個案C公司是一家半導體後段封裝公司，面臨著原物料庫存管理失衡的挑戰，傳統的物料需求計劃（MRP）在應對需求變異和供應不確定性方面略顯不足，導致庫存成本高和缺貨風險，因此，本研究提出運用需求驅動物料需求規劃（DDMRP）理論，以期提高庫存管理效能，降低成本並提升服務水平。
　　本研究旨在探討如何優化個案C公司晶盤原物料的庫存管理，設計了一套DDMRP架構來改進原物料管理模式，核心在於通過建立緩衝區來管理庫存，以應對需求變動和供應鏈的不確定性。研究首先介紹個案C公司及其目前的原物料管理現狀，並分析存在的問題，接著利用該公司原始MRP系統的歷史數據，重構最佳DDMRP原物料管理模式並與原始MRP進行差異比較，其中為避免歷史數據受到市場景氣循環影響增加分析不穩定性，本研究除了比較歷史數據以外還特別增加3種具有規律性條件來分析，分別是「實際需求恆大於預測需求」、「預測需求恆大於實際需求 」與「隨機變動實際與預測需求」。
　　在研究流程中，首先進行資料收集和前處理，確保數據的準確性和完整性。接下來，進行模型建構，將MRP與DDMRP在不同條件下的存貨管理績效進行比較。最後，通過歷史數據模擬，驗證DDMRP模型在庫存管理中的應用效果。為期12個月的實證研究顯示，DDMRP模型在個案C公司中具有可行性能有效改善其原物料管理模式。最後，將研究詳細描述了個案C公司原物料管理現狀，並通過模型比較與模擬驗證，確立了DDMRP架構在庫存管理中的優勢。具體步驟包括分析公司現況、比較不同模型的績效、以及通過模擬驗證DDMRP的應用效果，從而為未來的庫存管理提供了實踐依據。
　　研究結果顯示，在大多數情況下，DDMRP模型相較於傳統的MRP模型，能夠顯著降低庫存水位，同時提升客戶需求達交率，證實了DDMRP在庫存管理中的潛在優勢，特別是在應對需求變異和供應鏈不確定性方面，具體來說，DDMRP在需求變異大的情境下表現較有優勢，其優勢在於降低庫存成本與提升客戶需求達交率，DDMRP通過動態調整緩衝區，實現了庫存水位的最佳化，從而降低了庫存持有成本；然而研究也存在一些局限性，研究指出在實際需求恆大於預測需求情境下，DDMRP與MRP的庫存績效差異不大，因此，企業在應用DDMRP時，需根據實際需求變異情況進行評估，以選擇最適合的庫存管理策略。

Case Study Company C is a semiconductor packaging company facing the challenge of imbalanced raw material inventory management. Traditional Material Requirements Planning (MRP) has proven insufficient in addressing demand variability and supply uncertainty, leading to high inventory costs and stockout risks. Therefore, this study proposes the use of Demand Driven Material Requirements Planning (DDMRP) to enhance inventory management efficiency, reduce costs, and improve service levels.
　This research aims to explore how to optimize the raw material inventory management for Tray at Company C by designing a DDMRP framework to improve the current raw material management model. The core of this approach lies in establishing buffer zones to manage inventory, thereby responding to demand changes and supply chain uncertainties. This study provides a detailed description of the current state of raw material management at Company C and validates the feasibility of the DDMRP theory through a 12-month empirical study. The historical data from Company C’s original MRP system was utilized to reconstruct the optimal raw material management model. The research process includes data collection, preprocessing, model construction, and performance evaluation. The specific steps are as follows:
　Introduction of the Research Subject: Analyzing the current raw material management status and existing problems at Company C. Model Comparison: Comparing the inventory management performance of MRP and DDMRP under different conditions.Simulation Verification: Validating the application effects of the DDMRP model in inventory management through historical data simulation.
