximum customer satisfaction under a budgetary limit via mathematical models. However, from a marketing perspective, determining a method by which to afford adequate competitive advantage by developing target customer satisfaction for a newly designed product has seldom been addressed. Hence, this study is intended to develop new mathematical approaches to implement this market perspective. The developed approaches are based on Kano’s satisfaction concept (1984) as well as Anderson and Sullivan’s satisfaction concept (1993), respectively. Thus, under the premise of considering target customer satisfaction for a target market segment, we develop mathematical programming models by formulating the degree of target customer satisfaction as a required constraint. In addition, during the developing of the mathematical models, we find that a widely applied procedure for information integration, the Wasserman normalization model (1993), is deficient and irrational for use in general applications. Hence, we also extend the purpose of this study to improving the weaknesses of the Wasserman normalization model. Numerical examples are illustrated to show the applicability of the improved normalization models and the proposed mathematical models according to the different design concepts. A numerical comparison is also provides to investigate the design performance.
Anderson, E. W. ＆ M. W. Sullivan (1993), “The antecedents and consequences of customer satisfaction for firms, Marketing Science, 12 (2) pp.125~143.
Askin, R. G. ＆ D. W. Dawson (2000), “Maximizing customer satisfaction by optimal specification of engineering characteristics, IIE Transactions, 32 (1) pp.9~20.
Bode, J. ＆ R. Y. K. Fung (1998), “Cost engineering with quality function deployment, Computers ＆ Industrial Engineering, 35 (3-4) pp.587~590.
Braglia, M., G. Fantoni ＆ M. Frosolini (2007), “The house of reliability, International Journal of Quality and Reliability Management, 24 (4) pp.420~440.
Chan, L. -K. ＆ M. -L. Wu (1998), “Prioritizing the technical measures in quality function deployment, Quality Engineering, 10 (3) pp.467~479.
Chan, L. -K. ＆ M. -L. Wu (2002), “Quality function deployment: A literature review, European Journal of Operational Research, 143 (3) pp.463~497.
Chen, L. -H. ＆ M. -C. Weng (2003), “A fuzzy model for exploiting quality function deployment, Mathematical and Computer Modelling, 38 (5-6) pp.559~570.
Chen, L. -H. ＆ M. -C. Weng (2006), “An evaluation approach to engineering design in QFD process using fuzzy goal programming models, European Journal of Operational Research, 172 (1) pp.230~248.
Chen, L. -H. ＆ W. -C. Ko (2008), “A fuzzy nonlinear model for quality function deployment considering Kano’s concept, Mathematical and Computer Modelling, 48 (3-4) pp.581~593.
Chen, L. -H. ＆ W. -C. Ko (2009a), “Fuzzy approaches to quality function deployment for new product design, Fuzzy Sets and Systems, 160 (18) pp.2620~2639.
Chen, L. -H. ＆ W. -C. Ko (2009b), “Fuzzy linear programming models for new product design using QFD with FMEA, Applied Mathematical Modelling, 33 (2) pp.633~647.
Chiou, W. -C., H. -W. Kuo ＆ I. -Y. Lu (1999), “A technology oriented productivity measurement model, International Journal of Production Economics, 60-61 pp.69~77.
Chuang, P. -T. (2002), “A QFD approach for distribution’s location model, International Journal of Quality ＆ Reliability Management, 19 (8/9) pp.1037~1054.
Cohen, L. (1995), Quality Function Deployment: How to make QFD work for you, Reading, Massachusetts: Addison-Wesley
Delice, E. K. ＆ Z. Güngör (2009), “A new mixed integer linear programming model for product development using quality function deployment, Computers ＆ Industrial Engineering, 57 (3) pp.906~912.
Delice, E. K. ＆ Z. Güngör (2011), “A mixed integer goal programming model for discrete values of design requirements in QFD, International Journal of Production Research, 49 (10) pp.2941~2957.
Erevelles, S. ＆ C. Leavitt (1992), “A comparison of current models of consumer satisfaction/dissatisfaction, Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 5 pp.104~114
Franceschini, F. ＆ S. Rossetto (1995), “QFD: The problem of comparing technical/engineering design requirements, Research in Engineering Design, 7 pp.270~278.
Franceschini, F. ＆ S. Rossetto (2002), “QFD: an interactive algorithm for the prioritization of product’s technical design characteristics, Integrated Manufacturing Systems, 13 (1) pp.69~75.
Fung, R. Y. K., J. Tang, Y. Tu ＆ D. Wang (2002), “Product design resources optimization using a non-linear fuzzy quality function deployment model, International Journal of Production Research, 40 (3) pp.585~599.
Han, C. H., J. K. Kim, S. H. Choi ＆ S. H. Kim (1998), “Determination of information system development priority using quality function deployment, Computers ＆ Industrial Engineering, 35 (1-2) pp.241~244.
Han, C. H., J. K. Kim ＆ S. H. Choi (2004), “Prioritizing engineering characteristics in quality function deployment with incomplete information: A linear partial ordering approach, International Journal of Production Economics, 91 (3) pp.235~249.
