抄録
The semiconductor manufacturing industry, particularly the etching process, faces significant challenges in achieving optimal line balancing due to variability in process times, equipment availability, and material handling intricacies. This study addresses these challenges by developing an advanced Decision Support System (DSS) that integrates Data Analytics (DA) and Internet of Things (IoT) technology. The primary objectives are to enhance productivity by integrating DA with the DSS and to improve decision-making through real-time IoT systems. By incorporating real-time data analytics, the DSS provides actionable insights to streamline operations, reduce downtime, and enhance process efficiency and product yield. The comprehensive framework developed forms the backbone of the DSS, ensuring robust and informed decision-making. Additionally, the DSS incorporates predictive analytics to forecast equipment maintenance needs, preventing unexpected breakdowns and minimizing downtime. This predictive capability is crucial for maintaining continuous and efficient operations. The system also facilitates better resource allocation by analyzing historical data to predict future demand and adjust production schedules accordingly. Case-based validation demonstrates significant productivity improvements and enhanced decision-making capabilities, highlighting the potential of integrating data analytics and IoT technology to transform line balancing in semiconductor manufacturing. This integrated approach addresses current challenges and sets a foundation for future advancements in manufacturing efficiency.
