AI Case Studies
Facility
Profet AI assists facility departments with work hour and electricity usage predictions, as well as equipment energy optimization, using data analysis to reduce operational costs and enhance resource utilization.
Application Scenarios
Analysis and Prediction, Energy Optimization
Work Hour Analysis Prediction
In manufacturing, production scheduling is critical, and accurate work hour predictions are the foundation for efficient resource allocation and production efficiency.
Pain Points Analysis:
- Low Prediction Accuracy: Reliance on manual experience or simple statistical analysis leads to significant prediction errors.
- Complex Production Processes: Modern processes involve multiple steps, equipment, and materials, making accurate work hour estimation difficult.
Outcome Benefits:
- AI Models: Identify factors affecting work hour variations, improving product quality by 8%.
- Efficient Scheduling: Help companies create better schedules, increasing production efficiency by 7.5% and optimizing human resource allocation, significantly reducing production costs.
Work Order Electricity Usage Prediction
With rising electricity and energy prices, managing energy usage to reduce costs is crucial for enhancing manufacturing competitiveness.
Pain Points Analysis:
- High Electricity Costs: Electricity is a major production cost, and poor management directly impacts profits.
- Overuse Penalties: Poor energy management leads to fines, increasing operational costs.
- Uncoordinated Scheduling: Misaligned production schedules and electricity usage lead to energy waste.
Outcome Benefits:
- • Work Order Predictions: Better production planning, increasing equipment utilization by 12.7%.
- Proactive Energy Management: Reduce overuse rates by 9%.
Chiller System Energy Optimization
Chiller systems are often designed for peak loads under extreme conditions, leading to inefficiencies during milder seasons.
Pain Points Analysis:
- Low Energy Efficiency: Chillers operate at low efficiency under partial load conditions.
- Multi-Machine Coordination Issues: Different load combinations result in varying operational efficiencies.
Outcome Benefits:
- Predictive Models: Establish models based on temperature, flow, and load to guide operations and equipment adjustments.
- Reliable Models: Integrate control systems for efficient equipment management.