| dc.contributor.advisor | 羅明琇<br>陳冠儒 | zh_TW |
| dc.contributor.author (Authors) | 陳俐妏 | zh_TW |
| dc.contributor.author (Authors) | Chen, Li-Wen | en_US |
| dc.creator (作者) | 陳俐妏 | zh_TW |
| dc.creator (作者) | Chen, Li-Wen | en_US |
| dc.date (日期) | 2025 | en_US |
| dc.date.accessioned | 1-Jul-2025 14:24:17 (UTC+8) | - |
| dc.date.available | 1-Jul-2025 14:24:17 (UTC+8) | - |
| dc.date.issued (上傳時間) | 1-Jul-2025 14:24:17 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0112932090 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/157703 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 經營管理碩士學程(EMBA) | zh_TW |
| dc.description (描述) | 112932090 | zh_TW |
| dc.description.abstract (摘要) | 隨著人工智慧 (Artificial Intelligence, 以下簡稱 AI) 技術的快速演進與廣泛應用，邊緣AI逐漸成為推動各產業升級與實現數位轉型的重要驅動力。邊緣AI是指將AI運算資源部署於接近資料來源的邊緣裝置，透過即時資料處理與地端決策反應，達成低延遲、高效能與降低雲端依賴的效益。此特性使其特別適用於高度分散、對即時性要求高或網路條件不穩定的場域，包括智慧製造、交通監控、醫療照護、自駕車、零售場域等。隨著晶片效能的提升、開發工具的普及與通訊技術如5G與物聯網的成熟，邊緣AI已從實驗階段逐步邁入商業化應用，並促成整體產業結構的重組與新商機的誕生。該產業具有高度生態系整合的特性，涵蓋晶片與感測器、系統整合、軟體開發，到應用服務，形成多元且緊密的產業鏈。本研究以一中小型企業為個案，該公司原先專注於工業電腦級顯示卡產品，然而在面對AI產業崛起與營運隱憂的挑戰下，積極展開轉型行動。透過產品與業務策略逐步建構邊緣AI應用所需的完整解決方案，成功由傳統硬體供應商轉型為具備邊緣AI整合能力的解決方案提供者。儘管資源不如大型企業充沛，個案公司憑藉對市場趨勢的敏銳洞察、靈活的產品設計能力、彈性的組織調整及營運策略，逐步克服轉型過程中的資源侷限與產業競爭壓力，並與多家國內外指標性AI企業建立長期合作關係，順利切入產業價值鏈核心。本研究透過深入分析個案公司轉型歷程，探討其如何因應產業變遷與內部營運挑戰、，並從產品策略、業務 式、組織架構、營運流程與資訊系統建置等多方面著手調整，逐步提升客戶服務能力與整體營運效率。研究結果有助於理解中小型企業如何於新興的產業環境中尋找突破口，並提供其他企業在邁向AI應用轉型過程中可參考之策略 式與實務啟示。 | zh_TW |
| dc.description.abstract (摘要) | With the rapid advancement and widespread application of Artificial Intelligence (AI) technologies, Edge AI has increasingly become a critical driver for industrial upgrades and digital transformation. Edge AI refers to the deployment of AI computing resources at the proximity of data sources, enabling real-time data processing and on-site decision-making to achieve low latency, high performance, and reduced reliance on cloud services. These characteristics make Edge AI particularly suitable for highly distributed environments that demand real-time responsiveness or have unstable network conditions, such as smart manufacturing, traffic monitoring, healthcare, autonomous vehicles, and retail sectors. With improvements in chip performance, the proliferation of development tools, and the maturation of communication technologies like 5G and IoT, Edge AI has gradually transitioned from the experimental stage to commercial applications, fostering industry restructuring and new business opportunities. The Edge AI industry is characterized by a highly integrated ecosystem encompassing chips and sensors, system integration, software development, and application services, forming a diverse and tightly interconnected value chain.
