隨著市場競爭日益激烈，採購策略成為企業成敗的關鍵因素之一。相較於其他產業，零售業採購部門面臨更為複雜的挑戰。他們需同時兼顧市場潛力商品與潛在供應商的開發，亦須持續追蹤並評估供應商表現，導致其在決策流程需要面對大量的資訊以協助判斷。然而，現有系統多聚焦於供應商評估，對於零售業採購前期的探索與決策階段支援有限。
為理解零售採購於實務上的流程與需求，本研究邀請具備實務經驗之採購專家進行訪談，並歸納出系統設計需求。依據這些需求，我們開發一套名為 Procuvis 的視覺化決策支援系統，旨在輔助零售業於採購流程前期進行探索與決策判斷。系統採用 Glyph為核心的視覺化方法，整合市售商品與供應商的多維度資料，並透過不同的視覺化與互動設計，支援使用者在商品探索與供應商選擇過程中的分析與決策任務。研究同時結合使用者研究與專家訪談，評估系統於實務應用情境中的效益與潛力。

As market competition intensifies, procurement strategy has become one of the key factors determining business success. Compared to other industries, retail procurement faces more complex challenges. Procurement personnel must not only identify potential products with market value and develop new suppliers, but also continuously monitor and evaluate supplier performance. These tasks result in a need to process large amounts of information in the decision-making process. However, existing systems primarily focus on supplier evaluation, offering limited support for the exploratory phase at the early stage of retail procurement.
To understand the practical processes and needs of retail procurement, we conducted interviews with experienced procurement experts and derived system design requirements accordingly. Based on these requirements, we developed a decision support system named Procuvis, aiming to assist retail practitioners in the early-stage exploration and decision-making process. The system adopts glyph as its core method to integrate multi-dimensional data on market products and suppliers, and leverages various visualization and interactive designs to support market exploration and supplier selection tasks. Both user study and expert interviews were conducted to evaluate the system’s effectiveness and practical potential.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 v
圖目錄 vii
表目錄 ix
第一章 緒論 1
第一節　研究背景 1
第二節　研究問題 2
第二章 相關研究 4
第一節　企業採購的決策 4
第二節　零售業的採購決策流程與挑戰 5
第三節　市場狀況的視覺化 6
第四節　供應商的視覺化 7
第三章 系統設計 8
第一節　設計需求 8
第二節　資料 11
第三節　商品相似度與推薦計算 13
第四節　視覺化設計 16
第五節　Procuvis 23
第六節　使用情境 30
第四章 系統評估 36
第一節　使用者研究 36
第二節　專家訪談 59
第五章 結論與建議 67
第一節　研究結論 67
第二節　研究限制 68
參考文獻 71
附錄 82
附錄一　使用者研究之圖示理解任務 82
附錄二　使用者研究之直覺性問題 86
附錄三　使用者研究之偏好程度問題 89
附錄四　使用者研究決策任務之決策難度問題 92
附錄五　使用者研究決策任務之決策信心問題 93

Aggarwal, C. C., Hinneburg, A., & Keim, D. A. (2001). On the Surprising Behavior of Distance Metrics in High Dimensional Space. In J. Van Den Bussche & V. Vianu (Eds.), Database Theory—ICDT 2001 (Vol. 1973, pp. 420–434). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-44503-X_27
Ambergen, T. (2021). Visualizing supplier quality performance in an interactive dashboard [B.S. thesis]. University of Twente.
