Customer Background
For long-term multi-site factory management, TSMC centralizes maintenance data into a unified platform, enabling asset reuse and advanced development.
AI-driven analytics enable anomaly prediction and proactive diagnostics, supporting real-time intelligent operations and improved M&E management.
Requirements & Challenges
Before AI adoption, facility management systems mainly served as equipment alert and diagnostic tools, relying on manual rules and experience, with limited flexibility and adaptability to diverse maintenance needs.
Solution
To address maintenance challenges, we developed an AI-driven alert and diagnostic model using operational and maintenance data, replacing rule-based systems with data-driven insights from integrated facility monitoring.
Three core analytics modules includes:
• Baseline Modeling : Establishes normal operating patterns to detect anomalies early
• Root Cause Analysis: Automatically analyzes data to identify causes of anomalies
• Solution Recommendation: Provides optimal actions based on maintenance records, turning expert knowledge into actionable insights
Results & Benefits
• Proactive Alerts
Detects around 360 potential issues monthly with up to 99% accuracy, improving service quality.
• Early Intervention
Reduces incidents by 50% through proactive handling, improving customer
satisfaction.
• Automated Recommendations
Reduces resolution time by over 60% and saves approximately NT$3.6 million in labor costs.
• AI Learning
Continuously learns from operations, enabling knowledge transfer and reducing training costs.