This study investigates a Small-to-Medium-sized Enterprise (SMEs) that initially specialized in industrial-grade graphics cards but proactively embarked on a transformation journey in response to the rise of the AI industry and emerging operational challenges. By progressively developing comprehensive solutions tailored for Edge AI applications through strategic product and business initiatives, the company successfully transitioned from a traditional hardware supplier to a solutions provider with Edge AI integration capabilities. Despite limited resources compared to large enterprises, the case company leveraged its keen market insights, flexible product design, agile organizational adjustments, and operational strategies to
overcome resource constraints and competitive pressures during the transformation process. It also established long-term partnerships with several leading domestic and international AI companies, securing a position within the core of the industry value chain.
Through an in-depth analysis of the company's transformation journey, this study explores how it addressed industry shifts and internal operational challenges by implementing strategic adjustments across product strategy, business models, organizational structure, operational processes, and information system development. The findings provide valuable insights into how SMEs can identify breakthrough opportunities within emerging industries and offer strategic patterns and practical implications for enterprises undergoing AI-driven transformations. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第二章 產業介紹 4
第一節 邊緣AI產業 4
第二節 邊緣AI產業未來趨勢 8
第三章 個案公司簡介 11
第一節 個案公司背景 11
第二節 個案公司商業模式 14
第四章 個案公司的挑戰 22
第一節 轉型契機內外部分析 22
第二節 商業模式帶領轉型 . 25
第三節 轉型挑戰從組織架構分析 29
第四節 轉型挑戰從供應鏈營運分析 32
第五節 轉型過程中的難題 49
第六節 轉型效益改善 52
第五章 結論 54
第一節 研究發現 54
第二節 研究貢獻 58
第三節 未來研究建議 59
參考文獻 61 | zh_TW |
| dc.format.extent | 1160436 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0112932090 | en_US |
| dc.subject (關鍵詞) | 邊緣AI | zh_TW |
| dc.subject (關鍵詞) | 數位轉型 | zh_TW |
| dc.subject (關鍵詞) | 中小企業轉型 | zh_TW |
| dc.subject (關鍵詞) | 解決方案提供者 | zh_TW |
| dc.subject (關鍵詞) | 生態系 | zh_TW |
| dc.subject (關鍵詞) | EdgeAI | en_US |
| dc.subject (關鍵詞) | Digital Transformation | en_US |
| dc.subject (關鍵詞) | SMEs Transformation | en_US |
| dc.subject (關鍵詞) | Solutions Provider | en_US |
| dc.subject (關鍵詞) | Ecosystem | en_US |
| dc.title (題名) | 從硬體供應商到邊緣AI解決方案提供者 : 以營運效率與客戶滿意度為導向的個案分析 | zh_TW |
| dc.title (題名) | From Hardware Supplier to Edge AI Solution Provider: A Case Study Oriented Toward Operational Efficiency and Customer Satisfaction | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 1. 數位時代.（2024年3月12日）。Edge AI 為何成為雲端 AI 的重要替代？從優勢、挑戰到關鍵應用一次看懂。https://www.bnext.com.tw/article/80846/edge-ai-cloud-ai
2. Allied Market Research. (2021). Edge AI Hardware Market Outlook – 2030. 取自 https://www.alliedmarketresearch.com/edge-ai-hardware-market-A13111
3. Precedence Research. (2024). Edge AI Market Size, Share and Trends 2024 to 2034. 取自 https://www.precedenceresearch.com/edge-ai-market
4. The Business Research Company. (2024). Edge AI Software Market Outlook 2025-2034: Key Trends, Growth. 取自https://www.openpr.com/news/3850177/edge-ai-software-market-outlook-2025-2034-key-trends-growth
5. CB Insights. (2024, March 27). AI 100: The most promising AI startups of 2024. 取自https://www.cbinsights.com/research/report/artificial-intelligence-top-startups-2024/
5. Product Service Innovation. (2010). Strategic options: Product vs. Service centric | zh_TW |
