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Development of Predictive Maintenance and Operational Efficiency System for Chemical Industry

stxnext.com

Manufacturing

Energy & natural resources

Utilities

Operational Inefficiencies and Reactive Maintenance Challenges

The client faced significant challenges in managing massive volumes of operational data from IoT sensors, lacked proactive maintenance capabilities, and struggled with demand forecasting. Their legacy systems failed to provide real-time insights, leading to unplanned downtime, inefficient resource allocation, and increased maintenance costs.

About the Client

Multinational corporation specializing in plastics, chemicals, and refining with global production facilities

Key Project Goals

Implement predictive maintenance capabilities to reduce equipment failures
Centralize real-time data processing from IoT sensors
Develop customizable dashboards for operational insights
Optimize demand planning and energy consumption forecasting
Achieve 20% reduction in unplanned downtime
Enable scalable data infrastructure for hundreds of terabytes

Core System Capabilities

Real-time asset parameter visualization with historical trend analysis
IoT data ingestion pipeline from sensors via EventHub
Customizable alert system for anomaly detection
Predictive maintenance scheduling with resource optimization
Demand forecasting with carbon emission tracking
Multi-plant data consolidation with role-based access
Automated reporting and decision support dashboards

Technology Stack Requirements

Python for data engineering and ML models

Azure Event Hubs for IoT data streaming

Time-series databases (e.g., InfluxDB)

TensorFlow/PyTorch for predictive modeling

React.js for frontend visualization

Cloud-native architecture on Azure/AWS

System Integration Needs

Legacy SCADA systems
ERP platforms (SAP/Oracle)
IoT sensor networks
Energy management systems
Existing data lakes

Operational Requirements

Support 1M+ sensor data points per second
99.99% system availability
Sub-second anomaly detection latency
Data retention policy for 10+ years
Role-based access control with ISO 27001 compliance
Auto-scaling architecture for petabyte-scale data growth

Expected Business Outcomes

Implementation will reduce unplanned downtime by 20%, decrease maintenance costs through predictive scheduling, optimize energy consumption by 15-20%, and enable proactive supply chain management. The system will support exponential data growth from thousands to millions of IoT endpoints while maintaining sub-second analytical performance.