Alarm Management Innovation in the Era of Digital Transformation: Enhancing Manufacturing Competitiveness through AI Technology Adoption

Effective alarm management in the plant industry is essential for improving safety and productivity. (1) Background/Purpose: Current alarm management systems face critical issues including excessive alarm generation, inappropriate priority settings, unnecessary alarms, and alarm chattering, which increase operator burden and risk the oversight of critical alerts. Industrial accidents such as the 2005 Texas City Refinery explosion demonstrate the potentially catastrophic consequences of alarm overload. This study examines the current limitations of traditional alarm management and explores how AI technology can address these challenges. (2) Study Design/Methodology/Approach: This research analyzes the Industry 4.0 infrastructure prerequisites required for AI-based alarm management implementation, reviews existing solutions from major automation vendors (Emerson AgileOps and Honeywell Experion), and evaluates AI application potential across key industrial sectors. (3) Findings: AI-based alarm management systems can significantly enhance efficiency through intelligent priority setting, predictive analytics, customized alarm configuration, and integrated data visualization. Semiconductor chemical and oil refining industries are identified as sectors where AI alarm management adoption is most urgently needed. (4) Originality/Value: This study provides a comprehensive framework for understanding the conditions necessary for successful AI-based alarm management adoption, offering strategic insights for manufacturing enterprises seeking to leverage Industry 4.0 capabilities to enhance competitiveness, safety, and operational efficiency.