时间：2024年3月6日（星期二）13:30-14:30
地点：经管大楼A楼 四楼第二会议室报告厅
主题：不确定性需求预测及产能管理以提升半导体供应链韧性（Demand Forecast and Capacity Management Under Uncertainty for Semiconductor Supply Chain Resilience）
主讲人：傅文翰（beat365中国官方网站）
简介：傅文翰，beat365中国官方网站工业工程系讲师，硕士生导师。台湾清华大学工业工程与工程管理博士。主要研究方向为数据分析与决策、供应链管理、质量管理、人工智能等。在Computers & Industrial Engineering、Journal of Intelligent Manufacturing, Applied Soft Computing等国际期刊和会议发表论文10余篇。
Fu Wenhan is a Lecturer and Master Supervisor in the Department of Industrial Engineering at the Business School, University of Shanghai for Science and Technology. He received PhD degree in Industrial Engineering and Engineering Management from Tsing Hua University, Taiwan. His main research areas include data analysis and decision-making, supply chain management, quality management and artificial intelligence. He published over 10 papers in the international journals and conferences including Computers& Industrial Engineering, Journal of Intelligent Manufacturing, Applied Soft Computing.
摘要：高科技产业是资本密集型产业，竞争日益激烈。制造商需要有效管理供应链活动，以优化资源投资和产出，从而保持竞争优势。目前，由于上下游的信息透明度低、供应链规划的筹备时间长、生产周期长以及技术不断迁移，供应链的不确定性很高。供应链新范式的构建与创新面临巨大挑战。本研究旨在提出一种不确定性下的智能供应链决策框架，以有效提高需求预测的准确性和产能管理效率，增强半导体供应链管理的弹性，增强产业竞争力。它可以帮助决策者了解每个决策单元的执行情况，进行适当的资源分配，并设定供应的创新方向和规模。
High-tech industry is capital intensive and increasingly competitive. Manufacturers need to effectively manage supply chain activities to optimize resource investments and outputs to maintain competitive advantages. Currently supply chain uncertainty is high due to low information transparency in the upstream and downstream, long lead time for supply chain planning, lengthy production cycle time, and continuous technology migration. The construction and innovation of the new paragram of supply chain faces huge challenges. This study aims to propose a smart supply chain framework of decision-making under uncertainty to effectively improve demand forecast accuracy and capacity management efficiency to enhance semiconductor supply chain management resilience and strengthen industrial competitiveness. It can help decision makers to understand the implementation status of each decision-making unit, make appropriate resource allocation, and set the innovation direction and magnitude of supply chain management.
