本研究旨在探討數位孿生防災演練系統於台灣製造產業中的應用現況、導入挑戰與商業化策略，並以X公司為個案，建構具可行性與永續發展性的商業模式。隨著人工智慧（AI）與數位模擬技術的快速發展，企業防災訓練正由傳統實體演練轉向虛實整合的智慧模擬模式。此轉型能有效提升訓練頻率與精準度，並降低演練成本與安全風險，代表製造業防災管理進入新階段。
　　本研究採質性個案研究法（Qualitative Case Study），整合PEST分析、波特五力分析、SWOT模型與商業模式畫布（BMC）等理論架構進行多層次分析。研究資料主要來自產業報告、政府政策文件、企業內部資料與專家深度訪談，研究期間涵蓋2023年至2025年。
　　研究結果顯示：（1）台灣製造產業對數位防災系統的需求快速上升，政策推動與技術成熟為主要驅動力；（2）導入初期挑戰包括成本負擔、組織抗拒及跨系統整合困難；數位孿生系統之核心價值在於模擬真實災害情境、量化訓練成果與提供即時回饋，可協助企業強化安全管理與應變韌性；（4）個案 X 公司應結合自身技術優勢與合作夥伴生態系，發展以「顧問導入、系統整合、教育訓練」並行之商業模式，以建立市場差異化定位並推動產業採用。
　　綜合而言，本研究提出防災演練系統於製造產業之商業模式架構，從理論與實務雙重角度探討市場機會與推廣策略，期能為台灣製造業之防災科技應用提供具體參考與發展方向。

This study aims to explore the application status, implementation challenges, and commercialization strategies of digital twin-based disaster drill systems within Taiwan's manufacturing industry. Using X Company as a case study, the research constructs a feasible and sustainable business model. With the rapid advancement of artificial intelligence (AI) and digital simulation technologies, corporate disaster preparedness training is evolving from traditional physical drills to integrated virtual-physical smart simulation models. This transformation effectively enhances training frequency and precision while reducing drill costs and safety risks, signifying a new stage in manufacturing disaster management.
Adopting a qualitative case study approach, this study integrates theoretical frameworks including PEST analysis, Porter’s Five Forces analysis, SWOT analysis, and the Business Model Canvas (BMC) to conduct a multi-level analysis. The research data is primarily derived from industry reports, government policy documents, internal corporate data, and in-depth expert interviews, covering the period from 2023 to 2025.
The results indicate that: (1) Demand for digital disaster preparedness systems in Taiwan's manufacturing sector is rising rapidly, driven primarily by policy incentives and technological maturity; (2) Initial implementation challenges include high cost burdens, organizational resistance, and cross-system integration difficulties; (3) The core value of digital twin systems lies in simulating realistic disaster scenarios, quantifying training outcomes, and providing real-time feedback, thereby assisting enterprises in strengthening safety management and response resilience; and (4) X Company should leverage its technical strengths and partner ecosystem to develop a business model that integrates consulting, system integration, and education training, thereby establishing market differentiation and driving industry adoption.
In conclusion, this study proposes a business model framework for disaster drill systems in the manufacturing industry. By exploring market opportunities and promotion strategies from both theoretical and practical perspectives, the study aims to provide concrete references and development directions for the application of disaster prevention technologies in Taiwan's manufacturing sector.

• 工研院產科國際所 (2025)。2025 台灣智慧製造產業白皮書。工業技術研究院。
• 工業技術研究院。（2024）。〈數位孿生技術在韌性製造與防災之應用〉。取自工研院產業情報網 (IEKNet)。
• 勞動部職業安全衛生署 (2023)。職業安全衛生法規彙編。勞動部。
• 鍾启駿 (2025)。專家訪談紀錄：數位孿生防災演練系統之需求與挑戰 [未出版之原始數據]。國立政治大學。

• Aguilar, F. J. (1967). Scanning the Business Environment. New York, NY: Macmillan.
• Caggianese, G., Gallo, L., & Neroni, P. (2020). Smart and safe: A VR-based training system for risk management in industrial plants. IEEE Access, 8, 105474–105488. https://doi.org/10.1109/ACCESS.2020.2999871
• Deloitte. (2023). 2023 Smart Manufacturing Logistics Report. Deloitte Insights.
• Dassault Systèmes. (2023). Virtual Twins for Resilient Cities: Leveraging Data for Urban Planning and Disaster Prevention. Dassault Systèmes White Paper.
• Grieves, M. (2002). Origins of the Digital Twin Concept. University of Michigan, PLM Center.
• Grieves, M. (2014). Digital Twin: Manufacturing Excellence through Virtual Factory Replication. White Paper, Michael W. Grieves, LLC.
• Garcia, L., et al. (2022). “Simulation-based safety training effectiveness in process industries.” Safety Science Journal, 150, 105–118.
• Humphrey, A. S. (2005) Albert Humphrey. SWOT analysis for management consulting. SRI Alumni Association Newsletter, 1, 7–8.
• IDC. (2023). The Cost of Unplanned Downtime in Manufacturing: Sector-Specific Analysis and Mitigation Strategies. Framingham, MA: International Data Corporation.
• International Association of Emergency Management (IAEM). (2023). Definitions of Emergency Management. IAEM.
• International Labour Organization (ILO). (2023). World Employment and Social Outlook: Trends 2023. ILO.
• NVIDIA. (2025). AI-Driven Digital Twins: Enhancing Predictive Accuracy and Response Efficiency in Industrial Safety. NVIDIA Technical White Paper.
• Occupational Safety and Health Administration (OSHA). (2022). Recommended Practices for Safety and Health Programs. U.S. Department of Labor.
• Osterwalder, A., & Pigneur, Y. (2010). Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. John Wiley & Sons.
• PwC. (2022). Digital Factories 2022: Reimagining the Future of Manufacturing. PwC Global.
• Porter, M. E. (1979) Michael Porter. How Competitive Forces Shape Strategy. Harvard Business Review, 57(2), 137-145.
• Siemens. (2023). Industrial Metaverse: The Future of Manufacturing. Siemens AG.
• TCI Network. (2024). AI CloudXR Intelligent Simulation Training System for High-Risk Industries. Retrieved from TCI Network Industry Report.
• Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Thousand Oaks, CA: Sage Publications.