臺灣博碩士論文加值系統

企業資源規劃系統的普及讓各個企業都累積大量的營運資料，目前這些資料的呈現大都是靜態表格的畫面或報表，當使用者面對很多表格與數字時，事實上並不容易掌握表格之間或表格數字之間的關係與意涵。除了企業資源規劃系統所蒐集的資料以外，企業的管理還要建立企業營運的指標以便進行績效管理。因此，如何讓資料透過分析彙整，使資料產生更大價值及效用，是目前企業資源規劃系統的重要研究議題。本研究提出以線上分析處理與資訊視覺化的技術來處理這些的資料，我們將以銷配模組資料來展示分析的過程。研究結果顯示，線上分析處理提供和使用者互動介面，讓使用者可以以不同角度觀看資料；而資料視覺化，透過多元的靜態與動態圖表呈現資料，讓資料更容易被理解；資料視覺化的使用者自定義功能，則提供設計企業營運指標的方法與視覺化呈現。使用者以互動、動態與視覺化的方式來進行資料的了解與分析的工作，可以使資訊被更快速且正確的被認知、解讀，讓資料成為管理決策的依據，資訊視覺化亦可成為組織之間溝通的利器，提高組織間溝通的效率。

Due to the popularity of enterprise resource planning system (ERP), all enterprises have accumulated a lot of enterprises operation data. Currently, the presentation of these data are mostly by using the static table format of screen forms or reports, when the user face a lot of tables and digital numbers, it is not easy to realize the relationship between these numbers or tables and their meaning. In addition to the data collected by the ERP, managers also needs to establish the performance indicators. Therefore, how to make the data go through analysis and aggregation so that the data can create greater values is an important research issue of the ERP. This study proposes to process data by online analytical processing (OLAP) and information visualization (IV). We will show the steps of the analysis with the sales module data. The results show that the OLAP provides better user interactions and allows users to view data from different perspectives. IV provides multiple static and dynamic charts, makes the data easier to understand. User defined function of IV let the users to design business performance indicators and present the indicators in visualization. The use of interactive, dynamic and visual means of the data can make the data more quickly and correctly recognized and interpreted, so that the data can be the basis of management decision-making. The results of information visualization can also become a communication tool between organizations to improve the efficiency of communication.

目錄

I
致謝 i
摘要 iii
Abstract iv
目錄 v
第一章 緒論 1
1.1研究背景動機 1
1.2研究目的 3
第二章 文獻探討 4
2.1企業資源規劃(Enterprise Resource Planning) 4
2.2線上分析處理(Online Analytical Processing) 5
2.3資訊視覺化(Information Visualization) 8
2.4指標與績效(KPI) 12
2.5 RFM模型(Recency, Frequency, Monetary) 14
第三章 研究方法與步驟 16
3.1資料蒐集 17
3.2前置資料處理 18
3.3 ETL的轉換 18
3.4維度設計 21
3.5 OLAP分析 23
3.6指標設計 24
3.7自定義函數設計 25
3.8視覺化介面設計 27
3.9視覺化呈現 27
第四章 研究結果 29
4.1線上分析處理 29
4.2指標設計 31
4.3使用者自定義設計 34
4.4 RFM分析 36
4.5儀表板-RFM模型分析 38
第五章 結論 43
參考文獻 45

中文文獻
1.Dan Jones、Jim Womack，譯者：林建銘 (民97)，「綜觀全局：繪製延伸價值流示意圖」，中衛發展中心
2.林峰正、黃昱霖、吳宗翰、衷嵐焜 (民101)，「巨量資料視覺化之研究」，中華民國科技部。
3.林文遠、周采蓉、吳宏達 (民101)，「政府歲計會計資料多維度分析之應用」(研究報告)，行政院主計總處。
4.江美英、吳佩諭著 (民100)，「ERP基礎觀念及導入方法篇」鼎新電腦，台北。
5.何靖遠、陳慧玲 (民103)，「以擴充RFM模型探討海峽兩岸消費者在網路購物之再購行為研究」，中央大學，桃園市。
6.余巧、陳國雄 (民97)，「商業智慧的開發模組。2008資訊管理技術與實務應用發展暨資訊人才培育研討會」，台北市。
7.國立中央大學管理學院ERP中心 (民100)，「商業智慧」，滄海書局，桃園市。
8.國立中央大學管理學院ERP中心 (民102)，「ERP企業資源規劃導論」，旗標出版，桃園市。
9.夏則智 (民93)，「線上分析處理之彈性報表設計方法論」，高雄。
10.陳紀宏 (民90)，「資料視覺化於生產管理報表資料」，桃園縣。
11.陳馥瑜、劉念琪 (民98)，「組織績效指標間關連性探討」，第十五屆企業人力資源管理實務專題研究成果發表會，中央大學。
12.廖漢君、曾光輝 (民102)，「電子商務網站經營與管理：osCommerce(第二版)」，碁峰，台北市。
13.戴建耘、袁贊助、蔡志宏 (民91)，「OLAP線上分析處理在資料倉儲的應用－以技職課程資源網站為例(下)」生活科技教育，35(10)。
14.戴國豪、郭義、陳盈岑、吳義男 (民99)，「資料倉儲與線上分析處理技術之應用-以員工健檢資料分析為例」，醫檢會報第二十五卷第二期。

