| 摘要(英) |
Retail chain stores can be described as a supply center for consumers in their daily life. In Taiwan, where people are densely populated, multinational chain stores are bullish about Taiwan residents’ spending power. Over the past ten years, multinational companies are attracted to this densely populated and competitive market to set up their branches. In this highly competitive business environment, how to use big data to solve their business and strategic problems, as well as to analyze on the impact of big data that may affect the change of the organizations, are the main points discussed in this paper.
With the advancement and popularity of information technology, chained retailers are receiving data from consumers’ s transactions all the time. These huge amounts of data are important assets for the enterprises. Data within the organization, such as sales analysis, forecasting, product recommendations, and decision guidance, all contribute to the control and execution of the company′s business decisions.
This research uses a case study method to describe the company’s transformation process with information technology. Through big data, observations, actual participation, and collection of research-related documents, the effectiveness of the company’s system introduction and migration is presented. The studied company understands the importance of big data implementation for their retail industry. From the development and introduction of a promotion management system, to the integration of aggregated store intelligence, the big data analysis proved to be an excellent reference for decision-making. Such big data analytics have not only improved the accuracy of the store merchandising, but also greatly reduced the labor cost. Finally, it reduces the workload of operation units and improves their work efficiency. |
| 參考文獻 |
【中文文獻】
1. APEC通訊（2017），「APEC運用大數據分析以因應就業市場需求」。
http://www.ctasc.org.tw/02publication/APEC-209-1.pdf
2. MBA智库·百科（2020），「時間序列預測法」。
https://wiki.mbalib.com/zh-tw/%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97%E9%A2%84%E6%B5%8B%E6%B3%95
3. MBA智库·百科（2020），「深度學習」。
https://wiki.mbalib.com/zh-tw/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0
4. 中華郵政（2018），2019 「中華郵政大數據競賽」。
https://stat.nutc.edu.tw/ezfiles/26/1026/attach/9/pta_36974_9662505_63006.pdf
5. 台北產經（2018），「拆解大數據開啟創業新商機」。
https://www.taipeiecon.taipei/article_cont.aspx?MmmID=1201&MSid=710374276057737734
6. 哈佛商業評論（2012），「大數據：管理的革命」。
https://www.hbrtaiwan.com/article_content_AR0007381.html
7. 哈佛商業評論（2018），「阿里巴巴打造一千個品牌數據銀行」。https://www.hbrtaiwan.com/article_content_AR0008175.html
8. 香港矽谷（2015），「前進 Zara 西班牙總部！揭開全世界最大時尚零售商的數字之謎」。https://www.hksilicon.com/articles/937766
9. 新網路科技（2018），「自製電影數超越好萊塢，Netflix靠大數據黏住觀眾！」。
https://www.smartm.com.tw/article/35353438cea3
10. 經濟部統計處（2019），「108年11月批發、零售及餐飲業營業額統計」。
https://www.moea.gov.tw/Mns/dos/bulletin/Bulletin.aspx?kind=8&html=1&menu_id=6727&bull_id=6689
11. 維基百科（2020），「決策數」。https://zh.wikipedia.org/wiki/%E5%86%B3%E7%AD%96%E6%A0%91
12. 維基百科（2020），「情感分析」。 https://zh.wikipedia.org/wiki/%E6%96%87%E6%9C%AC%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90
13. 數位時代（2016），「從線下到線上！Walmart 用數據抓住消費者的心」。
https://www.bnext.com.tw/article/41482/walmart-offline-to-online-big-data-e-commerce
14. 數位時代（2018），「如何讓網購更快更方便？亞馬遜不斷出奇致勝」。
https://www.bnext.com.tw/article/31719/BN-ARTICLE-31719
15. 數位時代（2019），「揭秘電商龍頭亞馬遜，不想讓你知道的自動化策略」。
https://www.bnext.com.tw/article/55217/amazon-logistics-automation
16. 游美惠（2000），「內容分析、文本分析與論述分析在社會研究的運用」，調查研究 – 方法與應用，8，5-42 。
17. 賴燕、孫海峰、任建軍（2017）。「了解在物流和供應鏈管理中採用大數據分析（BDA）的決定因素：一項實證研究」。國際物流管理雜誌，29（2），676-703。
18. 謝邦昌、江志民（2016） ，「主計資料大數據分析之研究」，臺北醫學大學管理學院大數據研究中心。
19. 謝邦昌，謝邦彥（2016），「Fanpage Karma」，大數據：語意分析整合篇，2，17-22 。
【英文文獻】
1. Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). “Research Trends on Big Data in Marketing: A Text Mining and Topic Modeling Based Literature Analysis.” European Research on Management and Business Economics, 24 (1), 1-7.
2. Agrawal, D., Bernstein, P., & Bertino, E. (2011). “Challenges and Opportunities with Big Data 2011-1.” Proceedings of the VLDB Endowment, 5, 12.
3. McAfee, A. & Brynjolfsson, E. (2012). “Big Data: The Management Revolution.” Harvard Business Review, 90(10):60-68 ·
4. Berry, M. J. A. & Linoff, G. (1997). Data Mining Technique for Marketing, Sale, and Customer Support, John Wiley & Sons, New York, NY.
5. Chen, H. M., Kazman, R., Haziyev, S., & Hrytsay, O. (2015). Big Data System Development：An Embedded Case Study with a Global Outsourcing Firm. In Proceedings of the First International Workshop on BIG Data Software Engineering, IEEE Press, 44-50 .
6. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R. (2000), “CRISPDM 1.0: Step-by-Step Data Mining Guide,” CRISP-DM Consortium: NCR Systems Engineering Copenhagen (USA and Denmark).
7. Eisenhardt, K. M. (1989), “Building Theories from Case Study Research,” The Academy of Management Review, 14 (4), 532 – 550.
8. Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014), “Big Data and Its Technical Challenges,” Communications of the ACM, 57 (7), 86-94.
9. Lohr, S. (2012), “The Age of Big Data,” New York Times, 11.
10. Madden, S. ( 2012 ), “From Databases to Big Data”. IEEE Internet Computing,16 (3), 4-6.
11. Russom, P. ( 2011 ), “TDWI Best Practices Report: Big Data Analytics”, The Data Warehouse Institute (TDWI), 24.
12. Shahbaz, Q. (2015), Data Mapping for Data Warehouse Design, Elsevier, 180.
13. Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017), “Critical Analysis of Big Data Challenges and Analytical Methods,” Journal of Business Research, 70, 263–286.
14. Yin, K. R. (1994), Case Study Research: Design and Methods, London: Sage Publications. |
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