Predictive analytics for roasting process optimization with GE Historian and MES integration.
Industry: Food & Beverage Manufacturing
Quick Facts
| Attribute | Value |
|---|---|
| ERP | Microsoft D365 (Cloud) |
| MES | GE Plant Applications |
| Historian | GE Proficy Historian |
| Front End | FrameworX dashboards |
| PLCs | Rockwell Automation |
| Analytics | Predictive and prescriptive models |
The Challenge
Challenge: Implement predictive analytics for roasting processes to address quality variations caused by environmental factors (humidity, seasons) and raw material variations (moisture content).
Specific pain points:
- Roasting processes relied on delayed quality checks and operator experience
- Quality issues discovered hours later, after large volumes already in process
- Environmental variability (humidity, seasons) affecting outcomes
- Raw material variation (moisture content) causing inconsistent results
- High waste and elevated energy use from suboptimal process control
Impact: Inconsistent quality, high waste rates, and elevated energy consumption due to reactive rather than proactive process control.
The Solution
Architecture
| Tier | Component | Capabilities |
|---|---|---|
| Data Sources | GE Historian, MES, Process Sensors | Time-series data, events, quality measurements |
| Analytics | Data Science Models | Predictive analytics for roaster performance and product color |
| Front End | FrameworX Dashboards | Real-time predictions and operator recommendations |
| Control | Rockwell PLCs | Controllable variables (burners, belt speeds, airflow) |
Data Flow
Process Sensors + GE Historian + MES Events
↓
Data Science Models (predictive analytics)
↓
FrameworX Dashboards (real-time predictions)
↓
Operator Adjustments / Automated Control
Controllable Variables
- Burner levels
- Belt speeds
- Airflow
Modeled Factors
- Humidity
- Seasonal variations
- Raw material moisture content
Key Enablers
FrameworX capabilities that made this solution possible:
| Capability | Application |
|---|---|
| Historian Integration | GE Proficy Historian time-series data for analytics |
| MES Connectivity | GE Plant Applications event data integration |
| Dashboard Delivery | Real-time model outputs to operators |
| Prescriptive Recommendations | Automated operator guidance |
| Continuous Improvement | Iterative model refinement support |
The Results
10-15% First-Pass Quality Improvement — Predictive models enabled proactive adjustments before quality issues occurred
~5% Waste Reduction — Reduced scrap through optimized process parameters
~10% Fewer Product Downgrades — Maintained specifications through real-time recommendations
10-15% Energy Savings — Optimized roasting operation reduced fuel consumption
Reduced Schedule Disruptions — Stabilized product consistency minimized production interruptions
This case demonstrates predictive analytics integration with existing historian and MES systems for manufacturing process optimization.