Objective: Maximize production efficiency, stabilize operations, and reduce variability

Real-time control of kiln stability

Reduces fuel consumption and process fluctuations

Improves clinker quality consistency

Uses ML soft sensors + MPC control

Optimizes mill output and energy consumption

Stabilizes product quality

Impact: Higher throughput, lower energy cost, more stable operations

Objective: Extend innovation beyond cement into downstream business

Integration with All Concrete Business Software

Data collection to one unified database from:

Quality

Production process

Fleet process

Administrative data

Target:

Predict slump during transit using ML

Stabilize process, reduces quality standard deviation, and reduce energy consumption.

Centralized data platform using OSIsoft PI System

Covers full data lifecycle:

Capture → Store → Analyze → Visualize → Share

Integration of:

PLC/SCADA data

Quality lab systems

Maintenance data

Plant Analyzers

Deployment of tablets for field operators and maintenance teams

Installation of barcode/QR codes at each equipment and inspection point

Advanced Features (Future Phase):

Automatic alerts on abnormal readings

AI-based anomaly detection from inspection patterns

Predictive maintenance triggers based on inspection inputs

Full traceability of inspections (who, when, where)

Elimination of paper-based errors and delays

Faster issue detection and escalation

Strong foundation for predictive maintenance