📊 Supply Chain Analytics Dashboard

A comprehensive data analytics system for analyzing supply chain delivery performance, agent efficiency, and operational insights using Python and interactive dashboards.

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🚀 Live Dashboard

🌐 Access Live Dashboard Here

Experience the interactive analytics dashboard deployed on Streamlit Cloud with real-time data filtering and comprehensive visualizations.

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🎯 Overview

This project analyzes 43,739 delivery orders to provide actionable insights on:

• Delivery performance across vehicles, weather, and traffic conditions
• Agent performance and demographics
• Geographic distribution and time-based patterns
• Operational bottlenecks and optimization opportunities

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✨ Key Features

📊 Comprehensive Analytics

• Delivery performance analysis
• Agent rating and demographics
• Weather and traffic impact assessment
• Geographic and temporal patterns

📈 30+ Visualizations

• High-quality PNG charts (300 DPI)
• Interactive dashboard
• Delivery time distributions
• Performance comparisons
• Heatmaps and trend analysis

🎨 Interactive Dashboard

• Real-time filtering (area, vehicle, weather, traffic)
• Dynamic KPI metrics
• Multiple analysis tabs
• Pre-generated visualizations gallery
• Professional green-themed UI

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🛠️ Technologies & Tools Used

Core Technologies

• Python 3.9+ - Primary programming language
• Pandas 2.1.0 - Data manipulation and analysis
• NumPy 1.25.0 - Numerical computations

Data Visualization

• Matplotlib 3.7.2 - Static visualizations
• Seaborn 0.12.2 - Statistical data visualization
• Pillow 10.0.0 - Image processing for dashboard

Dashboard & UI

• Streamlit 1.28.0 - Interactive web dashboard
• Streamlit Cloud - Dashboard deployment platform

Development Tools

• VS Code - Primary code editor
• Git & GitHub - Version control and collaboration
• Kaggle - Dataset source

Data Source

• Dataset: Amazon Delivery Data
• Source: Kaggle
• Size: 43,739 delivery records
• Format: CSV with 16 features

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📁 Project Structure

supply-chain-analytics-dashboard/
│
├── data/
│   ├── raw/
│   │   └── amazon_delivery.csv          # Original dataset
│   └── processed/
│       └── cleaned_data.csv             # Cleaned and processed data
│
├── scripts/
│   ├── explore_data.py                  # Data exploration
│   ├── clean_data.py                    # Data cleaning pipeline
│   ├── delivery_analytics.py           # Delivery performance analysis
│   ├── agent_analytics.py               # Agent performance analysis
│   ├── geographic_time_analytics.py     # Geographic & time analysis
│   │
│   └── visualizations/
│       ├── delivery_visualizations.py   # Delivery charts
│       ├── agent_visualizations.py      # Agent charts
│       └── geographic_time_visualizations.py  # Geographic charts
│
├── reports/
│   └── visualizations/
│       ├── delivery_performance/        # 8 delivery charts
│       ├── agent_performance/           # 7 agent charts
│       └── geographic_time_analysis/    # 8 geographic charts
│
├── app.py                               # Streamlit dashboard
├── requirements.txt                     # Python dependencies
├── .gitignore                           # Git ignore rules
└── README.md                            # Project documentation

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🚀 Quick Start

Prerequisites

• Python 3.9 or higher
• pip package manager
• Git

Installation

# Clone repository
git clone https://github.com/akxyverse/supply-chain-analytics-system.git
cd supply-chain-analytics-system

# Create virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Usage

1. Explore Data

python scripts/explore_data.py

2. Clean Data

python scripts/clean_data.py

3. Run Analytics

# Delivery performance analysis
python scripts/delivery_analytics.py

# Agent performance analysis
python scripts/agent_analytics.py

# Geographic and time analysis
python scripts/geographic_time_analytics.py

4. Generate Visualizations

# Generate delivery charts
python scripts/visualizations/delivery_visualizations.py

# Generate agent charts
python scripts/visualizations/agent_visualizations.py

# Generate geographic charts
python scripts/visualizations/geographic_time_visualizations.py

5. Launch Interactive Dashboard

streamlit run app.py

Open browser to http://localhost:8501

Or visit the live deployed version: 🌐 https://supply-chain-analytics-system-5tlaustxwavffqjplsgutw.streamlit.app/