Bals, L., Hartmann, E., & Ritter, T. (2009). Barriers of purchasing departments’ involvement in marketing service procurement. Industrial Marketing Management, 38(8), 892–902. https://doi.org/10.1016/j.indmarman.2009.06.010
Bangor, A., Kortum ,Philip T., & and Miller, J. T. (2008). An Empirical Evaluation of the System Usability Scale. International Journal of Human–Computer Interaction, 24(6), 574–594. https://doi.org/10.1080/10447310802205776
Basole, R. C., Clear, T., Hu, M., Mehrotra, H., & Stasko, J. (2013). Understanding Interfirm Relationships in Business Ecosystems with Interactive Visualization. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2526–2535. https://doi.org/10.1109/TVCG.2013.209
Bharadwaj, N. (2004). Investigating the decision criteria used in electronic components procurement. Industrial Marketing Management, 33(4), 317–323. https://doi.org/10.1016/S0019-8501(03)00081-6
Borgo, R., Kehrer, J., Chung, D. H. S., Maguire, E., Laramee, R. S., Hauser, H., Ward, M., & Chen, M. (2013). Glyph-based Visualization: Foundations, Design Guidelines, Techniques and Applications.
Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability Evaluation in Industry, 189(194), 4–7.
Carr, A. S., & Pearson, J. N. (1999). Strategically managed buyer–supplier relationships and performance outcomes. Journal of Operations Management, 17(5), 497–519. https://doi.org/10.1016/S0272-6963(99)00007-8
Chen, M., Sun, Y., Fu, P., & Zhang, H. (2010). Image retrieval based on multi-feature similarity score fusion using genetic algorithm. 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2, 45–49. https://doi.org/10.1109/ICCAE.2010.5451373
Cleveland, W. S., & McGill, R. (1984). Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Association, 79(387), 531–554. https://doi.org/10.2307/2288400
Cui, Y., Che, W., Liu, T., Qin, B., Wang, S., & Hu, G. (2020). Revisiting Pre-Trained Models for Chinese Natural Language Processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, 657–668. https://www.aclweb.org/anthology/2020.findings-emnlp.58
Dimara, E., Bezerianos, A., & Dragicevic, P. (2018). Conceptual and Methodological Issues in Evaluating Multidimensional Visualizations for Decision Support. IEEE Transactions on Visualization and Computer Graphics, 24(1), 749–759. https://doi.org/10.1109/TVCG.2017.2745138
Dy, B., Ibrahim, N., Poorthuis, A., & Joyce, S. (2022). Improving Visualization Design for Effective Multi-Objective Decision Making. IEEE Transactions on Visualization and Computer Graphics, 28(10), 3405–3416. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2021.3065126
Fan, M., Lin, J., Chung, C., & Truong, K. N. (2019). Concurrent Think-Aloud Verbalizations and Usability Problems. ACM Trans. Comput.-Hum. Interact., 26(5), 28:1-28:35. https://doi.org/10.1145/3325281
France, S. L., & Ghose, S. (2016). An Analysis and Visualization Methodology for Identifying and Testing Market Structure. Marketing Science, 35(1), 182–197. https://doi.org/10.1287/mksc.2015.0958
Frej, E. A., de Almeida, A. T., & Costa, A. P. C. S. (2019). Using data visualization for ranking alternatives with partial information and interactive tradeoff elicitation. Operational Research, 19(4), 909–931. https://doi.org/10.1007/s12351-018-00444-2
Gemma Team. (2024). Gemma. https://doi.org/10.34740/KAGGLE/M/3301
Heer, J., & Shneiderman, B. (2012). Interactive dynamics for visual analysis: A taxonomy of tools that support the fluent and flexible use of visualizations. Queue, 10(2), 30–55.
Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16–24. https://doi.org/10.1016/j.ejor.2009.05.009
Huang, M., Huang, J., Song, M., & Fang, X. (2023). A Bibliometric Review on Supplier Evaluation and Selection. In W. Hong & Y. Weng (Eds.), Computer Science and Education (pp. 496–507). Springer Nature. https://doi.org/10.1007/978-981-99-2443-1_43
Johansson, U. (2001). Retail buying: Process, information and IT use: a conceptual framework. The International Review of Retail, Distribution and Consumer Research, 11(4), 329–357. https://doi.org/10.1080/713770609
Joshi, A., Kale, S., Chandel, S., & Pal, D. K. (2015). Likert Scale: Explored and Explained. British Journal of Applied Science & Technology, 7(4), Article 4. https://doi.org/10.9734/BJAST/2015/14975
Klug, B. (2017). An Overview of the System Usability Scale in Library Website and System Usability Testing. Weave: Journal of Library User Experience, 1(6). https://doi.org/10.3998/weave.12535642.0001.602
Kosara, R., Bendix, F., & Hauser, H. (2006). Parallel Sets: Interactive exploration and visual analysis of categorical data. IEEE Transactions on Visualization and Computer Graphics, 12(4), 558–568. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2006.76
Kun, C., Zehua, H., Rui’an, L., Xuejun, W., & Dongdong, S. (2024). Supplier Management Decision Support System Under Data Mining Algorithms. 2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), 848–856. https://doi.org/10.1109/EEBDA60612.2024.10485731
Laios, L. G., & Moschuris, S. J. (2001). The influence of enterprise type on the purchasing decision process. International Journal of Operations & Production Management, 21(3), 351–372. https://doi.org/10.1108/01443570110364687
Li, Y.-N., Li, D.-J., & Zhang, K. (2015). Metaphoric Transfer Effect in Information Visualization Using Glyphs. Proceedings of the 8th International Symposium on Visual Information Communication and Interaction, 121–130. https://doi.org/10.1145/2801040.2801062
Liu, H., & McGoldrick, P. J. (1996). International Retail Sourcing: Trend, Nature, and Process. Journal of International Marketing, 4(4), 9–33. https://doi.org/10.1177/1069031X9600400403
Lops, P., de Gemmis, M., & Semeraro, G. (2011). Content-based Recommender Systems: State of the Art and Trends. In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.), Recommender Systems Handbook (pp. 73–105). Springer US. https://doi.org/10.1007/978-0-387-85820-3_3
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018). UMAP: Uniform Manifold Approximation and Projection. Journal of Open Source Software, 3(29).
McIvor, R., Humphreys, P., & McAleer, E. (1997). The evolution of the purchasing function. Strategic Change, 6(3), 165–179. https://doi.org/10.1002/(SICI)1099-1697(199705)6:3%253C165::AID-JSC247%253E3.0.CO;2-6
Mooney, R. J., & Roy, L. (2000). Content-based book recommending using learning for text categorization. Proceedings of the Fifth ACM Conference on Digital Libraries, 195–204.
Mufida, M. K., & Kartikasari, D. (2018). Featured Product Mapping in Hinterland Karimun Area Using Interactive Visualization. 2018 International Conference on Applied Engineering (ICAE), 1–6. https://doi.org/10.1109/INCAE.2018.8579413
Mukherjee, K. (2016). Supplier selection criteria and methods: Past, present and future. International Journal of Operational Research, 27(1–2), 356–373. https://doi.org/10.1504/IJOR.2016.078470
N. L. P. Town. (2023). Bert-base-multilingual-uncased-sentiment (Revision edd66ab). Hugging Face. https://doi.org/10.57967/hf/1515
Nielsen, J., & Landauer, T. K. (1993). A mathematical model of the finding of usability problems. Proceedings of the INTERACT ’93 and CHI ’93 Conference on Human Factors in Computing Systems, 206–213. https://doi.org/10.1145/169059.169166
Nik Alwi, N. N. A., Hassan, N. H., Baharuden, A. F., Bakar, N. A. A., & Maarop, N. (2019). Data Visualization of Supplier Selection Using Business Intelligence Dashboard. In H. Badioze Zaman, A. F. Smeaton, T. K. Shih, S. Velastin, T. Terutoshi, N. Mohamad Ali, & M. N. Ahmad (Eds.), Advances in Visual Informatics (pp. 71–81). Springer International Publishing. https://doi.org/10.1007/978-3-030-34032-2_7
