隨著網路的發達，引起消費習慣的改變，消費者習慣透過電商平台購物，而大多數的電商平台都會將商品交由第三方物流公司負責管理及配送服務，第三方物流公司每天都需要處裡來自電商平台大量的配送訂單，在過往的經營模式下，不論每日訂單量多寡，都採用每日配送的方式，需要耗費大量的時間及資源，若能有效的整合現有的訂單，針對不同時間的需求改善配送方式，甚至進一步評估各地區較常被訂購的商品，將需求品項提前配送至當地的物流倉庫，既能滿足消費者需求，亦能提升整體配送效率，為個案物流公司省下可觀的成本支出。
本研究透過數據分析，整合個案物流公司提供的歷史配送資料，將配送訂單根據進行區域分群，並針對不同地區的訂購特性提出更有效率的配送改善建議及商品庫存種類決策參考，提供個案物流公司作為處理現有訂單與存貨管理時的參考依據。
另外，本研究透過分析一定期間內的品項訂購頻率及數量，選取出熱門商品，並依照需求規則進行排列，提供個案公司儲位空間規劃建議。文中應用聚類分析將訂單資料進行區域分群，並分析訂單品項變化，了解消費者購物習慣，找出各地區需求品項，作為存貨管理依據。

With the development of the Internet, changes in consumption habits are caused. Nowadays consumers are used to shopping on e-commerce platforms. Most e-commerce platforms will deliver their goods to third-party logistics company for management and distribution services. Third-party logistics companies need to process a large number of distribution orders from e-commerce platforms every day. Under the previous business model, no matter how many of the daily order volume, the daily delivery method is used, which requires a lot of time and resources. If it can effectively integrate existing orders To improve the delivery method according to the needs at different times, and even further evaluate the more frequently ordered products in various regions, and deliver the required items to the local logistics warehouse in advance, which can not only meet the needs of consumers, but also improve the overall distribution efficiency. Logistics companies save considerable costs.
This study uses data analysis to integrate historical distribution data provided by case logistics companies, group delivery orders according to regions, and propose more efficient distribution improvement suggestions and merchandise inventory decision references for order characteristics in different regions to provide case logistics companies. As a reference basis for processing existing orders and inventory management.
In addition, this study selects popular commodities by analyzing the order frequency and quantity of items within a certain period, and arranges them according to the demand rules to provide storage space planning suggestions for case companies. In this paper, the cluster analysis method is used to regionally group order data, analyze the changes of order items, understand consumer shopping habits, find
out the demand items in various regions, and use them as the basis for inventory management.

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 研究對象與範圍 5
1.4 個案背景簡介 5
1.4.1 公司簡介 5
1.4.2 倉儲業務內容 6
1.4.3 電商專倉 6
1.5 研究限制 6
1.6 研究流程與架構 7
第二章 文獻探討 9
2.1 訂單數據分析 9
2.1.1 數據分析 9
2.1.2 視覺化數據分析 14
2.1.3 資料探勘 14
2.2 第三方物流倉儲 16
2.2.1 第三方物流定義 16
2.2.2 倉儲管理 18
2.2.3 儲位分配 19
2.3 資料分群 20
2.4 小結 25
第三章 研究方法 27
3.1 研究課題 27
3.2 研究流程 27
3.3 研究方法 29
3.3.1 資料匯入 29
3.3.2 資料處理 31
3.3.3 區域分群與資料再處理 34
3.3.4 資料分析與品項分析 37
第四章 資料分析結果 41
4.1 分群 41
4.2 每日各區及全台訂單量變化 46
4.3 各區每星期訂單量變化 48
4.4 不同選取規則熱門商品 52
4.5 各地區每周及全月熱門品項數與重複天數 55
4.5.1 北部群 56
4.5.2 中部群 59
4.5.3 南部群 62
4.5.4 小結 65
4.6 各地區全月熱門品項重複天數與平均數量 66
4.6.1 北部群 67
4.6.2 中部群 69
4.6.3 南部群 70
4.6.4 小結 72
4.7 不同擺放規則之儲位建議 73
第五章 結論與建議 93
5.1 結論 93
5.2 建議 93
參考文獻 95

Arima, C., Hakamada, K., Okamoto, M., & Hanai, T. (2008). Modified fuzzy gap statistic for estimating preferable number of clusters in fuzzy k-means clustering. Journal of bioscience and bioengineering, 105(3), 273-281.
Arora, P., & Varshney, S. (2016). Analysis of k-means and k-medoids algorithm for big data. Procedia Computer Science, 78, 507-512.
