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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99050完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭佳瑋 | zh_TW |
| dc.contributor.advisor | Chia-Wei Kuo | en |
| dc.contributor.author | 劉子綺 | zh_TW |
| dc.contributor.author | Tzu-Chi Liu | en |
| dc.date.accessioned | 2025-08-21T16:11:49Z | - |
| dc.date.available | 2025-08-22 | - |
| dc.date.copyright | 2025-08-21 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-02 | - |
| dc.identifier.citation | Bokor, Z. (2012). Cost calculation model for logistics service providers. Promet–Traffic & Transportation, 24(6), 515–524.
Boyer, K. K., Prud’homme, A., & Chung, W. (2009). The last mile challenge: Evaluating the effects of customer density and delivery window patterns. Journal of Business Logistics, 30(1), 185–201. Cheong, M. L., Bhatnagar, R., & Graves, S. C. (2007). Logistics network design with supplier consolidation hubs and multiple shipment options. Journal of Industrial and Management Optimization, 3(1), 51–69. Donald, J. B., David, J. C., Bixby, C. M., & John, C. B. (2020). Supply chain logistics management (5th ed.). McGraw-Hill Education. Ellram, L. M., LaLonde, B. J., & Weber, M. M. (1989). Retail logistics. International Journal of Physical Distribution & Materials Management, 19(12), 29–39. Esper, T. L., Jensen, T. D., Turnipseed, F. L., & Burton, S. (2003). The last mile: An examination of effects of online retail delivery strategies on consumers. Journal of Business Logistics, 24(2), 177–203. Ghosh, M., Kuiper, A., Mahes, R., & Maragno, D. (2023). Learn global and optimize local: A data-driven methodology for last-mile routing. Computers & Operations Research, 159, 106312. Ishfaq, R., & Bajwa, N. (2019). Profitability of online order fulfillment in multi-channel retailing. European Journal of Operational Research, 272(3), 1028–1040. Izadi, A., Nabipour, M., & Titidezh, O. (2019). Cost models and cost factors of road freight transportation: A literature review and model structure. Fuzzy Information and Engineering, 11(3), 257–278. Jang, H.-S., Chang, T.-W., & Kim, S.-H. (2023). Prediction of shipping cost on freight brokerage platform using machine learning. Sustainability, 15(2), 1122. Kaplan, R. S., & Anderson, S. R. (2007). Time-driven activity-based costing: A simpler and more powerful path to higher profits. Harvard Business Press. Lai, G., Liu, H., Xiao, W., & Zhao, X. (2022). “Fulfilled by Amazon”: A strategic perspective of competition at the e-commerce platform. Manufacturing & Service Operations Management, 24(3), 1406–1420. LaLonde, B. J., & Pohlen, T. L. (1996). Issues in supply chain costing. The International Journal of Logistics Management, 7(1), 1–12. Laporte, G. (1992). The vehicle routing problem: An overview of exact and approximate algorithms. European Journal of Operational Research, 59(3), 345–358. Li, L., & Li, G. (2023). Integrating logistics service or not? The role of platform entry strategy in an online marketplace. Transportation Research Part E: Logistics and Transportation Review, 170, 102991. Liu, H., Xu, T., Jing, S., Liu, Z., & Wang, S. (2023). The interplay between logistics strategy and platform’s channel structure design in B2C platform market. European Journal of Operational Research, 310(2), 812–833. Mo, B., Wang, Q., Guo, X., Winkenbach, M., & Zhao, J. (2023). Predicting drivers’ route trajectories in last-mile delivery using a pair-wise attention-based pointer neural network. Transportation Research Part E: Logistics and Transportation Review, 175, 103168. Ng, C. K. (2012). Inbound supply chain optimization and process improvement (Doctoral dissertation, Massachusetts Institute of Technology). Nguyen, D. H., De Leeuw, S., Dullaert, W., & Foubert, B. P. (2019). What is the right delivery option for you? Consumer preferences for delivery attributes