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研究生： 劉以琳
Liu, Elim
論文名稱： 產品多樣化設計方法之研究
Methodologies for developing variance-based product design
指導教授： 蕭世文
Hsiao, Shih-Wen
學位類別： 博士
Doctor
系所名稱： 規劃與設計學院 - 工業設計學系
Department of Industrial Design
論文出版年： 2005
畢業學年度： 93
語文別： 英文
論文頁數： 109
中文關鍵詞： 產品多樣化
外文關鍵詞： product variety
相關次數： 點閱：81  下載：19 
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    • 摘要
    　　由於加工製造技術的進步與成熟，以往大量生產的觀念已漸式微，取而代之的是少量多樣或大量客製化的趨勢。因此，在一個穩定的產品架構下發展多樣化的產品已成為一重要的競爭優勢。其優點在於增加產品選擇性以擴大市場佔有率。然而，一般企業雖知其重要性，但如何兼顧產品多樣化與降低生產成本，縮短產品開發時間，卻缺少可實際執行的方法。針對此問題，本論文提出三項方法輔助企業發展產品的多樣化設計。
    　　第一，本研究提出以結構化的圖示表現一產品內部零件的互動及階層關係，以決定零件設計順序，並評估設計成本。第二，本研究提出一產品架構最佳化之設計方法。此方法首先整合設計師知識，以應用分析網路程序（ANP）進行零件設計順序的排序，再將此排序結果導入目標規劃式中，在最佳資源分配的限制下，求取最佳的平台零件及變異零件項目，以得到穩健的產品家族架構。第三項方法則是應用人工智慧技術以求得最佳的客製化產品設計。此方法所運用的技術有：一、以模糊邏輯模擬口語化的市場資訊。二、訓練倒傳遞網路，使其可由顧客需求推論適當的設計變數。三、由設計變數的組合產生設計替選方案。四、應用遺傳演算法產生設計替選方案，並運用其搜尋能力找到最佳的產品設計。
    　　本論文中詳述此方法如何實際運用於三個家族產品的設計上：包含滴漏式咖啡壺，水冷裝置以及鬧鐘設計。此外，在設計理論的研究包含現有產品及未來產品多樣化之規劃與管理，以及設計程序最佳化等議題。

    ABSTRACT
    　　Developing a product variety under a robust architecture provides a company with an important competitive advantage. The competitive benefits include reducing extending product portfolios and expanding market share. While companies understand the strategic reasons for developing such architecture, but how to design product variety as well as reduce engineering costs and time to market is always a challenge. This dissertation develops three methodologies to assist companies in managing this problem.
    　　First, a methodology was developed to represents the design priority and related design constraints within a product using a structural graph. Second, a design methodology for achieving optimal product architecture was introduced. In this methodology, the analytic network process is first employed to incorporate designers’ knowledge in calculating relative importance of components regarding to customer needs. The goal programming approach then is applied to determine the platform and also the variant components focused on redesign. The drivers of variances of components are further investigated to ensure the redesigned parts meet the requirements of specialized niches in the segment markets. The third methodology proposes the use of artificial intelligence techniques to optimize customized product design. This study focuses on (1) modeling imprecise market information by applying fuzzy theory; (2) mapping relationships between design parameters and customer requirements using BPN; (3) synthesizing design alternatives, and (4) realizing the synthesis in GA, using its searching capacity to obtain the optimal solution.
    　　The dissertation demonstrates the methodologies in detail on three product families: the coffee makers, thermoelectric water coolers and alarm clocks. It also contains a description of uses for the methodologies in other areas of design research: including the management of current/future product variety and the optimization of design tasks.

