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研究生: 巫俊良
Wu, Chun-Liang
論文名稱: 鋼筋加工廠智慧轉型之個案研究
A Case Study on the Digital Transformation of a Rebar Processing Plant
指導教授: 邱奕嘉
口試委員: 巫立宇
李岱砡
學位類別: 碩士
Master
系所名稱: 商學院 - 經營管理碩士學程(EMBA)
Executive Master of Business Administration(EMBA)
論文出版年: 2026
畢業學年度: 114
語文別: 中文
論文頁數: 48
中文關鍵詞: 智慧製造數位轉型資訊整合營運績效個案研究
外文關鍵詞: Smart Manufacturing, Digital Transformation, Information Integration, Operational Performance, Case Study
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  • 本研究以臺灣某鋼筋加工廠為個案,探討其推動智慧製造轉型之規劃歷程、導入機制與營運成效,並分析智慧轉型對傳統工程導向加工產業之管理模式與營運結構所產生之影響。研究採單一個案研究法,透過文獻探討、現場觀察、內部營運資料與系統報表分析,建構智慧轉型成效評估架構,並以導入前後一定期間之平均營運資料進行比較分析。
    研究結果顯示,智慧轉型之影響呈現由內而外之遞進關係。首先,資訊即時性構面改善最為顯著,任務狀態同步時間與異常處理時效大幅縮短,資訊流由延遲回報轉為即時更新,重塑決策節奏與管理模式。其次,生產效率與人力效能構面隨資訊整合而逐步提升,設備稼動率、派工達成率與人均產出效率同步改善,且產出提升並未伴隨規模擴張,顯示效率來自既有資源配置優化。人力面向則呈現工時下降與跨站協作提升之能力結構轉變。最終,內部流程穩定性進一步外溢至顧客服務層面,準時出貨率與客戶滿意度提升,展現智慧轉型之市場價值具有漸進與累積特性。
    本研究進一步與數位轉型與Industry 4.0相關理論進行對應,指出資訊整合為智慧轉型之基礎機制,而技術導入需結合組織調整與管理能力建構,方能轉化為營運績效。相較於多數聚焦大型企業之研究,本研究提供傳統中小型加工產業之實證案例,補充智慧製造於低數位成熟度情境下之轉型路徑,對實務界與學術研究均具參考價值。


    This study investigates the digital transformation process of a rebar processing plant in Taiwan, focusing on its implementation mechanisms, operational impacts, and managerial implications. Adopting a single-case study approach, the research integrates literature review, field observation, internal operational records, and system-generated data to construct a performance evaluation framework. A comparative analysis was conducted using averaged operational data from pre- and post-implementation periods to assess structural changes while minimizing short-term fluctuations.
    The findings reveal a progressive transformation pattern from internal operational improvements to external market performance. Information responsiveness exhibited the most significant improvement, with substantial reductions in task synchronization time and anomaly handling duration. The transition from delayed reporting to real-time updates fundamentally reshaped decision-making rhythms and managerial control. Subsequently, production efficiency and workforce effectiveness improved as a result of enhanced information integration. Increases in equipment utilization, task completion rates, and per capita productivity were achieved without large-scale expansion of labor or equipment, indicating that efficiency gains stemmed from optimized resource allocation rather than scale enlargement. Workforce outcomes further reflected structural capability upgrading, characterized by reduced overtime and increased cross-functional collaboration. Ultimately, improvements in internal process stability extended to customer service performance, as evidenced by higher on-time delivery rates and customer satisfaction, suggesting that the market value of digital transformation accumulates gradually over time.
    By aligning the findings with digital transformation and Industry 4.0 theories, this study highlights that information integration serves as the foundational mechanism of smart transformation. Technological adoption must be accompanied by organizational adjustments and managerial capability development to translate digital investments into sustainable operational performance. Unlike many prior studies focusing on large enterprises, this research contributes an empirical case from a traditional small-to-medium-sized processing industry, enriching the understanding of digital transformation pathways in low digital maturity contexts.

