Procurement Optimization AI Agent

An intelligent AI agent that autonomously analyzes hospital purchase orders and vendor pricing to predict optimal procurement strategies for cost and availability optimization.

Features

• Autonomous Analysis: AI agent analyzes purchase orders and vendor data
• Cost Optimization: Predicts optimal procurement strategies for cost reduction
• Availability Forecasting: Forecasts demand and availability patterns
• Vendor Analysis: Evaluates vendor performance and pricing
• Interactive Dashboard: Streamlit-based user interface
• Open Source: Uses local LLMs (Ollama/Mistral-7B) without paid APIs

Tech Stack

• Agentic AI: LangChain + LangGraph
• LLM: Ollama (Mistral-7B/LLaMA-3) or Hugging Face Transformers
• Data Handling: Pandas + SQLAlchemy
• Analytics: Scikit-learn + XGBoost
• UI: Streamlit
• Visualization: Plotly + Matplotlib

Installation

1. Clone the repository:

git clone <repository-url>
cd procurement-optimization-ai

2. Install dependencies:

pip install -r requirements.txt

3. Install Ollama (for local LLM):

# Download from https://ollama.ai/
# Then run:
ollama pull mistral:7b

4. Run the application:

streamlit run app.py

Project Structure

├── app.py                          # Main Streamlit application
├── agent/
│   ├── __init__.py
│   ├── procurement_agent.py        # Main AI agent logic
│   ├── llm_interface.py           # LLM interface (Ollama/HuggingFace)
│   └── graph_workflow.py          # LangGraph workflow
├── data/
│   ├── __init__.py
│   ├── database.py                # SQLAlchemy database models
│   ├── sample_data.py             # Sample hospital data generator
│   └── data_processor.py          # Data processing utilities
├── models/
│   ├── __init__.py
│   ├── demand_forecaster.py       # Demand forecasting models
│   ├── cost_optimizer.py          # Cost optimization algorithms
│   └── vendor_analyzer.py         # Vendor analysis models
├── utils/
│   ├── __init__.py
│   ├── visualization.py           # Plotting utilities
│   └── config.py                  # Configuration settings
└── requirements.txt

Usage

1. Start the Application: Run streamlit run app.py
2. Upload Data: Upload hospital purchase orders and vendor data
3. Configure Agent: Set analysis parameters and preferences
4. Run Analysis: Let the AI agent analyze and provide recommendations
5. View Results: Explore interactive visualizations and reports

Key Components

AI Agent (LangChain + LangGraph)

• Autonomous analysis of procurement data
• Multi-step reasoning workflow
• Cost-benefit analysis
• Vendor evaluation

Demand Forecasting (Scikit-learn + XGBoost)

• Time series analysis
• Seasonal pattern detection
• Demand prediction models
• Inventory optimization

Cost Optimization

• Vendor price comparison
• Bulk purchase optimization
• Contract negotiation insights
• Budget allocation strategies

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Contact:

for any inquiries : vikhrams@saveetha.ac.in