AI Powered Monitoring System for L&T Galvanizing Plants
Real time AI analytics on galvanizing plant operations improving efficiency and reducing unplanned downtime.
- Timeline 10 Months
- Industry Manufacturing · Infrastructure
- Services AI Development · Data Engineering · Web Development
- Platform Web · AI
- Technologies Python, TensorFlow, React, InfluxDB, MQTT, AWS IoT
- Team Size 7 Engineers
- Country India
Bringing intelligence to industrial operations
L&T's galvanizing plants are critical infrastructure components in large scale construction projects. Any unplanned downtime has significant downstream costs.
Plant managers were operating with lagging indicators, including weekly reports and manual floor checks, with no real time visibility into plant health and no ability to anticipate failures.
The objective was to transform plant monitoring from reactive and manual to proactive and data driven, without requiring plant operators to become data scientists.
The Challenges We Were Solving
OT/IT Integration Complexity
Industrial control systems (SCADA, PLC) used proprietary protocols incompatible with modern cloud infrastructure, requiring custom protocol bridges.
High-Frequency Sensor Data
Thousands of sensors generating data at sub second intervals required a time series data infrastructure capable of ingesting and analyzing streams at scale.
Operator Adoption
Plant floor operators needed a monitoring interface that surfaced insights without requiring data literacy, simple, visual, and immediately actionable.
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We build AI powered monitoring platforms for industrial and manufacturing operations.
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We built an end to end IoT and AI monitoring platform, from OT protocol bridges at the plant floor to machine learning models in the cloud and dashboards for plant managers.
Plant Operations Dashboard
A real time dashboard showing live KPIs across all plant subsystems, including zinc bath temperature, line speed, coating thickness, and equipment health, with drill down to individual sensors.
Anomaly Detection Engine
ML models trained on historical plant data identify process deviations and equipment anomalies before they cause failures, alerting maintenance teams with recommended actions.
Production Analytics Reports
Automated daily and weekly production reports showing efficiency metrics, downtime analysis, quality trends, and energy consumption, replacing manual reporting entirely.
The Result
Through predictive maintenance alerts
Through process optimization insights
Across all monitored systems
Before failures occur
What the Platform Delivered
Reactive, manual plant management replaced by a proactive intelligence layer, giving L&T plant managers the visibility and lead time to prevent failures before they cost production.
35% Reduction in Plant Downtime
Predictive maintenance alerts surface equipment issues hours before failure, turning unplanned shutdowns into scheduled maintenance windows.
22% Energy Cost Savings
Process optimization insights identify energy inefficiencies across plant systems, reducing consumption without compromising production output.
Complete Real Time Plant Visibility
100% of monitored systems visible at all times, from zinc bath temperature to coating thickness, replacing weekly reports with live operational data.
4-Hour Average Fault Lead Time
Anomaly detection flags deteriorating conditions an average of four hours before failure, giving maintenance teams time to intervene with planned repairs.
Operator-Friendly Intelligence
Actionable alerts and visual dashboards designed for plant floor operators, no data science training required to act on AI generated maintenance recommendations.
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