Integrated Steel Producer
OAC dashboards unifying production, energy, and quality data across 3 steel plants.
All case studiesAn integrated steel producer operating three plants across Odisha and Jharkhand engaged Cydez Technologies to build a unified analytics platform. The company produced 1.8 million tonnes of steel annually across long products, flat products, and special steels — with energy costs representing 35% of total production costs.
Each plant operated independently with different SCADA systems, historian databases, and ERP instances. Monthly energy reports were assembled manually by plant engineers using Excel, and management received consolidated MIS reports 10-12 days after month-end. There was no capability to benchmark energy consumption across plants, identify furnace efficiency deviations in real time, or correlate quality outcomes with process parameters.
Cydez deployed Oracle Analytics Cloud with a unified semantic layer, using MuleSoft to connect SCADA historians, plant ERPs, and quality management systems across all three plants. The platform delivered 35+ dashboards covering energy consumption per tonne, furnace efficiency, rolling mill yield, caster performance, and quality grade distribution. Shift-level benchmarking enabled best-practice identification and sharing across plants. Automated anomaly alerts flagged energy consumption deviations within minutes of occurrence.
- Unified analytics across 3 steel plants
- SCADA and historian data integration via MuleSoft
- 35+ real-time operational dashboards
- Energy consumption benchmarking and anomaly detection
- Quality-process parameter correlation analysis
- Shift-level performance benchmarking
An integrated steel producer running three plants across two states had no consolidated view of energy consumption, production throughput, or quality metrics. Each plant used a different combination of SCADA, local databases, and spreadsheets for operational reporting. Monthly energy costs exceeded INR 45 crore, but there was no analytical capability to identify waste or benchmark across plants.
Cydez deployed Oracle Analytics Cloud with MuleSoft connectors pulling data from SCADA historians, plant ERPs, and quality management systems across all three plants. Dashboards covered energy consumption per tonne of output, furnace efficiency, rolling mill yield, and quality grade distribution. Automated alerts flagged energy anomalies in real time, and shift-level benchmarking enabled best-practice sharing across plants.
How we delivered this project
Discovery
Visited all 3 plants over 6 weeks. Mapped SCADA data structures, historian tag lists, and ERP schemas at each plant. Documented energy metering infrastructure and quality testing workflows. Aligned KPI definitions with plant managers and the group COO.
Design
Designed the OAC semantic layer with standardised KPI definitions for energy, production, quality, and maintenance. Created MuleSoft integration architecture for 3 SCADA historians and 3 ERP instances. Planned 35 dashboard layouts for executive, plant manager, and shift supervisor audiences.
Development
Built MuleSoft integration flows for real-time SCADA data ingestion and daily ERP data sync. Deployed OAC on OCI with ADW as the analytics data store. Configured 35+ dashboards with drill-down from group level to plant, furnace, and shift level. Developed automated anomaly detection alerts for energy consumption.
Launch
Rolled out plant-by-plant over 8 weeks. Installed shop floor display screens at each plant. Trained 60 managers, engineers, and shift supervisors. Conducted a 30-day parallel-run against manual reporting to validate data accuracy. Achieved full adoption within 6 weeks of go-live.
What we built
Energy Analytics
Energy consumption per tonne tracked at furnace, mill, and plant level. Real-time benchmarking across plants with automated anomaly alerts for consumption deviations exceeding 5% of baseline.
Furnace Efficiency Monitoring
Real-time furnace efficiency dashboards showing heat rate, tap-to-tap time, electrode consumption, and refractory wear. Trend analysis for proactive maintenance scheduling.
Rolling Mill Yield Tracking
Yield tracking from billet to finished product with loss classification by cause. Shift-level yield benchmarking enabling identification and replication of best practices.
Quality Grade Distribution
Real-time quality grade distribution analysis correlated with process parameters. Automated alerts when off-grade production exceeds threshold for any product category.
Shift Benchmarking
Performance comparison across shifts at each plant covering production output, energy efficiency, quality yield, and safety observations. League tables driving healthy competition.
Executive Dashboard
Consolidated group-level view of production, energy, quality, and financial metrics across all 3 plants with drill-down capability to any level of detail.
Measurable outcomes
- 12% reduction in energy cost per tonne within 8 months
- Real-time quality grade visibility reduced customer complaints by 22%
- Shift-level benchmarking improved rolling mill yield by 8%
- Monthly MIS preparation eliminated — dashboards update in real time
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