🔧 EIS Module

Equipment Information System - comprehensive solution for equipment, asset and maintenance management with IoT integration and predictive analytics

🎯 Key Business Problems

Unplanned equipment downtime

→ production and revenue losses

High repair costs

→ reactive maintenance more expensive than planned

Low utilization efficiency

→ equipment not operating at full capacity

Difficulty in planning maintenance

→ unclear when and what to service

✅ Solutions

Emergency Stoppage Prevention

Failure prediction 2-4 weeks in advance, 30-50% downtime reduction

Maintenance Cost Optimization

Transition from reactive to predictive maintenance, 20-40% savings

Performance Enhancement

15-25% OEE growth through operating mode optimization

Risk Management

Critical equipment monitoring, accident minimization

Spare Parts Optimization

Accurate demand planning, warehouse stock reduction

🚀 Key Features

1. Equipment Condition Monitoring

  • Real-time monitoring: IoT sensor integration
  • Telemetry: Collection of temperature, vibration, pressure data
  • Health scoring: Automatic condition assessment (0-100%)
  • Alert management: Instant notifications

2. Digital Twins

  • Virtual models: Digital copies of physical equipment
  • Simulation: Behavior modeling under different conditions
  • Predictive modeling: State forecasting
  • What-if analysis: What-if scenario analysis

3. Predictive Maintenance

  • Machine Learning: ML models for failure prediction
  • Anomaly detection: Anomaly identification
  • Failure prediction: Failure probability prediction
  • Risk assessment: Risk evaluation

4. Maintenance Management

  • Maintenance workflows: Maintenance order management
  • Component tracking: Component monitoring
  • Maintenance history: Complete maintenance history
  • Compliance tracking: Regulatory compliance

5. Efficiency Analytics (OEE)

Overall Equipment Effectiveness: Availability × Performance × Quality

  • MTTR/MTBF: Mean time to repair/between failures
  • Equipment utilization: Equipment utilization rates
  • Energy efficiency: Energy efficiency metrics

6. Spare Parts Management

  • Spare parts catalog: Spare parts catalog management
  • Stock optimization: Inventory optimization
  • Demand forecasting: Demand forecasting
  • Cost tracking: Cost monitoring

🏗️ Technical Architecture

IoT Integration Architecture

IoT Sensors → Edge Computing → Message Queue → Analytics Engine → Dashboard
↓ ↓ ↓ ↓ ↓
Equipment → Preprocessing → MQTT/Kafka → ML Models → Real-time UI

Digital Twin Architecture

Physical Equipment → IoT Data → Digital Model → Simulation → Predictions
↓ ↓ ↓ ↓ ↓
Sensors/Controllers → Streaming → State Sync → AI Engine → Actions

Cross-BC Integration

🔧 EIS BC → WFM (Maintenance Tasks)
📦 EIS BC → PLM (Spare Parts)
📍 EIS BC → Positioning (Equipment Location)
🏭 EIS BC → Core (Asset Registry)

📊 Analytics Capabilities

Equipment Health Analytics

  • Health score: 0-100% condition assessment
  • Anomaly detection: identification of deviations
  • Prediction accuracy: prediction accuracy
  • Alert response time: response time

Maintenance Analytics

  • Planned vs unplanned: maintenance ratio
  • Cost analysis: maintenance costs
  • Compliance rate: compliance level
  • Performance trends: efficiency trends

Efficiency Analytics

  • OEE metrics: overall equipment effectiveness
  • Utilization rates: utilization rates
  • Energy consumption: energy consumption
  • Quality metrics: quality metrics

🔒 Security and compliance

🏭 Industry applications

Manufacturing (assembly lines, CNC machines), Energy (power plants, wind turbines), Oil and gas (drilling equipment, pipelines)

📈 KPIs and Results

Equipment Uptime 95-99%
Maintenance Cost -20-40%
OEE Improvement +15-25%
Prediction Accuracy 85-95%
Response Time < 5 min

🚀 Benefits

For Business

  • 30-50% downtime reduction
  • Maintenance optimization - 20-40% cost savings
  • 15-25% OEE efficiency improvement
  • Failure prediction - 70-80% prevention

For IT

  • Scalability - from units to thousands of units
  • IoT integration - various sensor types
  • ML ready - built-in algorithms
  • Real-time - instant data processing

🎯 Practical Scenarios

Manufacturing Company

  • Predictive maintenance: 60% downtime reduction
  • OEE optimization: +25% efficiency
  • Quality control: -40% waste

Energy Company

  • Asset monitoring: Critical equipment
  • Energy efficiency: Consumption optimization
  • Compliance: Regulatory requirements

Transportation Company

  • Fleet management: Fleet control
  • Fuel optimization: Fuel consumption
  • Maintenance planning: Maintenance scheduling

🎯 EIS Module - Your Solution

Comprehensive platform for equipment management with IoT integration, predictive analytics, and digital twins

*Optimize equipment, predict failures, boost efficiency!*

📞 Next Steps

EIS Module - The Future of Equipment Management is Here! 🚀