    第一章 緒論 1
    第一節 研究背景與動機 1
    第二節 研究目的與問題 1
    第三節 研究對象與範圍 2
    第四節 研究流程與架構 2
    第二章 文獻探討 4
    第一節 數位轉型與智慧製造理論 4
    第二節 製造業智慧化架構 4
    第三節 關鍵技術與應用探討 6
    第四節 國內外案例回顧 6
    第五節 智慧轉型成功關鍵因素 9
    第三章 研究方法 11
    第一節 研究設計與方法說明 11
    第二節 資料來源與分析工具 11
    第三節 個案研究步驟與流程 13
    第四章 個案公司介紹 15
    第一節 公司背景與營運概況 15
    第二節 組織架構與現有生產模式 15
    第三節 現行流程與主要痛點 16
    第五章 智慧轉型分析 23
    第一節 轉型需求診斷 23
    第二節 規劃架構與導入技術內容 24
    第三節 實施過程與挑戰因應 25
    第四節 智慧轉型成效評估構面與指標設計 26
    第六章 研究結果與討論 29
    第一節 智慧轉型前後差異分析 29
    第二節 成效與理論之對應探討 36
    第三節 持續改善方向與轉型策略反思 38
    第七章 結論與建議 40
    第一節 研究結論 40
    第二節 對實務界的建議 41
    第三節 研究限制 43
    第四節 未來研究建議 44
    參考文獻 45

    一、中文文獻
    向德成(2020)。現在台灣工廠智動化需求。機械工業雜誌,(446),2-6。
    https://doi.org/10.30256/JIM.202005_(446).0002
    許朝詠、陸振原、吳崇勇(2023)。近代人工智慧技術於鋼鐵製程之應用。工程,96(4),64-79。取自
    https://www.cie.org.tw/var/file/0/1000/img/2/293714256.pdf?utm_source
    陳靜波、陳羿銘、林怡瑾、解明潔、吳順福、李偉瀚、余允誌(2024)。智慧製造軟體發展及應用。機械工業雜誌,(500),58-65。
    曾文光(2019)。如何運用MES系統構建智慧製造應用管理-以金屬加工行業為應用案例。機械工業雜誌,(440),81-89。https://doi.org/10.30256/JIM.201911_(440).0014
    楊淑慧、馮麗珍、蔡明城、鄒博年(2020)。台灣工廠智動化技術與案例介紹。機械工業雜誌,(446),17-21。https://doi.org/10.30256/JIM.202005_(446).0005
    簡榮茂(2022)。企業營運及製造之資訊系統導入規劃簡介。機械工業雜誌,(470),12-18。https://doi.org/10.30256/JIM.202205_(470).0004
    https://doi.org/10.30256/JIM.202411_(500).0010
    二、英文文獻
    Arsic, A., Lukovic, M., & Ducic, N. (2025). MESWARM: A modular and AI-driven manufacturing execution system for Industry 4.0. High Technologies. Business. Society, X(2), 47–52. Retrieved from https://stumejournals.com/journals/i4/2025/2/47.full.pdf
    Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482. https://doi.org/10.25300/MISQ/2013/37:2.3
    Dieguez, T., Malheiro, M. T., Leal, N., & Machado, J. (2025, June). Systematic literature review on manufacturing execution systems in the era of Industry 4.0: A bibliometric analysis. In J. Machado, J. Trojanowska, K. Antosz, C. P. Leão, L. Knapcikova, & A. Sover (Eds.), Innovations in Industrial Engineering IV. Proceedings of the International Conference Innovation in Engineering (pp. 298–310). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-94484-0_24
    Dieguez, T., & Machado, J. (2025). Reviewing Manufacturing Execution System in Industry 4.0: A Global Approach . EAI Endorsed Transactions on Digital Transformation of Industrial Processes, 1(3). https://doi.org/10.4108/dtip.9768
    El Zant, C., Charrier, Q., Benfriha, K., & Le Men, P. (2020, June). Enhanced manufacturing execution system “MES” through a smart vision system. In International Joint Conference on Mechanics, Design Engineering & Advanced Manufacturing (pp. 329-334). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-70566-4_52
    Hu, Z. (2025). A Data-Driven Framework for Cloud MES Implementation in Smart Manufacturing Environments. International Journal of Advance in Applied Science Research, 4(8), 80-85. Retrieved from https://h-tsp.com/index.php/ijaasr/article/view/132
    Imoize, A. L., Afolabi, O. S., Ojo, M. O., Adedeji, K. B., & Djouani, K. (2021). Performance measurement system and quality management in data-driven Industry 4.0: A review. Sensors, 22(1), 224. https://doi.org/10.3390/s22010224
    Jabir, S. A. (2025, November 20). AI-RPA integration in cloud-based MES for smart production (Master’s thesis, University of Vaasa, Finland). Industrial Systems Analytics. Retrieved from https://urn.fi/URN:NBN:fi-fe20251120109724
