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研究生： 曹偉杰
Vijay - Shekhar Jha
論文名稱： Knowledge management centric intelligent system with modified APQP : A case study for a potential solution for the future challenges of Semiconductor industry
Knowledge management centric intelligent system with modified APQP : A case study for a potential solution for the future challenges of Semiconductor industry
指導教授： 王孔政
Kung-Jeng Wang
口試委員: 歐陽超
Chao Ou-Yang
周碩彥
Shuo-Yan Chou
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2008
畢業學年度： 96
語文別： 英文
論文頁數： 71
中文關鍵詞： 知識管理 、品質管理 、半導體工業
外文關鍵詞： APQP, Semiconductor manufacturing services Industry
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    • Future challenges posed to semiconductor manufacturing service companies’ demand for a system framework that uses organizational knowledge intelligently in the era of information and competitiveness. We propose a framework that integrates agent based knowledge management (KM) intelligence and advance product quality planning (APQP) features to develop a systematic framework that responds to such challenges efficiently. Agent based knowledge management system (ABKMS) with APQP concept works just fine for any semiconductor manufacturing services organization. A case study of such an organization reflects how ABKMS successfully leverages intelligent agents to seek significant inroads into process knowledge repository to recommend a solution for decision making while following customized APQP process in delivering semiconductor manufacturing services. This system has the characteristics as intelligent, flexible, simple, and automatic etc.

    Future challenges posed to semiconductor manufacturing service companies’ demand for a system framework that uses organizational knowledge intelligently in the era of information and competitiveness. We propose a framework that integrates agent based knowledge management (KM) intelligence and advance product quality planning (APQP) features to develop a systematic framework that responds to such challenges efficiently. Agent based knowledge management system (ABKMS) with APQP concept works just fine for any semiconductor manufacturing services organization. A case study of such an organization reflects how ABKMS successfully leverages intelligent agents to seek significant inroads into process knowledge repository to recommend a solution for decision making while following customized APQP process in delivering semiconductor manufacturing services. This system has the characteristics as intelligent, flexible, simple, and automatic etc.

    Table of Contents CHAIRPERSON OF SUPERVISORY COMMITTEE1 TABLE OF FIGURES & TABLES6 CHAPTER 1. RESEARCH BACKGROUND7 1.1 CHALLENGES FOR THE FUTURE OF SEMICONDUCTOR MANUFACTURING SERVICES COMPANY10 CHAPTER 2. LITERATURE SURVEY18 2.1 RESEARCH CHALLENGES FOR ABKMS18 2.2 ENTERPRISE MODELING21 2.3 CRITERIA FOR EVALUATING ENTERPRISE MODELS29 2.4 COMPARISON OF MODELING APPROACHES31 2.5 OBJECT-ORIENTED APPROACH33 2.5.1 Unified Modeling Language (UML)34 2.5.2 UML characteristics34 2.5.3 Transfer a description into software implementation37 2. 6 SUMMARY37 CHAPTER 3. THE PROPOSED AGENT BASED KNOWLEDGE MANAGEMENT SYSTEMS (ABKMS) FRAMEWORK39 3.1 THE CHARACTERISTICS OF COMPONENTS OF ABKMS42 3.2 ADVANCE QUALITY PLANNING PROCESS- APQP44 3.2.1. INTELLIGENT TECHNOLOGY QUALITY PLANNING (ITQP)48 3.2.3. INTELLIGENT MATERIAL QUALITY PLANNING (IMQP)51 3.2.4. INTELLIGENT VENDOR QUALITY PLANNING (IVQP)55 3.2.5 INTELLIGENT EQUIPMENT/TOOLS QUALITY PLANNING (IETQP)58 CHAPTER 4. ABKMS SYSTEM IMPLEMENTATION & ASSESSMENT61 4.1 ABKMS IMPLEMENTATION62 4.1.1 Existing Infrastructure and planning62 4.1.2 Aligning KM and Business Strategy64 4.1.3: Knowledge Management System Analysis, Design, and Development65 4.2 SYSTEM REQUIREMENTS67 4.3 IMPLEMENTATION OF APQP SYSTEM70 4.3.1. APQP Development Planning Phase70 4.3.2. APQP Prototype phase70 4.3.3. Pilot Run71 4.3.4. Quality run71 4.4 MIDDLEWARE ARCHITECTURE OVERVIEW72 4.4.1 Elasticity of architecture75 4.5 CULTURAL ASSESSMENT AND KNOWLEDGE AUDITING77 4.5.1 Incentives to Participate77 4.5.2Eliminating Disincentives77 4.5.3 Rewards and Compensation77 4.6 METRICS FOR PERFORMANCE EVALUATION77 4.7. ASSESSMENT OF SYSTEM78 CHAPTER 5. CONCLUSION82 REFERENCES83

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