Smart Inventory Optimization

A small project scaffold for time-series demand forecasting and inventory optimization. Includes data preprocessing, multiple models (ARIMA, Prophet, LSTM, XGBoost), ensembling, and a cost-based inventory optimizer.

Project structure

smart-inventory-optimization/
├── data/
│   ├── raw/
│   │   └── component_x_data.csv
│   ├── processed/
│   │   └── clean_data.csv
│   └── external/
│       └── holidays.csv
├── notebooks/
│   ├── 01_data_exploration.ipynb
│   ├── 02_feature_engineering.ipynb
│   ├── 03_model_training.ipynb
│   └── 04_ensemble_and_optimization.ipynb
├── src/
│   ├── data_preprocessing/
│   ├── models/
│   ├── optimization/
│   ├── evaluation/
│   └── utils/
├── app/
│   ├── api.py
│   └── dashboard.py
├── tests/
├── requirements.txt
├── .gitignore
└── main.py

Quick start

1. Clone the repo and cd into the project root.
2. Create and activate a virtual environment:
   • python -m venv .venv
   • Windows: .venv\Scripts\activate
3. Install dependencies:
   • pip install -r requirements.txt
4. Populate data/raw/component_x_data.csv with your data.
5. Run the pipeline stub:
   • python main.py

Development notes

• Notebooks contain exploratory and modeling steps; update paths if running from a different working directory.
• Fill stub modules in src/ with actual implementations as you develop models and optimization logic.
• Use tests/ for pytest unit tests.

Running tests

• Install pytest (already in requirements.txt) and run:
  • pytest tests/

Contributing

• Create feature branches and open PRs.
• Keep changes focused and add tests for new logic.

License

• Add an appropriate LICENSE file for your project.