    ABSTRACT........................................I ACKNOWLEDGEMENT.................................III CONTENTS........................................IV LIST OF TABLES..................................VII LIST OF FIGURES.................................IX NOMENCLATURE....................................XI 1.INTRODUCTION ..................................1 1.1. MOTIVATION .................................1 1.2. RESEARCH BACKGROUND AND RELATED LITERATURES.2 1.2.1. Product variety...........................3 1.2.2. Product family architectur................3 1.2.3. Product platform..........................4 1.2.4. Component coupling........................6 1.2.5. Artificial intelligence aided product customization....................................7 1.3. OBJECTIVES..................................8 1.3.1. Structural component-based approach for designing product variety........................9 1.3.2. Decision support system for product family design...........................................10 1.3.3. Artificial intelligence aided product customization....................................11 1.4. ORGANIZATION OF THE THESIS..................13 2. THEORETICAL BACKGROUND........................14 2.1. INTRODUCTION................................14 2.2. INTERPRETIVE STRUCTRAL MATRIX...............14 2.3. ANALYTIC NETWORK PROCESS....................17 2.3.1. Supermatrix formation.....................18 2.3.2. Analytic hierarchy process ...............19 2.4. FUZZY LINQUISTIC EXPRESSION.................20 2.5. BACKPROPAGATION NEURAL NETWORK..............23 2.6. GENETIC ALGORITHMS..........................25 2.7. CONCLUSION..................................26 3. STRUCTURAL COMPONENT-BASED APPROACH FOR DESIGNING PRODUCT VARIETY........................28 3.1. INTRODUCTION................................28 3.2. PROSED METHODOLOGY..........................28 3.3. CASE STUDY: ANALYSIS PHASE..................29 3.3.1. Object product............................29 3.3.2. Identify market-driven variety............31 3.3.2.1. Market Planning ........................31 3.3.2.2. Identify the exterior drivers of variation: the QFD analysis......................32 3.3.3. Identify the interior hierarchical interactions: the ISM approach...................34 3.4. APPLYING THE ANALYTICAL RESULT TO PRODUCT FAMILY DEVELOPMENT: REDESIGN PHASE...............38 3.4.1. Design for spatial variety................38 3.4.2. Design for temporal variety...............41 3.4.3. Developing a product family...............43 3.5. CONCLUSION .................................46 4. DECISION SUPPORT SYSTEM FOR PRODUCT FAMILY DESIGN...........................................47 4.1. INTRODUCTION ...............................47 4.2. DECISION METHODOLOGY OF OPTIMAL PRODUCT FAMILY DESIGN...........................................47 4.2.1. Analytic network analysis (ANP)...........49 4.2.2. Goal programming..........................50 4.2.2.1. GP model for product platform construction.....................................50 4.2.2.2. GP model for selecting variant components.......................................51 4.3. ILLUSTRATIVE EXAMPLE........................52 4.3.1.Market planning............................53 4.3.2.The ANP approach...........................53 4.3.3.GP approach for determining optimal architecture.....................................61 4.4. BACKWARD APPROACH- IDENTIFYING THE SOURCES OF COMPONENT VARIANCES..............................65 4.4.1 Identify external drivers of component variance.........................................66 4.4.2 Identify internal drivers of component variance.........................................67 4.5. SENSITIVITY ANALYSIS OF ANP APPROACH........69 4.6. CONCLUSION..................................72 5. CUSTOMIZATION APPROACH FOR PRODUCT VARIETY DESIGN...........................................73 5.1. INTRODUCTION................................73 5.2. PROPOSED METHODOLOGY........................74 5.2.1. Modeling the criteria for evaluating customer requirements............................75 5.2.2. Synthesizing design alternatives..........76 5.2.3. Back propagation neural network-based inference mechanism..............................77 5.2.4. Evolutionary optimization of design.......77 5.2.4.1. Chromosome representation...............77 5.2.4.2. GA operations...........................78 5.3. CASE STUDIES................................80 5.3.1. Object product............................80 5.3.2. Modeling customer requirements............80 5.3.3. Developing design alternatives............83 5.3.4. Establishing the inference mechanism of BPN..............................................83 5.3.5. Experimental approach of BPN..............84 5.3.6. Evolutionary mechanism of GA..............87 5.3.7. GUI design................................90 5.4. CONCLUSION .................................91 6. DISCUSSIONS.................................. 93 6.1. DESIGN STRATEGIES EVOLVED IN THE STRUCTURAL COMPONENT-BASED APPROACH.........................93 6.2. COMPARING THE EFFECTIVENESS BETWEEN THE ANP-BASED PRODUCT VARIETY APPROCH AND THE DFV APPROACH.........................................94 6.3. COMPARING THE EFFICIENCY BETWEEN THE NEURO-FUZZY BSED APPROACH AND THE QFD BASED METHODOLOGY......................................96 7. CONCLUSIONS...................................98 7.1. CONTRIBUTION OF RESEARCH....................98 7.2. SUGGESTIONS FOR FOLLOW-UP RESEARCH STUDIES..99 REFERENCES......................................101 PUBLICATION.....................................108 VITA............................................109

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