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| Title: | 透過智慧製造系統導入提升營運績效：以A公司中國工廠為例
Performance Improvement through Smart Manufacturing System Implementation: A Case Study of a Chinese Factory of Company A |
| Authors: | 蕭麒元
Hsiao, Ci-Yuan |
| Contributors: | 羅明琇
Lo, Ming-Shiow
蕭麒元
Hsiao, Ci-Yuan |
| Keywords: | 智慧製造系統
數位轉型
人工智慧
機器人流程自動化(RPA）
自動光學檢測(AOI）
生產效率
Smart Manufacturing Systems
Digital Transformation
AI
RPA
AOI
Digital Transformation
Production Efficiency |
| Date: | 2025 |
| Issue Date: | 2025-08-04 13:49:24 (UTC+8) |
| Abstract: | 隨著製造業在數位創新的推動下快速轉型，智慧製造系統（Smart Manufacturing Systems, SMS）已成為應對勞動力短缺、客製化需求提升，以及提升營運效率等挑戰的關鍵解方。儘管關於SMS的理論潛力已有廣泛討論，但實際探討這些技術在工廠現場如何應用、產出哪些具體成效，以及在導入過程中面臨哪些挑戰的研究，實務佐證仍較缺乏。
本研究透過個案研究法，探討全球互連解決方案（Interconnect Solutions）供應商公司 A 的中國工廠中實際導入SMS與應用的情形。資料來源為與該公司數位轉型辦公室（Digital Transformation Office, DTO）成員的半結構式訪談，主要聚焦於人工智慧（AI）與機器人流程自動化（RPA）相關的整合實例。此外，為補充與交叉驗證訪談資料，本研究亦參考公司官方網站、公開企業報導與數位轉型相關文件，並針對部分訪談內容進行後續追問。透過多重資料來源的佐證，提升研究的可信度與資料完整性。
研究發現，導入如結合AI的自動光學檢測系統（Automated Optical Inspection, AOI）、後勤流程的機器人流程自動化（Robotic Process Automation, RPA）自動化、以及機器學習等SMS技術後，帶來了明顯的成效改善，包括產品缺陷率降低、檢測準確度提升，以及單月節省超過11,000個人工作天的重複性人力。儘管如此，導入過程中仍然面臨多項挑戰，例如員工抗拒新系統、訓練資源不足，以及面對產品規格頻繁變動時的系統適應困難等問題。
本研究參考實務產品生產過程，提供智慧製造系統導入過程中所面臨的機會與阻礙的深入觀察，期望能作為製造業者規劃數位轉型時的實務參考，也為相關學術研究提供第一線的資料。
As the manufacturing industry undergoes rapid transformation driven by digital innovation, Smart Manufacturing Systems (SMS) have become a key solution for addressing challenges like workforce shortage, increasing customization demands, and improving operational efficiency. While the theoretical potential of SMS is widely discussed, there remains a lack of empirical studies exploring how these technologies are applied in real-world factory settings, what measurable outcomes they produce and what kind of challenges appears during the implementation.
This study investigates the practical adoption and impact of SMS through a qualitative case study of a China-based factory operated by Company A, a global interconnect solutions provider. Data was collected via a semi-structured interview with a member of the company’s Digital Transformation Office (DTO), focusing on AI and Robotic Process Automation (RPA) integration. In addition, to supplement and cross-validate the interview data, this study also referenced the company’s official website, publicly available corporate reports, and documents related to digital transformation. Follow-up questions were conducted where necessary to clarify key points. The use of multiple data sources enhances the credibility and completeness of the research findings.
Findings showed that the adoption of SMS, such as AI-embedded Automated Optical Inspection (AOI), RPA for workflow automation, machine learning for process optimization and more, let to notable performance improvements. Improvements like measurable reduction in product defects, increased inspection accuracy, and the saving over 11,000 man-days in manual labor in a month. However, the implementation process also revealed several challenges, including employee resistance, limited staff training, and the complexity of adapting SMS to frequent product changes.
By examining SMS integration in a real-world production setting, this thesis provides grounded insights into the opportunities and obstacles of SMS adoption. The findings aim to support manufacturing practitioners considering similar transitions, while contributing to the academic discourse on digital transformation in industrial operations. |
| Reference: | Abd Al Rahman, M., & Mousavi, A. (2020). A review and analysis of automatic optical inspection and quality monitoring methods in electronics industry. Ieee Access, 8, 183192-183271.
Adelusi, J. (2024). Collaborative Robotics in Smart Manufacturing.
ADLINK Technology. (n.d.). AI vision system for contact lens inspection. ADLINK Technology. Retrieved June 16, 2025, from https://www.adlinktech.com/en/ai_vision_case_leda_contact_lens
Akintokunbo, O. O., & Adim, C. V. (2020). COVID-19 and supply chain disruption: A conceptual review. Asian Journal of Economics, Business and Accounting, 19(2), 40-47.
