Case study
Energy Management
Reduced energy cost
Flattened peak demand
Overall efficiency improvementChallenge
A leading warehouse management company faced rising energy costs due to inefficient equipment usage and uncontrolled charging cycles for electric forklifts. With multiple high-consumption processes running simultaneously, peak demand charges were escalating. The challenge was to develop an intelligent system that could analyze operational data in real time and dynamically optimize energy usage without disrupting productivity.
Solution
We developed an AI-powered optimization engine that combines machine learning with mathematical scheduling models. The system predicts energy consumption, identifies peak loads, and dynamically reschedules charging and equipment operation to off-peak hours. Integrated with existing warehouse systems, the solution runs in real time or near real time, ensuring continuous operations while reducing energy waste and avoiding peak demand charges.
Impact
The AI solution reduced energy costs by 27%, flattened peak demand, and improved overall efficiency—delivering sustainable savings without requiring infrastructure changes or disrupting daily warehouse operations.
Let’s build the future of AI together
Explore job opportunitiesWhether you’re a seasoned professional or just starting your career journey, we have opportunities across a range of disciplines.