in online retailing. Journal of Business Logistics, 40(4), 299–321. Özkaya, E., Keskinocak, P., Joseph, V. R., & Weight, R. (2010). Estimating and benchmarking less-than-truckload market rates. Transportation Research Part E: Logistics and Transportation Review, 46(5), 667–682. Risberg, A. (2023). A systematic literature review on e-commerce logistics: Towards an e-commerce and omni-channel decision framework. The International Review of Retail, Distribution and Consumer Research, 33(1), 67–91. Smith, D., & Srinivas, S. (2019). A simulation-based evaluation of warehouse check-in strategies for improving inbound logistics operations. Simulation Modelling Practice and Theory, 94, 303–320. Vakulenko, Y., Shams, P., Hellström, D., & Hjort, K. (2019). Online retail experience and customer satisfaction: The mediating role of last mile delivery. The International Review of Retail, Distribution and Consumer Research, 29(3), 306–320. Yu, Y., Wang, X., Zhong, R. Y., & Huang, G. Q. (2016). E-commerce logistics in supply chain management: Practice perspective. Procedia CIRP, 52, 179–185. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99050 | - |
| dc.description.abstract | 在 Amazon 平台運作下，第三方賣家需於訂單成立初期即選擇物流路徑，常見選項包括透過 Amazon Warehousing and Distribution（AWD）模式進行二段式配送，或採用 Fulfillment by Amazon（FBA）模式將商品直送至 Amazon 指定之倉儲中心。由於兩種路徑成本結構複雜且 Amazon 並未提供完整運費查詢機制，致使賣家難以即時做出具成本效益之決策。
本研究以 HourLoop 公司歷史訂單為研究對象，整合供應商、倉庫與商品尺寸等資料，建構小包裹（SPD）與卡車運輸（TL）之運費預測模型，並進一步建立分類模型以推薦每筆訂單最適物流路徑。模型結果顯示，Random Forest 在預測效能上表現最佳，SHAP 分析亦指出總體積與總重量為最具影響力之成本決策因子。敏感度測試結果顯示推薦模型具穩定性，僅在少數臨界訂單上產生路徑翻轉。 最終模擬結果顯示，若企業全面依推薦模型選擇路徑，整體運輸成本可較原始決策下降 16.57%。本研究提供一套數據驅動的物流決策工具，協助第三方賣家於訂單成立初期即做出有效且可解釋的路徑選擇，具備高度實務應用潛力。 | zh_TW |
| dc.description.abstract | Under the operational framework of Amazon, third-party sellers are required to determine their logistics path at the time of purchase order (PO) creation. The two main options include using Amazon Warehousing and Distribution (AWD) for two-stage delivery or Fulfillment by Amazon (FBA) for direct shipment to Amazon’s fulfillment centers (FCs). Due to the complexity of cost structures and the lack of transparent freight estimation tools, sellers face challenges in making cost-efficient decisions in real time.
This study focuses on historical order data from HourLoop Inc., integrating vendor addresses, warehouse locations, and product dimension information to develop freight cost prediction models for small parcel delivery (SPD) and truckload (TL) shipping. A classification model was further built to recommend the optimal logistics path for each PO. Experimental results show that the Random Forest model provides the best predictive performance, with SHAP analysis indicating that total volume and weight are the most influential cost factors. Sensitivity analysis reveals that the recommendation logic is stable under varying input conditions, with only marginal flips near decision boundaries. Simulation results demonstrate that following the model’s recommendations can reduce total logistics costs by 16.57% compared to actual decisions. This research provides a data-driven decision support tool that enables third-party sellers to make cost-effective and explainable logistics choices at the point of order creation, offering strong potential for real-world application. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-21T16:11:49Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-21T16:11:49Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 目次
