Predictive Stockout & Delivery Optimizer 📈🚚

A data-driven supply chain management system designed to anticipate inventory shortages before they occur and streamline logistics. This tool leverages machine learning models to analyze historical demand patterns, lead times, and transit variables to ensure optimal stock levels and delivery efficiency.

📖 Table of Contents

• Features
• Technologies Used
• Installation
• Usage
• Roadmap
• Contributing
• License

🚀 Features

• Stockout Prediction: Advanced forecasting to identify high-risk SKUs.
• Route & Delivery Optimization: Minimizes delivery delays using real-time logistics data.
• Automated Reorder Alerts: Triggers notifications when predicted stock levels fall below safety thresholds.
• Interactive Dashboards: Visual representation of supply chain health and KPI metrics.

🛠️ Technologies Used

• Language: Python 3.8+
• Machine Learning: Scikit-learn, XGBoost / Prophet (for time-series forecasting)
• Data Processing: Pandas, NumPy
• Visualization: Matplotlib, Seaborn, or Streamlit
• Environment: Jupyter Notebook / Docker

⚙️ Installation

1. Clone the repository:

   git clone https://github.com/mtepenner/predictive-stockout-delivery-optimizer.git
   cd predictive-stockout-delivery-optimizer

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

💡 Usage

1. Data Preparation: Place your historical inventory and shipping CSV files in the /data folder.
2. Run Prediction: Execute the main script to generate stockout risks.

   python run_optimizer.py --input data/inventory.csv

3. View Results: Output logs and optimized schedules will be saved in the /output directory.

🗺️ Roadmap

• Integration with real-time ERP APIs.
• Multi-warehouse support for global supply chains.
• Deep Learning implementation for non-linear demand spikes.

🤝 Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

Distributed under the MIT License. See LICENSE for more information.