Energy Management Optimization in Manufacturing

AI-driven energy monitoring and predictive optimization reducing costs, improving sustainability metrics, and enhancing efficiency across manufacturing facilities

The challenge

A large manufacturing company struggled with high energy costs and limited visibility into consumption patterns across production lines and facilities. Manual monitoring through legacy systems prevented real-time optimization, leading to peak demand charges and sustainability challenges.

The solution

We implemented Energy Management Platform using Azure IoT Hub to capture real-time data from sensors and equipment. Databricks ML models enabled predictive consumption forecasting and anomaly detection, while PBI dashboards provided insights for load balancing and peak demand management.

Measured impact

20-25%

Reduction in overall energy costs,

30%

Decrease in peak demand charges,

15%

Reduction in carbon emissions footprint