Bholowalia, P., & Kumar, A. (2014). EBK-means: A clustering technique based on elbow method and k-means in WSN. International Journal of Computer Applications, 105(9).
Bucklin, R. E., & Sismeiro, C. (2009). Click here for Internet insight: Advances in clickstream data analysis in marketing. Journal of Interactive marketing, 23(1), 35-48.
Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., & Zanasi, A. (1998). Discovering Data Mining. From Concept to Implementation. Upper Saddle River, NJ: Prentice Hall.
Card, M. (1999). Readings in information visualization: using vision to think. Morgan Kaufmann.
Choi, S., Kim, B. H., & Do Noh, S. (2015). A diagnosis and evaluation method for strategic planning and systematic design of a virtual factory in smart manufacturing systems. International Journal of Precision Engineering and Manufacturing, 16(6), 1107-1115.
Chuang, Y. F., Lee, H. T., & Lai, Y. C. (2012). Item-associated cluster assignment model on storage allocation problems. Computers & industrial engineering, 63(4), 1171-1177.
Cooper, Donald R and William. Emory(1995). Business Research Methods. Chicago: Irwin.
De Koster, R., Le Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182(2), 481–501.
Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73.
Dumbill, E. (2012). What is big data? An introduction to the big data landscape. Retrieved 17th December 2017, from http://radar.oreilly.com/2012/01/what-is-big-data.html
Gattorna, J., Selen, W., & Ogulin, R. (2004). Characteristics, strategies and trends for 3PL/4PL in Australia. Sydney, Australia, 11-16.
Gu, J. X., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1–21.
Hamdan, A., & Rogers, K. J. (2008). Evaluating the efficiency of 3PL logistics operations. International journal of production economics, 113(1), 235-244.
Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the royal statistical society. series c (applied statistics), 28(1), 100-108.
J. Drury, Towards more efficient order picking, IMM Monograph No. 1, The Institute of Materials Management, Cranfield, U.K., 1988.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.
Jam Lee(2018)。Partitional Clustering 切割式分群 | Kmeans, Kmedoid | Clustering 資料分群。檢自https://www.jamleecute.com/partitional-clustering-kmeans-kmedoid/(Apr.5,2020)
Knemeyer, A. M., & Murphy, P. R. (2004). Evaluating the performance of third‐party logistics arrangements: a relationship marketing perspective. Journal of supply chain management, 40(4), 35-51.
Lee, W. J. (2008). Discovery of fuzzy temporal and periodic association rules. National Sun Yat-Sen University: Department of Electrical Engineering.
Lin, C. M. (2005). Clustering Multilingual Documents: A Latent Semantic Indexing Based Approach. Department of Information Management.
Liu, B., Hsu, W., & Ma, Y. (1998, August). Integrating classification and association rule mining. In KDD (Vol. 98, pp. 80-86).
Liu, H. C. (2008). Fuzzy partition clustering algorithms based on alternative Mahalanobis distances. Journal of Educational Measurement and Statistics, 16.
Liu, J. C., & Yeats, M. (2016). 評論臺灣股票市場價格群聚現象之研究 (Critique of Papers on Price Clustering in the Taiwan Stock Market). Journal of Sciences & Humanities, 3(4), 39-62.
Louie, L. (2001). A study between sport participation and individual health care expenditure on Hong Kong adults. Hong Kong Sports Development Board Research Report.
Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F. Y. (2014). Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2), 865-873.
Ma, H., King, I., & Lyu, M. R. (2007, July). Effective missing data prediction for collaborative filtering. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 39-46).
Machui, F., Hösel, M., Li, N., Spyropoulos, G. D., Ameri, T., Søndergaard, R. R., ... & Lenssen, D. (2014). Cost analysis of roll-to-roll fabricated ITO free single and tandem organic solar modules based on data from manufacture. Energy & Environmental Science, 7(9), 2792-2802.
McAfeen, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, October, 1-9.
Merliana, N. P. E., & Santoso, A. J. (2015). Analisa Penentuan Jumlah Cluster Terbaik pada Metode K-Means Clustering.
Pchome Lam(2019)。Clustering決定分群數的方法。檢自https://medium.com/@pahome.chen/clustering%E6%B1%BA%E5%AE%9A%E5%88%86%E7%BE%A4%E6%95%B8%E7%9A%84%E6%96%B9%E6%B3%95-abedc1d81ccb(Jul.29,2020)
Pollard, K. S., & Van Der Laan, M. J. (2002). A method to identify significant clusters in gene expression data.
Sahay, B. S., & Mohan, R. (2006). 3PL practices: an Indian perspective. International Journal of Physical Distribution & Logistics Management.
