The Client

The world's largest chemical manufacturing company, operating in over 80 countries.

The Business Challenge

The client faced a significant risk of financial loss due to inconsistent maintenance of environmental conditions during operations. Chiller systems across locations were operating inefficiently, with high energy consumption and limited visibility. Downtime due to improper maintenance was also impacting process reliability and operational cost control.

Our Solution

Axcend developed a real-time IoT monitoring solution that connected chillers across multiple sites, enabling centralized analysis, predictive maintenance, and improved energy efficiency. All this, through a reliable and scalable architecture.

Key features included:

• Cloud-based data acquisition
  A real-time system was built to collect key parameters from both sensors and control systems, then sync that data to a cloud platform for centralized visibility and analysis across all locations.

• Resilient system architecture
  A store-and-forward mechanism ensured data integrity even during connectivity loss, maintaining operational awareness and system reliability across the communication network.

• Preventive maintenance alerts
  Custom alerts were configured to notify teams of anomalies in chiller performance — enabling early intervention and reducing unexpected downtime.

• Predictive maintenance using ML
  As part of an ongoing engagement, Axcend began developing machine learning models that would forecast failures and support intelligent maintenance planning.

Business Benefits

• 100% visibility into chiller parameters
  Teams gained real-time insight into key operating variables. This improved maintenance planning and significantly reduced unexpected downtime.

• Reduced financial risk
  With tighter control over environmental conditions, the client was able to reduce the financial risks associated with fluctuations, failures, or non-compliance.

• Improved energy efficiency
  By regulating chiller operations based on real-time data, the client optimized energy use, lowered consumption, and improved sustainability metrics.

• Foundation for predictive maintenance
  The system laid the groundwork for ML-based improvements, allowing for smarter interventions and reduced reliance on scheduled manual inspections.