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    請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/10954


    題名: 應用UML於供應鏈及企業資源規劃初步分析之比較;Comparison on the Preliminary Analysis for Supply Chain Model and ERP Process Using UML
    作者: 陳彥銘;Yan-Ming Cheng
    貢獻者: 工業管理研究所
    關鍵詞: 企業資源規劃;供應鏈管理;統一模式語言;資料轉換;Enterprise Resource Planning;Supply Chain Management;UML;Data transformation
    日期: 2001-07-05
    上傳時間: 2009-09-22 14:10:39 (UTC+8)
    出版者: 國立中央大學圖書館
    摘要: Supply Chain Management (SCM) has been known to deal with decision support problems while Enterprise Resource Planning (ERP) takes into account a firm’s execution problems. Traditionally these two types of problems are handled in two separate systems. This research, at our best knowledge is the first one to perform preliminary analysis on a prototype which integrates both systems together. This research analyzes a supply chain module, Supply Network Planning (SNP), and an ERP module, Sales and Distribution (SD), compares the Master Data of these two systems and analyzes the communication of them. Starting by discussing the Master Data of SD module and SNP module, we analyze two SNP decision models and two SD business transactions in UML to understand the static data and dynamic process of these two systems. Following this, we analyze and summarize the data transfer atomic operations between SD Master Data and SNP Master Data. We exploit the transformation types of Master Data used by these two systems and categorize the data transfer group. Each group represents various atomic operation required. At last, we study the interactions of SD and SNP modules in terms of data transfer types. In this research, we use Use Case Diagram to define the system boundary, Activity Diagram to explore interaction behaviors between SD and SNP, Sequence Diagram and Class Diagram to present the objects and messages among them. Via UML, we discover 9 data transformation atomic operations, 9 data transformation groups and count the related frequency between SD and SNP. At last we provide a tentative suggestion to improve the data transformation.
    顯示於類別:[工業管理研究所 ] 博碩士論文

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