電子與機構料件的深度學習缺料預測模型及與MRP系統關係研究-以A公司為例;Research on deep learning material shortage prediction models for electronic and mechanical components and their relationship with MRP system - An example using Company A.

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題名:	電子與機構料件的深度學習缺料預測模型及與MRP系統關係研究-以A公司為例;Research on deep learning material shortage prediction models for electronic and mechanical components and their relationship with MRP system - An example using Company A.
作者:	陳詡衣;CHEN-XU-YI
貢獻者:	工業管理研究所在職專班
關鍵詞:	深度學習;神經網路;Deep learning;Neural Network
日期:	2024-07-10
上傳時間:	2024-10-09 15:17:13 (UTC+8)
出版者:	國立中央大學
摘要:	電子製造業對於產品缺料與物料調整需求和交期，為產品出貨關鍵因素，然而在物料的預測與交期需要透過不間斷的追蹤料況，成為整個產品從研發、試量產到量產的過程中扮演重要的角色。因此，本研究深入探討了在電子製造業中預測缺料對產品出貨的關鍵影響。從產品規格確認到預測物料需求和確保交貨準時，預測與備料的過程需經由部門間協調和供應鏈追蹤，在整個產品生產過程中扮演關鍵角色。本研究聚焦於將類神經網絡（DNN）技術與物料需求計劃（MRP）系統結合，以預測缺料的重要性。透過研究缺料預測和備料的評估，使企業能夠應對生產挑戰，確保營運順利並準時發貨。此外，本研究強調數據收集、模型訓練和評估的關鍵性，並著重適當的參數初始化和學習策略設定。通過DNN模型，建立更準確的缺料預測模型，以因應未來供應鏈變化不確定性，同時確保高效的生產運作和交貨流程。本研究將物料需求計劃（MRP）系統結合深度神經網絡（DNN）模型，以找出影響缺料預測的關鍵因素。透過研究物料需求預測與庫存管理之間的關聯，針對缺料預測和備料評估進行深入分析，揭示企業在確保營運順暢和準時交貨方面所面臨的挑戰。研究不僅強調了DNN模型自變數的重要性，還著重於正確的參數初始化和適當的學習策略設定。通過應用DNN模型建立準確的缺料預測模型，以應對未來供應鏈變化中的不確定性，同時確保高效的運營和準時交貨。最終研究旨在實現即時且動態的庫存管理，以避免過剩或缺料所帶來的生產延遲和成本增加。通過不斷改進深度學習技術和模型性能，以期望提供更有效的改進措施和管理策略，來幫助企業應對市場需求的變化和內部供應鏈的調整。這將為供應鏈的整體效率和電子製造業提供更具策略性的方法，來應對不斷增長的複雜動態需求。 ;The electronics manufacturing industry considers product shortages, material adjustment needs, and delivery lead times as critical factors for product shipment. However, predicting materials and lead times requires inter-departmental tracking and tracing of materials, playing a crucial role throughout the entire product development, pilot production, and mass production process. Therefore, this study delves into the key impact of predicting material shortages on product shipments in the electronics manufacturing industry. From confirming product specifications to predicting material needs and ensuring on-time delivery, the forecasting and stocking process requires coordination between departments and supply chain tracking, playing a vital role in the entire product manufacturing process. This research focuses on integrating neural network (DNN) technology with Material Requirements Planning (MRP) systems to emphasize the importance of predicting shortages. By studying the interactive relationship between shortage prediction and material assessment, businesses can address production challenges, ensuring smooth operations and timely deliveries. Furthermore, the research highlights the criticality of data collection, model training, and evaluation, with an emphasis on appropriate parameter initialization and learning strategy setting. Through DNN technology, the study aims to establish more accurate shortage prediction models to adapt to the uncertainties of future supply chain changes while ensuring efficient production operations and delivery processes. The research emphasizes the crucial roles of Material Requirements Planning (MRP) systems and models based on DNN technology. It explores the relationship between predictive material requirements and inventory management, with a specific focus on predicting shortages and evaluating material replenishment. The study reveals the challenges that enterprises encounter in ensuring smooth operations and timely deliveries. It highlights the significance of data and emphasizes the importance of correct parameter initialization and appropriate learning strategies. Through the application of DNN technology, the research aims to develop more accurate shortage prediction models to address the uncertainty of future supply chain changes and ensure efficient operations and on-time deliveries. The ultimate goal of the study is to achieve real-time and dynamic inventory management, avoiding production delays and cost increases caused by excess or shortages. By continuously improving deep learning technology and model performance, the research aims to provide more effective improvement measures and management strategies to help businesses adapt to market demand changes and internal supply chain adjustments. This will have a positive impact on overall supply chain efficiency and business competitiveness, offering the electronics manufacturing industry more strategic approaches to address the increasingly complex dynamic demands.
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