Alex, B., & Johnson, M. (2025). A Framework for IoT-Enabled Smart Manufacturing for Energy and Resource Optimization. arXiv preprint arXiv:2502.03040.
Amruthnath, N., & Gupta, T. (2018). A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. 2018 5th international conference on industrial engineering and applications (ICIEA),
Badmus, A. D. (2023). Implementing Technology Manufacturing with Robotics Process Automation (RPA). Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 2(2), 96-103.
Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 8211.
Corporation, S. (2021). Saki Develops Next Generation 3D AOI System with Artificial Intelligence (AI). IConnect007. Retrieved 12 May 2025 from https://iconnect007.com/index.php/article/129007/saki-develops-next-generation-3d-aoi-system-with-artificial-intelligence-ai-/129010?skin=SMT
Cui, Y., Meng, J., & Lu, C. (2018). Recent developments in China's labor market: Labor shortage, rising wages and their implications. Review of Development Economics, 22(3), 1217-1238.
Dassault Systèmes. (n.d.). The growing global skills shortage. Retrieved from https://www.3ds.com/manufacturing/trends/growing-global-skills-shortage
de Gea Fernández, J., Mronga, D., Günther, M., Knobloch, T., Wirkus, M., Schröer, M., Trampler, M., Stiene, S., Kirchner, E., & Bargsten, V. (2017). Multimodal sensor-based whole-body control for human–robot collaboration in industrial settings. Robotics and Autonomous Systems, 94, 102-119.
De Lemos, M. A., Junior, G. B., & Marques, M. A. (2007). Petri nets teaching in the Mechatronics Engineering course. IFAC Proceedings Volumes, 40(19), 106-111.
Deloitte. (2015). The Deloitte Consumer Review: Made to Order : The Rise of Mass Personalisation. Deloitte. https://books.google.com.tw/books?id=mRe3tQEACAAJ
Dilberoglu, U. M., Gharehpapagh, B., Yaman, U., & Dolen, M. (2017). The role of additive manufacturing in the era of industry 4.0. Procedia manufacturing, 11, 545-554.
Evjemo, L. D., Gjerstad, T., Grøtli, E. I., & Sziebig, G. (2020). Trends in smart manufacturing: Role of humans and industrial robots in smart factories. Current Robotics Reports, 1, 35-41.
foodprocessing. (2024). Priestley's Gourmet Delights opens new $53m AI-powered food facility. Retrieved 14 May 2025 from https://www.foodprocessing.com.au/content/the-food-plant/article/priestley-s-gourmet-delights-opens-new-53m-ai-powered-food-facilty-305074954
Gao, Z., Paul, A., & Wang, X. (2022). Guest editorial: Digital twinning: Integrating AI-ML and big data analytics for virtual representation. IEEE Transactions on Industrial Informatics, 18(2), 1355-1358.
Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary perspectives on complex systems: New findings and approaches, 85-113.
Gudivaka, B. R. (2022). Real-Time Big Data Processing and Accurate Production Analysis in Smart Job Shops Using LSTM/GRU and RPA. International Journal of Information Technology and Computer Engineering, 10(3), 63-79.
Hu, Y., Jia, Q., Yao, Y., Lee, Y., Lee, M., Wang, C., Zhou, X., Xie, R., & Yu, F. R. (2024). Industrial internet of things intelligence empowering smart manufacturing: A literature review. IEEE Internet of Things Journal.
Innovations, R.-T. (2015). Real Time Innovations Inc, Industrial Internet of Things, RTI FAQ. Retrieved May 28 2025 from https://info.rti.com/hubfs/docs/Industrial_IoT_FAQ.pdf
Ismail, H., Alkafri, A., Layous, M. S. J. A., & Mohanna, N. Modeling 2-R Serial Robotic Arm. Jardim-Goncalves, R., Romero, D., & Grilo, A. (2017). Factories of the future: challenges and leading innovations in intelligent manufacturing. International Journal of Computer Integrated Manufacturing, 30(1), 4-14.
Jean, J. H., Chen, C. H., Huang, T. B., & Tsai, S. H. (2014). Development of an Automatic Optical Inspection System and its Application to Defect Examination. Applied Mechanics and Materials, 479, 636-640.
Jiawang, H., Lurong, J., Suoming, Z., Renwang, L., Changguo, X., Xinxia, L., & Yongjian, S. (2023). Fast plug-in capacitors polarity detection with morphology and SVM fusion method in automatic optical inspection system. Signal, Image and Video Processing, 17(5), 2555-2563.
Kitron. (2021). Making the visual inspection smarter with artificial intelligence. Kitron. Retrieved 12 May 2025 from https://kitron.com/blog/visual-inspection-with-artificial-intelligence
Kuo, Y.-H., & Kusiak, A. (2019). From data to big data in production research: the past and future trends. International Journal of Production Research, 57(15-16), 4828-4853.
Lee, J., Davari, H., Singh, J., & Pandhare, V. (2018). Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing letters, 18, 20-23.
