Overview
Developed an end-to-end analytics platform that ingests, processes, and visualizes real-time data from Manufacturing Execution Systems (MES), Laboratory Information Management Systems (LIMS), and equipment sensors. Created executive dashboards and operator-level insights for data-driven decision making.
Technical Architecture
Data Pipeline
- Python-based ETL processes using Pandas and Apache Airflow
- Real-time data ingestion from MES/LIMS APIs
- PostgreSQL data warehouse for historical analytics
- Redis caching for sub-second dashboard performance
Visualization Layer
- Power BI dashboards for executive leadership
- Custom React dashboards for operators
- Mobile-responsive views for floor managers
- Automated email reports with key metrics
Analytics & ML
- Anomaly detection for equipment performance
- Predictive maintenance models using scikit-learn
- Statistical process control (SPC) charts
- Yield optimization recommendations
Key Features
Executive Dashboard
- Real-time production metrics (OEE, throughput, yield)
- Cost per unit tracking
- Quality trend analysis
- Supply chain inventory levels
Operator Dashboard
- Line-level performance monitoring
- Defect rate tracking by station
- Downtime root cause analysis
- Shift handoff reports
Predictive Insights
- Equipment failure prediction (72-hour lookahead)
- Batch quality forecasting
- Capacity planning recommendations
Business Impact
- 25% reduction in unplanned downtime
- 15% improvement in overall equipment effectiveness (OEE)
- $500K+ annual savings through optimized maintenance schedules
- 30% faster decision-making with real-time data access
- Reduced manual reporting from 10 hours/week to automated
Technologies
- Backend: Python, Pandas, NumPy, Apache Airflow
- Database: PostgreSQL, Redis, TimescaleDB
- Visualization: Power BI, Tableau, D3.js, Plotly
- ML: scikit-learn, TensorFlow
- Infrastructure: Docker, AWS EC2, S3