Cybernetis AI Helps Concrete Manufacturer Reduce Energy Costs with AI Forecasts

Challenges

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A regional concrete manufacturer operates two plants, producing ready-mix and precast products for the construction industry. Concrete production is energy-intensive, especially with high electricity usage for mixers, crushers, and curing systems. Nearly 15% of total production costs were tied to energy consumption.Before adopting Cybernetis AI, the company relied on basic monitoring tools that tracked usage but could not forecast energy demand. As a result:

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• Unexpected peaks led to high grid purchases and penalties.
• Fluctuating energy costs reduced profit margins.
• Production had to be slowed during expensive peak hours, impacting delivery schedules and revenue.

The company required an AI-driven solution to predict energy demand, optimize schedules, and reduce costs without disrupting production.

Approach

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Cybernetis AI deployed its Energy Optimization module at the company’s largest plant. The system ingested 1 year of operational and energy data, including production schedules, mixer usage, curing times, and utility charge patterns.Machine learning models were configured to:

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• Forecast daily/hourly energy usage for key production stages.
• Identify cost-saving opportunities by shifting processes to off-peak hours.
• Correlate energy demand with throughput to balance efficiency and costs.

Managers gained access to an integrated dashboard that simulated energy scenarios, tracked consumption in real time, and provided actionable recommendations.

Project Highlights

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• 4 months from kickoff to production-ready deployment.
• 1 year of historical data integrated (covering ~15,000 production cycles).
• 2 ML models trained for energy forecasting and production optimization.
• Configured Cybernetis AI Energy Dashboard for plant operators.

Benefits

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By using Cybernetis AI, the concrete manufacturer achieved:

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• Significant reduction in electricity costs and penalties.
• Improved efficiency of production processes.
• Reliable demand forecasts for better energy planning.
• A scalable solution that can be rolled out to additional plants.