Leveraging Advanced Technologies to Improve Manufacturing Operations

Effective data collection is critical for optimizing production lines, yet traditional methods such as manual recording and PLC-coded data collection are fraught with inefficiencies and inaccuracies. Manual data entry often misses short downtime events and is subject to operator bias, while PLC-based systems suffer from inconsistencies, excessive costs, and revalidation challenges.

The future of data collection lies in automation, modular modeling, and intelligent data processing, providing a foundation for digital transformation and sustainable manufacturing excellence.

This session will explore the following concepts:
· Advanced data collection goes beyond monitoring bottleneck operations, incorporating machine-level insights across all assets.
· A multi-layered approach – integrating real-time signal processing, logic engines, and high-speed data acquisition – enhances fidelity, reduces integration costs, and improves root cause analysis.
· Additionally, Aa Fault Learning approach dynamically identifies and ranks faults, leading to better diagnostics and predictive maintenance.
· By leveraging digital twins, synchronizing multiple data streams, and enabling fast data validation, companies can significantly improve operational efficiency.
· A robust data collection strategy supports MES, OEE, and AI/ML applications, ensuring accurate modeling, predictive analytics, and enterprise-wide standardization.