| dc.description.abstract | 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. | en_US |