Real-time program health monitoring for delivery leaders
Managing a $5M+ delivery portfolio across multiple product lines, I found myself drowning in weekly RAG status updates and monthly steering committee slides. The problem? Most dashboards are reactive—they tell you what went wrong after you missed your commitments.
I needed a single source of truth that:
- Shows program health at a glance
- Surfaces problems before they become crises
- Focuses on leading indicators, not vanity metrics
- Works for both technical teams and business stakeholders
This dashboard does exactly that.
- Program Velocity Trends — Sprint-by-sprint capacity analysis with trend lines
- Defect Density Tracking — Bugs per thousand lines of code with quality thresholds
- Resource Utilization Heat Maps — Team allocation and availability patterns
- CSAT Trajectory Analysis — Customer satisfaction trends with predictive insights
- Color-coded status indicators (Green/Amber/Red)
- Configurable alert thresholds
- Automatic trend detection
- Built with modern Python stack
- Responsive design works on desktop and tablet
- Dark theme for extended viewing
- Zero external data dependencies (uses sample data)
| Component | Technology |
|---|---|
| Backend | Python 3.8+ |
| Dashboard | Dash (Plotly) |
| Data Processing | Pandas, NumPy |
| Styling | Plotly CSS theming |
- Python 3.8 or higher
- pip or conda
# Clone the repository
git clone https://github.com/Satyapraveenv/delivery-excellence-dashboard.git
cd delivery-excellence-dashboard
# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txtpython delivery_metrics.pyThe dashboard will start at http://localhost:8050
Open in your browser and explore the metrics in real-time!
- Historical capacity and completed story points
- Sprint-over-sprint trend analysis
- Burndown velocity indicators
- Bugs per 1000 lines of code by sprint
- Quality trend lines
- Risk zone highlighting
- Team allocation heatmap
- Availability patterns
- Capacity planning insights
- Customer satisfaction scores
- Trend analysis
- Satisfaction momentum
Most dashboards answer the question: "What happened?"
This dashboard answers: "What's about to happen?"
The difference is profound. By focusing on leading indicators—velocity stability, defect injection rates, team capacity headroom, and customer sentiment trends—you can:
- Predict risks 2-3 sprints ahead
- Course-correct before slipping schedules
- Make data-driven staffing decisions
- Present compelling stories to leadership
Leading indicators are where excellence happens.
delivery-excellence-dashboard/
├── README.md # This file
├── delivery_metrics.py # Main Dash application
├── requirements.txt # Python dependencies
├── LICENSE # MIT License
├── .gitignore # Git ignore rules
└── data/
└── sample_data.py # Sample data generator
Edit thresholds in delivery_metrics.py:
# Defect density thresholds (bugs per 1000 LOC)
DEFECT_DENSITY_WARNING = 5.0
DEFECT_DENSITY_CRITICAL = 8.0
# Velocity variance thresholds
VELOCITY_WARNING = 15 # % variance
VELOCITY_CRITICAL = 25 # % variance[Dashboard visualization showing sprint velocity trends, defect density, resource utilization, and CSAT metrics]
Feedback and contributions are welcome! Please feel free to:
- Report bugs via GitHub Issues
- Suggest features for new metrics
- Share how you're using this in your organization
This project is licensed under the MIT License - see the LICENSE file for details.
Copyright © 2025 Satya Praveen Vemuri
I'm a Program Manager & Data Analyst passionate about using data and visualization to drive delivery excellence at scale.
Currently managing:
- Multi-product portfolio delivery ($5M+ annual investment)
- Cross-functional teams (Engineering, Product, QA)
- Stakeholder communication and executive reporting
Connect with me:
Last Updated: February 2025
Built with attention to detail. For delivery leaders who demand precision.