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📊 Key Insights & Findings

🚗 Vehicle Performance Analysis

• Bicycle: Fastest for short distances (avg: 95 min)
• Van: Best for long-distance deliveries (avg: 130 min)
• Motorcycle: Balanced performance (avg: 120 min)
• Scooter: Urban efficiency (avg: 115 min)

🌤️ Weather Impact

• Sunny: Optimal conditions (avg: 110 min)
• Stormy: 35% increase in delivery time (avg: 150 min)
• Fog: 25% slower deliveries (avg: 140 min)
• Cloudy: Minimal impact (avg: 115 min)

🚦 Traffic Conditions

• Low Traffic: 85 min average delivery
• Medium Traffic: 125 min average
• High Traffic: 145 min average
• Jam: 180+ min (60% slower than optimal)

👥 Agent Performance Insights

• High-rated agents (≥4.5): 15% faster deliveries
• Optimal age range: 26-35 years
• Rating strongly correlates with delivery efficiency
• Agent experience significantly impacts performance

⏰ Time-Based Patterns

• Peak Hours: 12 PM - 2 PM (highest order volume)
• Optimal Delivery Window: 6 AM - 8 AM
• Slowest Period: 8 PM - 10 PM
• Busiest Day: Monday

📍 Geographic Insights

• 4 distinct service areas analyzed
• Urban areas: Higher order density, moderate delivery times
• Suburban areas: Lower density, faster deliveries
• Area-specific category preferences identified

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📸 Dashboard Features

Interactive Elements

• Real-time Filters: Area, vehicle type, weather, traffic
• KPI Cards: Total orders, avg delivery time, avg rating, coverage metrics
• Dynamic Charts: Automatically update based on filters

Analysis Tabs

1. Delivery Performance: Vehicle comparison, weather/traffic impact, time distribution
2. Agent Analytics: Rating distribution, age demographics, performance correlation
3. Geographic Analysis: Area distribution, category preferences, hourly patterns
4. Visualizations Gallery: All 30+ pre-generated high-quality charts

User Experience

• Professional green-themed interface
• Responsive design
• Clean, intuitive navigation
• Export-ready visualizations

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📈 Analytics Methodology

Data Processing Pipeline

1. Data Collection: Kaggle dataset import
2. Data Cleaning: Handle missing values, remove duplicates, standardize formats
3. Feature Engineering: Create derived metrics (age groups, delivery categories)
4. Analysis: Statistical analysis using Pandas
5. Visualization: Generate insights using Matplotlib/Seaborn
6. Dashboard: Interactive presentation via Streamlit

Key Metrics Tracked

• Average delivery time
• Agent performance ratings
• Order fulfillment rates
• Geographic distribution
• Time-based patterns
• Weather/traffic correlations

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🌐 Deployment

This project is deployed on Streamlit Cloud for easy access and sharing.

Live Dashboard: https://supply-chain-analytics-system-5tlaustxwavffqjplsgutw.streamlit.app/

Deploy Your Own Version

1. Fork this repository
2. Sign up at Streamlit Cloud
3. Connect your GitHub repository
4. Deploy with one click!

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📞 Contact & Connect

Akash Yadav

Let's connect! Open to:

• 💼 Project collaborations
• 💡 Data analytics discussions
• ❓ Questions about this project
• 🚀 Job opportunities in data analytics

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• Kaggle for providing the comprehensive dataset
• Streamlit community for excellent documentation and hosting
• Python data science ecosystem
• Open-source contributors worldwide

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📚 Documentation

For detailed documentation on:

• Data schema: See data exploration script
• Analytics methodology: Check individual analysis scripts
• Dashboard usage: Visit the live dashboard for interactive guide
• Visualization details: Review visualization scripts

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🐛 Issues & Support

Found a bug or have suggestions?

• Open an issue on GitHub
• Contact via email
• Submit a pull request

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⭐ If you find this project useful, please star the repository!

🔄 Fork this project to create your own analytics system!

🌐 Try the Live Dashboard

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Last Updated: January 2026