二、英文文獻
1.Ausiello, G., Datri, A. and Sacca, D. (1983), “Graph Algorithms for Functional Dependency Manipulation,” Journal of the Association for Computing Machinery, Vol. 30, No, 4, October 1983, pp. 752-766.
2.Beale, R., McNab, J. and Witten, H. (1997), “Visualizing Sequences of Queries: A New Tool for Information Retrieval,” IEEE Conference on information Visualization, pp.57-62, August.
3.Card, K., Mackinlay, D., Shneiderman, B. (1999), ” Readings in Information Visualization: Using Vision to Think,” Univ. of Maryland, College Park pp.442-45.
4.Catarci, T., Costabile, F., Levialdi, S. and Batini, C, (1997), “Visual Query Systems for Databases: A Survey,” Journal of Visual Languages and Computing, Vol. 8, pp. 215-260.
5.Chaudhuri, S. and Dayal, U. (1997), “An overview of data warehousing and OLAP technology,” (ACM) 26 (1): pp.65-74.
6.Christa, K. and Thorsten, W. (2011), “Ten guidelines for effective data visualization in scientific publications, ” Environmental Modeling & Software, pp.1-6.
7.Connolly, T. and Begg, C. (1998), “DATABASE SYSTEM: a practical approach to design, implement, and management,” Addsion-Wesley.
8.Codd, F.(1993), “Providing OLAP (on-line analytical processing) to user-analysts: An IT mandate,” Technical report, E. F. Codd Associates.
9.Demirag, S. (1987), “How UK companies measure overseas performance,” Accountancy, Vol. 99, pp. 101-103.
10.Ha, H. (2007), “Applying knowledge engineering techniques to customer analysis in the service industry,” Advanced Engineering Informatics, 21(3), pp.293-301.
11.Havre, S., Hetzler, E., Whitney, P. et al. (2002), “Theme River: visualizing thematic changes in large document collections,” IEEE Transactions on Visualization and Computer Graphics, 8(1): pp.9-20.
12.Hayes, D. C., Hunton, J. E. and Reck, J. L. (2001),“Market reaction to ERP implementation announcements,” Journal of Information Systems, 15(1): pp.3-18.
13.Hetzler, E., Whitney, P., Martucci, L. and Thomas, J. (1998), “Multi-faceted Insight Through Interoperable Visual Information Analysis Paradigms,” Proceedings of IEEE Symposium on Information Visualization.
14.Hetzler, B., Whitney, P., Martucci, L. and Thomas, J (1998), “Multi-faceted Insight Through Interoperable Visual Information Analysis Paradigms,” Proceedings of IEEE Symposium on Information Visualization, InfoVis '98, pp.137-144.
15.Hughes, Arthur M. (1994), Strategic Database Marketing, Chicago：Probus Publishing.
16.Han, J. and Kamber, M. (2001), Data mining : Concepts and techniques, San Francisco, CA: Morgan Kaufmann Publishers.
17.Kaymak, U. (2001), “Fuzzy target selection using RFM variables,” IFSA World Congress and 20th NAFIPS International Conference, Vol.2, pp. 1038 –1043.
18.Miglautsch, R. (2000), “Thoughts on RFM Scoring,” Journal of Database Marketing, Vol. 8, No. 1, pp.67-72.
19.Riccardo, M (2009), Introduction to Information Visualization. Switzerland; Springer.
20.Sung-Hwan, M. (2005), “Detection of the customer time-variant pattern for improving recommender systems,” Expert Systems with Applications, 28(2), pp.189-199.
21.Steers, R. M. (1977), “Antecedents And Outcomes of Organizational Commitment,” Administrative Science Quarterly, (3:1), pp 46-56.
22.Superstore Sale (2015), Retrieved August 15. 2015, from http://community.tableau.com/docs/DOC-1236
23.Tableau (2015). Retrieved December 15. 2015, from http://get.tableau.com/trial/tableau-software.html
24.Ware, C. (2000), Information Visualization: Perception for Design, Academic Press, San Diego.
25.Wise, J. A., Thomas, J. J., Pennock, K., Lantrip, D., Pottier, M., Schur, A. and Crow, V. (1995), “Visualizing the Non-Visual: Spatial Analysis and Interaction with Information from Text Documents,” IEEE Symposium On Information Visualization 1995, Atlanta, Georgia, USA.
26.Wylie, L. (1990), “A Vision of Next Generation MRP II,” Scenario, Gartner Group, pp.300-339.