    Janković, N., Nikolić, I., & Rajković, T. (2025). Digital and digital transformation key performance indicators: A systematic literature review. In V. Štavljanin, I. Mijatović, & I. Luković (Eds.), Unlocking the hidden potentials of organization through merging of humans and digitals: SymOrg 2024 (Vol. 1680, pp. 168–184). Lecture Notes in Networks and Systems. Springer. https://doi.org/10.1007/978-3-032-08093-6_11
    Mantravadi, S., Li, C., & Møller, C. (2019). Multi-agent Manufacturing Execution System (MES): Concept, Architecture & ML Algorithm for a Smart Factory Case. Proceedings of the 21st International Conference on Enterprise Information Systems, 477-482. https://doi.org/10.5220/0007768904770482
    Mantravadi, S., Møller, C., & Schnyder, R. (2022). Design choices for next-generation IIoT-connected MES/MOM: An empirical study on smart factories. Robotics and Computer-Integrated Manufacturing, 73, 102225. https://doi.org/10.1016/j.rcim.2021.102225
    Merriam, S. B. (1998). Qualitative research and case study applications in education. Jossey-Bass Publishers.
    Müller, J. M., Buliga, O., & Voigt, K. I. (2018). Fortune favors the prepared: How SMEs approach Industry 4.0. Technological Forecasting and Social Change, 132, 2–17.
    https://doi.org/10.1016/j.techfore.2017.12.019
    Okpala, C. C., Egwuatu-Elem, I. C., & Nwamekwe, C. O. (2025). Integrating artificial intelligence and time-series forecasting for smart textile production: Trends, challenges, and opportunities in the Industry 4.0 era. International Journal of Society Reviews (INJOSER), 3(2), 461–477. Retrieved from https://hal.science/hal-05402236v1
    Pozzi, R., Rossi, T., & Secchi, R. (2023). Industry 4.0 technologies: Critical success factors for implementation and improvements in manufacturing companies. Production Planning & Control, 34(2), 139–158. https://doi.org/10.1080/09537287.2021.1891481
    Pelletier, C., L'ecuyer, F., & Raymond, L. (2025). Building organizational agility through digital transformation: a configurational approach in SMEs. Industrial Management & Data Systems, 125(4), 1503-1529. https://doi.org/10.1108/IMDS-05-2024-0488
    Rocha, C. F., Quandt, C., Deschamps, F., & Cruzara, G. (2025). Digital transformation readiness in large manufacturing firms: A building block model proposition. Journal of Manufacturing Technology Management, 36(1), 45–68. https://doi.org/10.1108/JMTM-12-2023-0544
    Sagala, G. H., & Őri, D. (2024). Toward SMEs digital transformation success: A systematic literature review. Information Systems and E-Business Management, 22, 667–719.
    https://doi.org/10.1007/s10257-024-00682-2
    Stake, R. E. (1995). The art of case study research. Sage Publications.
    Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901.
    https://doi.org/10.1016/j.jbusres.2019.09.022
    Vial, G. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118–144.
    https://doi.org/10.1016/j.jsis.2019.01.003
    Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349.
    https://doi.org/10.1016/j.lrp.2018.12.001
    Xu, X., Xu, L. D., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941–2962.
    https://doi.org/10.1080/00207543.2018.1444806
    Yoo, Y., Henfridsson, O., & Lyytinen, K. (2012). The new organizing logic of digital innovation: An agenda for information systems research. Information Systems Research, 23(4), 1398–1408. https://doi.org/10.1287/isre.1120.0456
    Zheng, P., Wang, H., Sang, Z., Zhong, R. Y., Liu, Y., Liu, C., & Xu, X. (2018). Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Frontiers of Mechanical Engineering, 13(2), 137–150.
    https://doi.org/10.1007/s11465-018-0499-5

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