AI-Powered Energy Optimization System Cutting Energy Cost by 34% For Automative Parts Manufacturer.
About Client
The NineHertz collaborated with a mid-sized automobile parts manufacturing company that operated with three production facilities in the Midwestern United States. The company specializes in making precision-machined metal components for Tier-1 automotive suppliers.
The overall business revolves around energy-intensive workflow, including CNC machines, surface finishing lines, and heat treatment. The burgeoning sustainability mandates and energy tariffs made it extremely important for the firm to reduce its energy consumption without lowering the production output or parts quality.
Key Challenges
The firm used to rely on manual monitoring and old-fashioned time-based control to manage the energy usage. As the production scaled, the outdated system turned ineffective, causing operational gaps. The client witnessed multiple blind spots in consumption data where the team had no idea about the shifts or processes that were consuming the most power.
Idle-state operation was another challenge where the compressed air system ran at a constant 110 PSI, irrespective of actual line requirements. The reactive maintenance approach also began to cause breakdowns due to worn bearings, clogged filters, and degraded insulation, which often led to prolonged periods of unnecessary energy draw. The client also faced sustainability pressure from two of its largest OEM customers.
Our Solutions
The NineHertz designed an AI-enabled energy optimization platform that could grant the required visibility into energy usage and consumption patterns in easy-to-understand dashboards.
IoT-Powered Energy Monitoring
IoT sensors were installed in 140+ machines and utility systems, which feed into a centralized data pipeline, updating data every 15 seconds.
AI-Powered Dynamic Load Optimization
Our team created a machine learning engine that is trained on 14 months of historical energy and production data. The model predicts the energy demand within production lines to vary air pressure, furnace ramp schedules, and the HVAC setpoints
Predictive Maintenance
We implemented a predictive engine of maintenance that proactively monitors the equipment health and performance to precisely forecast the possibility of failures, and provides a warning about timely action.
Automated Sustainability Reporting Dashboard
The platform consisted of a reporting layer that calculates the energy-per-unit of the entire product line and offers OEM-ready sustainability report cards in Excel and PDF format.
Impact That Drives Results
The AI-powered energy optimization platform delivered measurable financial and operational results within six months of full deployment.
34%
Reduction in Energy Costs
Dynamic load optimization system saved monthly energy expenses of an average of $287K to 189K at three plants and resulted in yearly savings of $1.17M.
91%
Reduction in Unplanned Energy Spikes
The predictive maintenance system detects the wear and tear of the equipment before it causes excessive consumption of energy.
97%
Faster Sustainability Reporting
The automatic technician assignment replaced the manual discovery during inspection rounds, fostering real-time response.
Preferred Supplier Status Retained
The client passed both the OEM sustainability audit with substantial documentation and energy per unit savings improvement, gaining the contract renewal with more volume allocations in the following cycle.
