面對全球半導體市場的高度波動與供應鏈不確定性，企業在追求營運韌性與競爭優勢的過程中，存貨管理策略扮演著至關重要的角色。尤其在半導體設備製造產業中，產品高度客製化、物料週期長、零組件單價高，使得精準的存貨配置與靈活的供應鏈調度更具挑戰性。
本研究以A公司為個案，結合文獻探討、專家訪談與層級分析法（Analytic Hierarchy Process, AHP），建構涵蓋「需求管理」、「庫存管理」、「供應鏈協同管理」與「智能化技術管理」四大構面、共十一項評估準則之量化決策架構。透過15位具備半導體產業或供應鏈實務經驗之專家問卷調查，計算各構面及細項指標之權重，並通過一致性檢驗（C.R.值<0.1），確保結果具科學性與信效度。
AHP分析結果顯示：「需求監控與動態調整」為最具關鍵性指標，其次為「需求驅動的物料管理」、「安全庫存管理」與「關鍵物料管理」，反映企業當前更重視以市場需求為導向的動態補貨能力與高風險物料之備援策略。相對而言，「智能技術與自動化應用」排名最低，顯示AI與IoT等技術雖具潛力，但實務應用仍處導入初期，未能形成關鍵決策工具。
綜合AHP結果與A公司現況對照，並透過SWOT分析歸納三大優化路徑，包括：「預測優化與技術導入」、「強化安全庫存與關鍵物料管理」、「深化供應鏈協同關係」。本研究不僅具體指出半導體存貨優化之關鍵成功因素，亦提供企業在數位轉型趨勢下推動智慧供應鏈與存貨管理策略之實證依據與管理建議，對學術研究與實務應用皆具重要貢獻。

In response to increasing volatility in the global semiconductor market and rising uncertainty across supply chains, inventory management has become a critical strategy for firms seeking to enhance operational resilience and maintain competitive advantage. Particularly in the semiconductor equipment manufacturing industry, where products are highly customized, lead times are long, and component costs are high, precise inventory allocation and agile supply coordination present significant challenges.
This study adopts A Company as a case and integrates literature review, expert interviews, and the Analytic Hierarchy Process (AHP) to develop a structured, quantitative framework. The model includes four primary dimensions—Demand Management, Inventory Management, Supply Chain Collaboration, and Intelligent Technology Management—with a total of eleven evaluation criteria. AHP surveys were conducted with 15 experts in the semiconductor and supply chain fields. The consistency ratios (C.R. < 0.1) confirm the reliability and validity of the results.
The AHP findings reveal that "Real-Time Demand Monitoring and Adjustment" is the most critical success factor, followed by "Demand-Driven Material Replenishment," "Safety Stock Management," and "Critical Material Management." These results indicate that companies prioritize agile, demand-based inventory strategies and contingency planning for high-risk materials. Conversely, "Intelligent Technology Management" ranked lowest, suggesting that AI and IoT, while promising, remain at an early stage of implementation and are not yet central to decision-making in practice.
Combining AHP results with the case company’s operational context and SWOT analysis, three strategic improvement paths are proposed: (1) Enhancing Forecasting Accuracy and Technology Adoption, (2) Strengthening Safety Stock and Critical Material Management, and (3) Deepening Supply Chain Collaboration. This study not only identifies key success factors for inventory optimization in the semiconductor industry but also provides practical guidance for firms advancing digital transformation and building smart supply chain systems. The findings contribute to both academic research and managerial practice.

Afridi, M. T., et al. A deep reinforcement learning approach for optimal replenishment policy in a vendor managed inventory setting for semiconductors. 2020 Winter Simulation Conference (WSC), IEEE. (2020).
Ambilkar, P., et al. "Product returns management: a comprehensive review and future research agenda." International Journal of Production Research 60(12): 3920-3944. (2022).
Azzamouri, A., et al. "Demand driven material requirements planning (DDMRP): a systematic review and classification." Journal of Industrial Engineering and Management 14(3): 439-456. (2021).
Bacchetti, A. and N. Saccani "Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice." Omega 40(6): 722-737. (2012).
Claassen, M. J., et al. "Performance outcomes and success factors of vendor managed inventory (VMI)." Supply chain management: an International journal 13(6): 406-414. (2008).
Dadaneh, D. Z., et al. "Simultaneous planning of purchase orders, production, and inventory management under demand uncertainty." International Journal of Production Economics 265: 109012. (2023).
De Jong, W. "New SCOP Method in ASML Supply Chain: Application of Enhanced Synchronized Base Stock in planning of Supply Chain Planning environment." Eindhoven, the Netherlands: TU/e. (2010).
Domaracka, L., et al. "Efficient use of critical raw materials for optimal resource management in EU countries." Sustainability 14(11): 6554. (2022).
Gonçalves, J. N., et al. "Operations research models and methods for safety stock determination: A review." Operations Research Perspectives 7: 100164. (2020).
Graves, S. C. and S. P. Willems "Supply chain design: safety stock placement and supply chain configuration." Handbooks in operations research and management science 11: 95-132. (2003).
Griffin, G., et al. "A framework for firm-level critical material supply management and mitigation." Resources Policy 60: 262-276. (2019).
Hoegl, M. and S. M. Wagner "Buyer-supplier collaboration in product development projects." Journal of management 31(4): 530-548. (2005).
Lacin, B. Determining Safety Buffer requirements against supply side uncertainties at ASML, TU/e-Master Thesis. (2011).
Lamghari-Idrissi, D., et al. "Increasing Chip Availability Through a New After-Sales Service Supply Concept at ASML." INFORMS Journal on Applied Analytics 52(5): 460-470. (2022).
Miclo, R., et al. "Demand Driven MRP: assessment of a new approach to materials management." International Journal of Production Research 57(1): 166-181. (2019)
MIDILLI, Y. E. and B. Elevli "Value Stream Mapping with Simulation to Optimize Stock Levels: Case Study." Jordan Journal of Mechanical & Industrial Engineering 14(3). (2020).
Mishra, B. K., et al. "Demand forecast sharing in supply chains." Production and Operations Management 18(2): 152-166. (2009).
Mönch, L., et al. "A survey of semiconductor supply chain models part I: semiconductor supply chains, strategic network design, and supply chain simulation." International Journal of Production Research 56(13): 4524-4545. (2018).
Okada, M. and K. Shirahada "Organizational Learning for Sustainable Semiconductor Supply Chain Operation: A Case Study of a Japanese Company in Cross Border M&A." Sustainability 14(22): 15316. (2022).
Uzsoy, R., et al. "A survey of semiconductor supply chain models Part II: demand planning, inventory management, and capacity planning." International Journal of Production Research 56(13): 4546-4564. (2018).
Van Rooy, J. Logistical information sharing and collaboration between ASML and its supply chain, Master’s thesis, Eindhoven University of Technology, URL https://pure. tue (2010).
Yuan, B. J., et al. Strategies of semiconductor industry in Taiwan. IEMC'98 Proceedings. International Conference on Engineering and Technology Management. Pioneering New Technologies: Management Issues and Challenges in the Third Millennium (Cat. No. 98CH36266), IEEE. (1998).