Iranmanesh, H. ＆ V. Thomson (2008), “Competitive advantage by adjusting design characteristics to satisfy cost target, International Journal of Production Economics, 115 (1) pp.64~71.
Kano, N., N. Seraku, F. Takahashi ＆ S. Tsuji (1984), “Attractive quality and must-be quality, Hinshitsu: The Journal of the Japanese Society for Quality Control, 14(2) pp.39~48.
Karsak, E. E. (2004), “Fuzzy multiple objective decision making approach to prioritize design requirements in quality function deployment, International Journal of Production Research, 42 (18) pp.3957~3974.
Kim, K. -J. (1997), “Determining optimal design characteristic levels in quality function deployment, Quality Engineering, 10 (2) pp.295~307.
Kwong, C. K., Y. Chen, H. Bai ＆ D. S. K. Chan (2007), “A methodology of determining aggregated importance of engineering characteristics in QFD, Computers ＆ Industrial Engineering, 53 (4) pp.667~679.
Lai, X., M. Xie ＆ K. C. Tan (2005), “Dynamic programming for QFD optimization, Quality and Reliability Engineering International, 21 (8) pp.769~780.
Liu, S. -T. (2005), “Rating design requirements in fuzzy quality function deployment via a mathematical programming approach, International Journal of Production Research, 43 (3) pp.497~513.
Lyman, D. (1990), “Deployment normalization, Transactions from the second symposium on Quality Function Deployment, a conference co-sponsored by the Automotive Division of the American Society for Quality Control, the American Supplier Institute, Dearborn, Michigan, and GOAL/QPC, Methuen, Massachusetts, pp.307~315.
Morgan, M. J., J. A. Attaway ＆ M. Griffin (1996), “The role of product/service experience in the satisfaction formation process: a test of moderation, Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 9 pp.391~405.
Moskowitz, H. ＆ K. -J. Kim (1997), “QFD optimizer: A novice friendly quality function deployment decision support system for optimizing product design, Computers ＆ Industrial Engineering, 32 (3) pp.641~655.
Park, T. ＆ K. -J. Kim (1998), “Determination of an optimal set of design requirements using house of quality, Journal of Operations Management, 16 (5) pp.569~581.
Raharjo, H., M. Xie ＆ A. C. Brombacher (2006), “Prioritizing quality characteristics in dynamic quality function deployment, International Journal of Production Research, 44 (23) pp.5005~5018.
Raharjo, H., M. Xie ＆ A. C. Brombacher (2011), “A systematic methodology to deal with the dynamics of customer needs in Quality Function Deployment, Expert Systems with Applications, 38 (4) pp.3653~3662.
Ramanathan, R. ＆ J. Yunfeng (2009), “Incorporating cost and environmental factors in quality function deployment using data envelopment analysis, Omega, 37 (3) pp.711~723.
Shin, J. -H., H. -B. Jun, D. Kiritsis ＆ P. Xirouchakis (2011), “A design support method for product conceptual design considering product lifecycle factors and resource constraints, International Journal of Advanced Manufacturing Technology, 52 (9-12) pp.865~886.
Takai, S. ＆ R. M. Kalapurackal (2012), “Sensitivity analysis of relative worth in quality function deployment matrices, Concurrent Engineering: Research and Applications, 20 (3) pp.195~202.
Tsiotsou, R. (2006), “The role of perceived product quality and overall satisfaction on purchase intentions, International Journal of Consumer Studies, 30 (2) pp. 207~217.
Wang, H., M. Xie ＆ T. N. Goh (1998), “A comparative study of the prioritization matrix method and the analytical process technique in quality function deployment, Total Quality Management, 9 (6) pp.421~430.
Wang, J. (1999). “Fuzzy outranking approach to prioritize design requirements in quality function deployment, International Journal of Production Research, 37 (4) pp.899~916.
Wang, Y. -M. ＆ K. -S. Chin (2011), “Technical importance ratings in fuzzy QFD by integrating fuzzy normalization and fuzzy weighted average, Computers and Mathematics with applications, 62 (11) pp.4207~4221.
Wang, Y. -M. (2012a), “A fuzzy-normalization-based group decision-making approach for prioritizing engineering design requirements in QFD under uncertainty, International Journal of Production Research, 50 (23) pp.6963~6977.
Wang, Y. -M. (2012b), “Assessing the relative importance weights of customer requirements using multiple preference formats and nonlinear programming, International Journal of Production Research, 50 (16) pp.4414~4425.
Wasserman, G. S. (1993), “On how to prioritize design requirements during the QFD planning process, IIE Transactions, 25 (3) pp.59~65.
Xu, Q., R. J. Jiao, Y. Xi ＆ M. Helander (2009), “An analytical Kano model for customer need analysis, Design Studies, 30(1) pp.87~110.
Yang, Z. ＆ R. T. Peterson (2004), “Customer perceived value, satisfaction, and loyalty: the role of switching cost, Psychology and Marketing, 21 pp.799~822.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：1058（臺灣：1030，外國：28） ／ 本年度總使用人次：2156165 ／ 訪客人次（自102年09月）：67956031
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上