　The research results indicate that, in most cases, the DDMRP model can significantly reduce inventory levels and improve customer demand fulfillment rates compared to the traditional MRP model. This confirms the potential advantages of DDMRP in inventory management, especially in dealing with demand variability and supply chain uncertainties. Specifically, DDMRP performs better in scenarios with high demand variability, with its advantages including lowering inventory costs and enhancing customer demand fulfillment rates. DDMRP achieves inventory level optimization through dynamic buffer adjustments, thereby reducing inventory holding costs. However, the study also acknowledges some limitations. It points out that in scenarios where actual demand consistently exceeds forecasted demand, the performance difference between DDMRP and MRP is minimal. Therefore, enterprises need to evaluate the actual demand variability when applying DDMRP to choose the most suitable inventory management strategy.

中文參考文獻：
王雅群. (2015). 利用DDMRP之緩衝管理及歷史需求與市場預測資訊改善晶圓製造廠之存貨管理. (碩士). 國立交通大學工業工程與管理系所.
林怡汝. (2021). 應用DDMRP理論改善存貨績效之實證研究-以I公司為例.(碩士).國立成功大學工業與資訊管理學系碩士在職專班.
林清河. (2006). 工業工程與管理: 華泰文化.
洪崇獻. (2015). 運用 DDMRP 概念改善工廠庫存管理-以 E 公司為例. (碩士). 國立交通大學管理學院工業工程與管理學程.
徐嘉徽. (2018). 運用TOC 動態緩衝管理改善封測製造廠存貨績效之實證研究. 國立交通大學管理學院工業工程與管理學程.
陳冠霖. (2023). 運用 DDMRP 理論改善備品存貨管理之研究-以 C 公司為例. (碩士).國立成功大學工業與資訊管理學系碩士在職專班.
黃惠民. (2015). 物料管理與供應鏈導論(第三版): 滄海圖書資訊.
英文參考文獻：
Cox, J. F., & Schleier, J. G. (2010). Theory of constraints handbook. New York: McGraw-Hill.
Favaretto, D., & Marin, A. (2018). An empirical comparison study between DDMRP and MRP in Material Management. Department of Management, Università Ca'Foscari Venezia Working Paper(15).
Hasanati, N., Permatasari, E., Nurhasanah, N., & Hidayat, S. (2019). Implementation of material requirement planning (MRP) on raw material order planning system for garment industry. Paper presented at the IOP Conference Series: Materials Science and Engineering.
Ihme, M., & Stratton, R. (2015). Evaluating demand driven MRP: a case based simulated study.
Kortabarria, A., Apaolaza, U., Lizarralde, A., & Amorrortu, I. (2018). Material management without forecasting: From MRP to demand driven MRP. Journal of Industrial Engineering and Management, 11(4), 632-650.
Miclo, R., Lauras, M., Fontanili, F.& Lamothe, J. (2019). Demand Driven MRP: assessment of a new approach to materials management. International Journal of Production Research, 57(1),
Orlicky, J. (1975). Material requirements planning : the new way of life in production and inventory management. New York: McGraw-Hill.
Porras, E., & Dekker, R. (2008). An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods. European Journal of Operational Research, 184(1), 101-132.
Ptak, C. A., & Smith, C. (2016). Demand driven material requirements planning (DDMRP). South Norwalk, Connecticut: Industrial Press, Inc.
Ptak, C. A., Smith, C., Orlicky, J., & AccessEngineering. (2011). Orlicky's material requirements planning (3rd ed.). Place of publication not identified: McGraw Hill.
Shofa, M. J., Moeis, A. O., & Restiana, N. (2018). Effective production planning for purchased part under long lead time and uncertain demand: MRP Vs demand-driven MRP. Paper presented at the IOP Conference Series: Materials Science and Engineering.
Shofa, M. J., & Widyarto, W. O. (2017). Effective production control in an automotive industry: MRP vs. demand-driven MRP. Paper presented at the AIP Conference Proceedings.
Spearman, M. L., & Zazanis, M. A. (1992). Push and pull production systems: issues and comparisons. Operations research, 40(3), 521-532.
Wilson, R. H. (1934). A Scientific Routine for Stock Control: Simple Tables, Scientifically Prepared for the Economic Control of Each Stock Item, to Aid Stock Control Clerks in Accurately Establishing Ordering Amounts and Ordering Points: Bell Telephone Laboratories.
Xu, G., Guan, Z., Yue, L., & Mumtaz, J. (2023). An efficient production planning approach based demand driven MRP under resource constraints. International Journal of Industrial Engineering Computations, 14(3), 451-466.