致謝 i 摘要 ii Abstract iii 目次 v 圖次 viii 表次 ix 第一章緒論 1 1.1 研究背景與動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 研究對象概述：Hour Loop 飛輪電商. . . . . . . . . . . . . . . . . . 3 1.3 研究問題與目的. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 研究方法與流程. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 第二章文獻回顧 8 2.1 物流研究的演進：從傳統供應鏈到電商物流. . . . . . . . . . . . . 8 2.2 平台型物流制度的發展與挑戰. . . . . . . . . . . . . . . . . . . . . 9 2.3 出庫導向的研究趨勢與入庫階段的文獻缺口. . . . . . . . . . . . . 11 2.4 運費預測模型與物流成本建構之文獻探討. . . . . . . . . . . . . . 13 2.5 研究定位與貢獻小結. . . . . . . . . . . . . . . . . . . . . . . . . . 15 第三章研究方法 17 3.1 研究架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2 成本結構釐清. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2.1 FBA 模式. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.2 AWD 模式. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3 資料整合與處理. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3.1 資料來源. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3.2 資料清理與特徵工程. . . . . . . . . . . . . . . . . . . . . . . . 24 3.4 運費模型建構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.4.1 模型預測單位與輸入特徵. . . . . . . . . . . . . . . . . . . . . . 26 3.4.2 模型建構方法. . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.5 模型效能指標選擇. . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.6 決策推薦與驗證. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.6.1 模擬場景設定與前提假設. . . . . . . . . . . . . . . . . . . . . . 30 3.6.2 決策邏輯與推薦準則. . . . . . . . . . . . . . . . . . . . . . . . 30 3.6.3 推薦結果分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 第四章研究結果 32 4.1 SPD 運費模型分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.1 模型建構與效能比較. . . . . . . . . . . . . . . . . . . . . . . . 33 4.1.2 模型參數調整與測試集驗證. . . . . . . . . . . . . . . . . . . . 34 4.1.3 預測誤差分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.1.4 變數重要性與SHAP 解釋. . . . . . . . . . . . . . . . . . . . . . 37 4.1.5 小結與模型優勢. . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.2 TL 運費模型分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.2.1 模型建構與效能比較. . . . . . . . . . . . . . . . . . . . . . . . 39 4.2.2 模型參數調整與測試集驗證. . . . . . . . . . . . . . . . . . . . 40 4.2.3 預測誤差分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.4 變數重要性SHAP 分析. . . . . . . . . . . . . . . . . . . . . . . 44 4.2.5 小結與模型優勢. . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.3 決策推薦. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.3.1 決策邏輯與成本比較基準. . . . . . . . . . . . . . . . . . . . . . 46 4.3.2 成本差異與推薦結果分析. . . . . . . . . . . . . . . . . . . . . . 47 4.3.3 訂單特徵與推薦關係分析. . . . . . . . . . . . . . . . . . . . . . 48 4.3.4 推薦路徑敏感度分析. . . . . . . . . . . . . . . . . . . . . . . . 51 第五章結論與未來建議 56 5.1 研究總結與貢獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5.2 實務建議. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.3 研究限制. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.4 未來研究建議. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 參考文獻 61 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 運費模型 | zh_TW |
| dc.subject | 隨機森林 | zh_TW |
| dc.subject | 第三方賣家 | zh_TW |
| dc.subject | 物流成本預測 | zh_TW |
| dc.subject | AWD | zh_TW |
| dc.subject | FBA | zh_TW |
| dc.subject | 路徑推薦 | zh_TW |
| dc.subject | Third-Party Seller | en |
| dc.subject | Freight Model | en |
| dc.subject | Route Recommendation | en |
| dc.subject | Random Forest | en |
| dc.subject | AWD | en |
| dc.subject | FBA | en |
| dc.subject | Logistics Cost Prediction | en |
| dc.title | Amazon 第三方賣家物流成本效益分析－以 Hour Loop 為例 | zh_TW |
| dc.title | Logistics Cost-Benefit Analysis for Amazon Third-Party Sellers: A Case Study of Hour Loop | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 黃奎隆;孔令傑 | zh_TW |
| dc.contributor.oralexamcommittee | Kwei-Long Huang;Ling-Chieh Kung | en |
| dc.subject.keyword | 物流成本預測,第三方賣家,AWD,FBA,運費模型,路徑推薦,隨機森林, | zh_TW |
| dc.subject.keyword | Logistics Cost Prediction,Third-Party Seller,AWD,FBA,Freight Model,Route Recommendation,Random Forest, | en |
| dc.relation.page | 64 | - |
| dc.identifier.doi | 10.6342/NTU202502688 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-08-06 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 商學研究所 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 商學研究所 | |
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