PlantOS - Manufacturing IntelligencePlantOS - Manufacturing Intelligence 
Conversion Cost per Unit
Utilization Growth %
ROI / Value Creation per Unit Time per Unit Area
Cost of Maintenance per Unit
Safety & Risk Management
ROI / Production Agility
Digital Tool Scatter / Integration Complexity
AI-driven Site-wise Dashboards + Schedules
ROI-centric Digital Transformation
Output Growth %
% Decisions Based on AI Prescriptions
Cost Competitiveness
Unscheduled Downtime
% AI Prescriptions Accepted & Acted Upon
Asset Reliability
Cost of Energy per Unit
Energy Efficiency
Productivity Growth %
Digital Ways of Working
Digital Transformation ROI
Critical Equipment
Critical Equipment
Critical Equipment
Critical Equipment
Critical Equipment
| KPI | Before PlantOS | Post-CoE Action | Tangible Impact |
|---|---|---|---|
| Energy Consumption per Ton | High & inconsistent due to idle runs and non-optimized load | Equipment-level energy mapping & operation- time correlation |  8–15% reduction in
energy cost |
| Mean Time Between Failures (MTBF) | Frequent unplanned breakdowns, low reliability | Predictive maintenance insights, condition monitoring |  MTBF improved by
20–30% |
| Equipment Availability | Unknown idle periods, poor utilization | Real-time run/idle/failure tracking with alerts | 10–25% boost in
availability |
| Production Cost per Ton | Fragmented visibility across departments | Integrated energy + maintenance + quality data per batch | Cost rationalization
through holistic
insights |
| Process Quality Analysis | Reactive issue detection after poor recovery or rejects | Live trend monitoring of process parameters | Consistency in
quality, 5–10% defect
reduction |
| Operational Visibility | Manual data logging, siloed systems | Unified, real-time dashboards for all KPIs |  Faster decisions,
centralized insights |
| Maintenance Response Time (MTTR) | Long delays due to reactive maintenance | Downtime reason analysis + auto-alert workflows | MTTR reduced by
20–30% |
Prescriptive MaintenanceEnergy Efficiency