Liao, H.-C., Lim, Z.-Y., Hu, Y.-X., & Tseng, H.-W. (2018). Guidelines of automated optical inspection (AOI) system development. 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP),
Liu, H., & Wang, L. (2018). Gesture recognition for human-robot collaboration: A review. International Journal of Industrial Ergonomics, 68, 355-367.
Liu, Z., Liu, Q., Xu, W., Wang, L., & Zhou, Z. (2022). Robot learning towards smart robotic manufacturing: A review. Robotics and Computer-Integrated Manufacturing, 77, 102360.
Lu, S., Xu, C., Zhong, R. Y., & Wang, L. (2017). A RFID-enabled positioning system in automated guided vehicle for smart factories. Journal of Manufacturing Systems, 44, 179-190.
Luo, Y., Duan, Y., Li, W., Pace, P., & Fortino, G. (2018). Workshop networks integration using mobile intelligence in smart factories. IEEE Communications Magazine, 56(2), 68-75.
LuxCreo. (2022). How Sustainable is Additive Manufacturing with Global Supply Chain Disruptions? LuxCreo. Retrieved 15 May 2025 from https://luxcreo.com/how-sustainable-is-additive-manufacturing-with-global-supply-chain-disruptions-lc/
Madni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging digital twin technology in model-based systems engineering. Systems, 7(1), 7.
McFarland, F. (2024). SPEED FREAK BMW ‘human’ robot gets major upgrade with 400% speed increase as it’s tasked with production line duties. https://www.thesun.co.uk/tech/31928204/bmw-figure-robot-fleet-video-humanoid/
McKinsey & Company. (2021). Five fifty: The skillful corporation. https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/five-fifty-the-skillful-corporation
Mehami, J., Nawi, M., & Zhong, R. Y. (2018). Smart automated guided vehicles for manufacturing in the context of Industry 4.0. Procedia manufacturing, 26, 1077-1086.
Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B., Vosooghnia, A., Emamian, S. S., & Gisario, A. (2019). The potential of additive manufacturing in the smart factory industrial 4.0: A review. Applied Sciences, 9(18), 3865.
Mok, A. (2025). How AI and robotics can help prevent breakdowns in factories — and save manufacturers big bucks. https://www.businessinsider.com/artificial-intelligence-robotics-predictive-maintenance-manufacturing-factory-solutions-2025-5
Nagorny, K., Lima-Monteiro, P., Barata, J., & Colombo, A. W. (2017). Big data analysis in smart manufacturing: A review. International Journal of Communications, Network and System Sciences, 10(3), 31-58.
News, A. (2024). Priestley's Gourmet Delights opens new artificially intelligent robot powered factory in Queensland. Retrieved 14 May 2025 from https://www.abc.net.au/news/2024-05-28/qld-cake-factory-priestly-gourmet-delights-has-robot-bakers/103890182
Phuyal, S., Bista, D., & Bista, R. (2020). Challenges, opportunities and future directions of smart manufacturing: a state of art review. Sustainable Futures, 2, 100023.
Ruppert, T., Csalodi, R., & Abonyi, J. (2021). Estimation of machine setup and changeover times by survival analysis. Computers & Industrial Engineering, 153, 107026.
Sahoo, S., & Lo, C.-Y. (2022). Smart manufacturing powered by recent technological advancements: A review. Journal of Manufacturing Systems, 64, 236-250.
Shukla, N., Tiwari, M. K., & Beydoun, G. (2019). Next generation smart manufacturing and service systems using big data analytics. In (Vol. 128, pp. 905-910): Elsevier.
Theunissen, J., Xu, H., Zhong, R. Y., & Xu, X. (2018). Smart AGV system for manufacturing shopfloor in the context of industry 4.0. 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP),
Wallén, J. (2008). The history of the industrial robot. Linköping University Electronic Press.
Ward, B. (2024). IT/OT convergence success at Siemens’ Erlangen factory. https://blogs.sw.siemens.com/thought-leadership/2024/07/09/it-ot-convergence-success-at-siemens-erlangen-factory/
Xu, J., Kovatsch, M., Mattern, D., Mazza, F., Harasic, M., Paschke, A., & Lucia, S. (2022). A review on AI for smart manufacturing: Deep learning challenges and solutions. Applied Sciences, 12(16), 8239.
Yao, X., Zhou, J., Lin, Y., Li, Y., Yu, H., & Liu, Y. (2019). Smart manufacturing based on cyber-physical systems and beyond. Journal of Intelligent Manufacturing, 30, 2805-2817.
Yap, M. (n. d.). The Impact of AI in Manufacturing: Unleashing Productivity. Jabil. Retrieved 12 May 2025 from https://www.jabil.com/blog/artificial-intelligence-in-manufacturing.html |
| Description: | 碩士
國立政治大學
企業管理研究所(MBA學位學程)
112363105 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112363105 |
| Data Type: | thesis |
| Appears in Collections: | [企業管理研究所（MBA學位學程）] 學位論文
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