Sharma, S., & Sharma, J. (2009). Sales and advertisement relationship for selected companies operating in India: A panel data analysis. School of Doctoral Studies (European Union) Journal, 1(1), 83-96.
Sheng, W., & Liu, X. (2004, June). A hybrid algorithm for k-medoid clustering of large data sets. In Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753) (Vol. 1, pp. 77-82). IEEE.
Shulin Lan., Chen, Yang., George Q, Huang. (2017). Data analysis for metropolitan economic and logistics development. Advanced Engineering Informatics Volume 32, April 2017, Pages 66-76
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2000). Designing and Managing the Supply Chain. 台北市，新陸書局。
Skjoett‐Larsen, T. (2000). Third party logistics–from an interorganizational point of view. International journal of physical distribution & logistics management.
Smiciklas, M. (2012). The power of infographics: Using pictures to communicate and connect with your audiences. Que Publishing.
Subbalakshmi, C., Krishna, G. R., Rao, S. K. M., & Rao, P. V. (2015). A method to find optimum number of clusters based on fuzzy silhouette on dynamic data set. Procedia Computer Science, 46, 346-353.
Syakur, M. A., Khotimah, B. K., Rochman, E. M. S., & Satoto, B. D. (2018, April). Integration k-means clustering method and elbow method for identification of the best customer profile cluster. In IOP Conference Series: Materials Science and Engineering (Vol. 336, No. 1, p. 012017). IOP Publishing.
Thakkar, J., Deshmukh, S. G., Gupta, A. D., & Shankar, R. (2005, January). Selection of third-party logistics (3PL): a hybrid approach using interpretive structural modeling (ISM) and analytic network process (ANP). In Supply Chain Forum: An International Journal (Vol. 6, No. 1, pp. 32-46). Taylor & Francis.
Van den Berg, J. P., & Zijm, W. H. (1999). Models for warehouse management: Classification and examples. International journal of production economics, 59(1-3), 519-528.
Velmurugan, T., & Santhanam, T. (2010). Computational complexity between K-means and K-medoids clustering algorithms for normal and uniform distributions of data points. Journal of computer science, 6(3), 363.
Verykios, V. S., Elmagarmid, A. K., Bertino, E., Saygin, Y., & Dasseni, E. (2004). Association rule hiding. IEEE Transactions on knowledge and data engineering, 16(4), 434-447.
Wagstaff, K., Cardie, C., Rogers, S., & Schrödl, S. (2001, June). Constrained k-means clustering with background knowledge. In Icml (Vol. 1, pp. 577-584).
Wong, Y.-Y., Maher, T.E., Nicholson, J.D. and Gurney, N.P. (2000), ‘‘Strategic alliances in logistics outsourcing’’, Asia Pacific Journal of Logistics and Marketing, Vol. 12 No. 4, pp. 3-21.
Yan, M., & Ye, K. (2007). Determining the number of clusters using the weighted gap statistic. Biometrics, 63(4), 1031-1037.
YH Huang(2018)。工業生產線數據分析平台之自動化測試與實作案例。
呂瑞男(2015)。大數據分析於行銷策略應用之研究。中山大學高階經營碩士班學位論文,1-39。
侯榮英, 張文信, 蕭智維, & 江宜靜 (2010)。運用商業智慧系統建構視覺化會計圖表應用於醫院管理-以南部某醫院為例。醫務管理期刊, 11(3), 75-87。
施東河(2004)。惡意電子郵件偵測之研究-以自我組織映射圖與 k-medoids 群集模式為例。 資訊管理學報, 11(2), 211-235。
洪德樺(2019)。環境汙染源溯源行動載具之場域數據分析。
徐晟熏(2015)。資料探勘(Data mining)-在人力資源管理上的分析與應用。
高翊瑋, 侯藹玲, 葉玲瓏, & 姜葉飛. (2016)。醫療大數據概述。 Journal of Data Analysis, 11(5), 1-12經濟部商業司(2007)。2007台灣物流年鑑。台北：經濟部。
張家豪(2018)。讓台灣棒球的數據分析與美國齊頭並進。科學教育月刊。
莊硯勛(2019)。大數據分析於環境規劃與管理之應用-指標建立、空品監測、地下水質分析。
曾敬富,劉姿妤,唐佳慈,游子瑩,莊宣玲(2018)。愛滋病人口變項數據分析研究。
劉嘉薇。(2017)。網路統獨的聲量研究：大數據的分析。政治科學論叢, (71), 113-165.
劉增蔚(2012)。第三方物流因應突發性需求之策略探討。
蘇雄義(2011)。物流與運籌管理：供應鏈管理的基礎。台北市：華泰文化事業股份有限公司。