| dc.contributor.advisor | 羅明琇 | zh_TW |
| dc.contributor.advisor | Lo, Ming-Shiow | en_US |
| dc.contributor.author (Authors) | 張家瑋 | zh_TW |
| dc.contributor.author (Authors) | Chang, Chia-Wei | en_US |
| dc.creator (作者) | 張家瑋 | zh_TW |
| dc.creator (作者) | Chang, Chia-Wei | en_US |
| dc.date (日期) | 2025 | en_US |
| dc.date.accessioned | 4-Aug-2025 13:07:44 (UTC+8) | - |
| dc.date.available | 4-Aug-2025 13:07:44 (UTC+8) | - |
| dc.date.issued (上傳時間) | 4-Aug-2025 13:07:44 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0112932439 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/158363 | - |
| 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) | 112932439 | zh_TW |
| dc.description.abstract (摘要) | 本研究以台灣一家中型汽車外觀零件製造企業為個案，探討該企業在全球汽車產業邁向電動化與智慧化的背景下，如何藉由數位轉型與智慧製造導入，以回應產業挑戰與市場壓力。研究透過深度訪談與系統資料分析，從生產管理、資訊整合、組織升級與公司治理四大構面切入，提出具體可行的五年轉型行動方案。
研究結果顯示，透過人工智慧（AI, Artificial Intelligence）預測模型、製造執行系統（MES, Manufacturing Execution System）、自動化設備與物聯網技術（IoT, Internet of Things）的導入，企業成功提升出貨效率與生產準確性，並降低次品率與物料浪費。組織層面則導入扁平化管理、關鍵績效指標制度（KPI, Key Performance Indicator）與數位學習平台，有效促進內部溝通與人才發展。同時，針對家族企業的接班挑戰，企業逐步導入制度化治理與永續經營觀念，轉型為以數據為基礎的決策模式。
本研究的貢獻在於建構出涵蓋「智慧製造」、「數據治理」、「組織升級」與「家族傳承」的整合性轉型藍圖，為台灣中小型汽車零件企業在產業變遷中提供具參考價值之實務建議。 | zh_TW |
| dc.description.abstract (摘要) | This study investigates the digital transformation and adoption of smart manufacturing in a mid-sized Taiwanese automotive exterior parts manufacturer. Against the backdrop of global trends toward electrification and intelligence in the automotive industry, the company has proactively introduced digital tools and smart technologies to respond to industrial challenges and market pressures.
Using in-depth interviews and systematic data analysis, this research addresses four key dimensions: production management, information integration, organizational upgrading, and corporate governance. Based on these dimensions, a practical and feasible five-year transformation action plan is proposed.
The findings reveal that the integration of AI-based forecasting models, MES systems, automation equipment, and IoT technologies has significantly enhanced shipping efficiency and production accuracy while reducing defect rates and material waste. At the organizational level, the introduction of flat management structures, KPI-driven performance evaluation, and digital learning platforms has effectively improved internal communication and talent development. Moreover, in response to the succession challenges common in family-run enterprises, the company has gradually adopted institutionalized governance and sustainability principles, transitioning to a data-driven decision-making approach.
The key contribution of this study is the development of a comprehensive transformation blueprint that integrates smart manufacturing, data governance, organizational advancement, and family succession planning. It offers practical insights for small and medium-sized enterprises in Taiwan’s automotive parts industry undergoing similar industrial transitions. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 5
第三節 研究目的 7
第二章 個案公司介紹 8
第一節 創業初期與市場開拓（1990年代） 9
第二節 技術精進與品牌建立（2000-2010年） 10
第三節 產品線擴展與國際化布局（2010-2020年） 11
第四節 數位轉型與新產品線發展（2020年至今） 12
第三章 企業面臨的挑戰 13
第一節生產流程的挑戰 13
第二節 數位轉型的阻力 15
第三節 組織管理與人才發展的困境 17
第四節 家族傳承與接班問題 19
第四章 企業未來五年轉型計劃 21
第一節 生產流程優化：打造智慧工廠，提升生產彈性與效率 22
第二節數位轉型：從數據導向決策到全面智慧化運營 22
第三節 組織管理與人才發展：打造高效能與創新驅動的團隊 23
第四節 家族企業傳承 24
第五章 產業轉型競爭力與永續策略 26
第一節 產業變局下的競爭力重構 26
第二節數位化能力與營運升級的策略重點 28
第三節組織演化與人才永續策略 31
第四節從ESG治理走向產業永續發展 34
第五節整合性轉型藍圖與預期效益 36
第六章 結語 38
第一節 研究發現 38
第二節 研究貢獻與未來研究方向 38
參考文獻 41 | zh_TW |
| dc.format.extent | 1279950 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0112932439 | en_US |
| dc.subject (關鍵詞) | 數位轉型 | zh_TW |
| dc.subject (關鍵詞) | 智慧製造 | zh_TW |
| dc.subject (關鍵詞) | 汽車零件產業 | zh_TW |
| dc.subject (關鍵詞) | 家族企業治理 | zh_TW |
| dc.subject (關鍵詞) | 供應鏈管理 | zh_TW |
| dc.subject (關鍵詞) | Digital transformation | en_US |
| dc.subject (關鍵詞) | Smart manufacturing | en_US |
| dc.subject (關鍵詞) | Automotive parts industry | en_US |
| dc.subject (關鍵詞) | Family business governance | en_US |
| dc.subject (關鍵詞) | Supply chain management | en_US |
| dc.title (題名) | 企業數位轉型下營運流程與治理模式整合之研究 — 以台灣汽車外觀零件製造企業為例 | zh_TW |
| dc.title (題名) | A study on the integration of operational processes and governance models under enterprise digital transformation: a case study of a Taiwanese automotive exterior parts manufacturer | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 中文部分:
經濟日報，(2024)。汽車產業鏈啟動轉型拚晉升兆元產業。取自：https://money.udn.com/money/story/5612/8269017
台灣區車輛工業同業公會，(2024)。台灣汽車產業概況。取自：https://www.ttvma.org.tw/industry
英文部分:
Akabot, (2024), Automation & digital transformation in the Taiwanese business community in 2024, from: https://akabot.com/additional-resources/blog/automation-digital-transformation-in-the-taiwanese-business-community-in-2024
Digitimes, (2024), Taiwan dominates global automotive aftermarket parts supply, from: https://www.digitimes.com/news/a20241210PD201/automotive-aftermarket-digitimes-taiwan-2024.html
Industrial Automation India, (2024), Taiwan’s auto parts industry growth driven by adaptable manufacturing and competitive pricing in US market, from: https://industrialautomationindia.in/news/taiwan-auto-parts-industry-growth-adaptable-manufacturing-competitive-pricing-us-market
Spherical Insights, (2024), Global Automotive Industry Market Size To Exceed USD 6,861.45 Billion By 2033, CAGR Of 6.77%, GlobeNewswir, from: https://www.globenewswire.com/news-release/2024/03/14/2846022/0/en/Global-Automotive-Industry-Market-Size-To-Exceed-USD-6-861-45-Billion-By-2033-CAGR-Of-6-77.html
Taiwan News, (2024), Auto parts and components market in Asia-Pacific to reach USD 79.13 billion by 2030, from: https://www.taiwannews.com.tw/en/news/6078950
The Business Research Company, (2025), Automotive aftermarket global market report 2025,from:https://www.thebusinessresearchcompany.com/report/automotive-aftermarket-global-market-report?utm_source=chatgpt.com | zh_TW |