Omar, A., Davis-Sramek, B., Myers, M. B., & Mentzer, J. T. (2012). A Global Analysis of Orientation, Coordination, and Flexibility in Supply Chains. Journal of Business Logistics, 33(2), 128–144. https://doi.org/10.1111/j.0000-0000.2012.01045.x
Omurca, S. I. (2013). An intelligent supplier evaluation, selection and development system. Applied Soft Computing, 13(1), 690–697. https://doi.org/10.1016/j.asoc.2012.08.008
Parmar, D., Wu, T., Callarman, T., Fowler, J., & Wolfe, P. (2010). A clustering algorithm for supplier base management. International Journal of Production Research, 48(13), 3803–3821. https://doi.org/10.1080/00207540902942891
Paulraj, A., Chen, I. J., & Flynn, J. (2006). Levels of strategic purchasing: Impact on supply integration and performance. Journal of Purchasing and Supply Management, 12(3), 107–122. https://doi.org/10.1016/j.pursup.2006.08.002
Precision Business Insights. (2024). Household storage container market: By material, by product, by application and geography – Market estimation, dynamics, regional share, trends, competitors analysis 2017–2023 and forecast 2024–2030 [Industry report]. Precision Business Insights. https://www.precisionbusinessinsights.com/market-reports/household-storage-container-market
Rauch, E., Bataleblu, A. A., Golser, M., Emer, A., & Matt, D. T. (2024). Potential of Graph Database Visualization of the Supplier Network to Increase Resilience in Multi-tier Supply Chains. In J. Trojanowska, A. Kujawińska, I. Pavlenko, & J. Husar (Eds.), Advances in Manufacturing IV (pp. 125–139). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-56444-4_10
Ringel, D. M., & Skiera, B. (2016). Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data. Marketing Science, 35(3), 511–534. https://doi.org/10.1287/mksc.2015.0950
Roy, S. A., Ali, S. M., Kabir, G., Enayet, R., Suhi, S. A., Haque, T., & Hasan, R. (2020). A framework for sustainable supplier selection with transportation criteria. International Journal of Sustainable Engineering, 13(2), 77–92. https://doi.org/10.1080/19397038.2019.1625983
Sheth, J. N., Sharma, A., & Iyer, G. R. (2009). Why integrating purchasing with marketing is both inevitable and beneficial. Industrial Marketing Management, 38(8), 865–871. https://doi.org/10.1016/j.indmarman.2008.12.021
Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. Proceedings 1996 IEEE Symposium on Visual Languages, 336–343. https://doi.org/10.1109/VL.1996.545307
Sinar, E. F. (2015). Data Visualization. In Big Data at Work. Routledge.
Sodhi, M. S., & Son, B.-G. (2009). Supply-chain partnership performance. Transportation Research Part E: Logistics and Transportation Review, 45(6), 937–945. https://doi.org/10.1016/j.tre.2009.05.004
Stadtler, H. (2008). Purchasing and Material Requirements Planning. In H. Stadtler & C. Kilger (Eds.), Supply Chain Management and Advanced Planning: Concepts, Models, Software, and Case Studies (pp. 217–229). Springer. https://doi.org/10.1007/978-3-540-74512-9_12
Tan, P.-N., Steinbach, M., & Kumar, V. (2014). Introduction to data mining (New internat. edition). Pearson.
Telgen, J., & Sitar-Pop, C. E. (2001). Possible kinds of values added by the purchasing department. Proceedings of the 10th International Annual IPSERA Conference, 8-11 April 2001, Jonkoping, Sweden, 803–813. https://research.utwente.nl/en/publications/possible-kinds-of-values-added-by-the-purchasing-department
Thrulogachantar, P., & Zailani, S. (2011). The influence of purchasing strategies on manufacturing performance. Journal of Manufacturing Technology Management, 22(5), 641–663. https://doi.org/10.1108/17410381111134482
van Hoek, R., & Thomas, R. (2021). Notes and debate paper: Should merchandising and sourcing be worlds apart? The opportunity for more integrated strategic sourcing research. Journal of Purchasing and Supply Management, 27(1), 100659. https://doi.org/10.1016/j.pursup.2020.100659
Xideas, E., & Moschuris, S. (1998). The influence of product type on the purchasing structure. European Journal of Marketing, 32(11/12), 974–992. https://doi.org/10.1108/03090569810243587