AI-Driven Strategic Procurement & Spend Analytics for Enterprise Supply Chains
Part of the Quantisage SAGE Platform ecosystem
The Procurement Optimization platform is an AI-driven solution for enterprise procurement that combines spend analytics, strategic sourcing algorithms, contract lifecycle management, and autonomous purchasing agents. It delivers measurable cost savings while ensuring compliance and sustainability across the procurement lifecycle.
Capability Description Impact Spend Analytics AI-powered spend classification, tail spend analysis, maverick spend detection 15-25% cost visibility improvement Strategic Sourcing Multi-objective optimization for supplier selection & allocation 10-20% procurement cost reduction Contract Management Automated CLM with NLP-powered contract analysis & risk detection 30% faster contract cycles Demand Forecasting ML-based demand prediction for proactive procurement planning 25% inventory cost reduction Autonomous Agents AI agents for routine purchasing, price negotiation & order management 40% process automation Sustainability Scoring ESG-integrated sourcing with carbon footprint optimization Scope 3 emissions reduction
procurement-optimization/ ├── src/ │ ├── spend_analytics/ │ │ ├── __init__.py │ │ ├── classifier.py # AI spend classification engine │ │ ├── tail_spend.py # Tail spend analysis & consolidation │ │ ├── maverick_detector.py # Maverick spend detection │ │ ├── category_manager.py # Spend category management │ │ └── benchmarking.py # Price benchmarking & market intelligence │ ├── sourcing/ │ │ ├── __init__.py │ │ ├── optimizer.py # Multi-objective sourcing optimizer │ │ ├── auction_engine.py # Reverse auction & RFx management │ │ ├── allocation.py # Supplier allocation algorithms │ │ ├── total_cost.py # Total cost of ownership calculator │ │ └── sustainability.py # Sustainable sourcing optimizer │ ├── contracts/ │ │ ├── __init__.py │ │ ├── lifecycle.py # Contract lifecycle management │ │ ├── nlp_analyzer.py # NLP contract clause analysis │ │ ├── risk_detector.py # Contract risk detection │ │ ├── compliance.py # Contract compliance monitoring │ │ └── renewal_engine.py # Auto-renewal & renegotiation engine │ ├── agents/ │ │ ├── __init__.py │ │ ├── purchasing_agent.py # Autonomous purchasing agent │ │ ├── negotiation_agent.py # AI price negotiation agent │ │ ├── approval_workflow.py # Intelligent approval routing │ │ └── order_manager.py # Automated order management │ ├── forecasting/ │ │ ├── __init__.py │ │ ├── demand_predictor.py # ML demand forecasting │ │ ├── price_predictor.py # Commodity price prediction │ │ └── lead_time_estimator.py # Supplier lead time estimation │ └── api/ │ ├── __init__.py │ ├── main.py # FastAPI application │ └── routes/ │ ├── spend.py # Spend analytics endpoints │ ├── sourcing.py # Sourcing optimization endpoints │ ├── contracts.py # Contract management endpoints │ ├── agents.py # Agent management endpoints │ └── forecasting.py # Forecasting endpoints ├── tests/ │ ├── test_spend_analytics/ │ ├── test_sourcing/ │ ├── test_contracts/ │ └── test_agents/ ├── notebooks/ │ ├── spend_classification.ipynb │ ├── sourcing_optimization.ipynb │ └── demand_forecasting.ipynb ├── config/ │ ├── categories.yaml │ ├── sourcing_rules.yaml │ └── agent_config.yaml ├── requirements.txt ├── Dockerfile └── README.md
Automatically classify and categorize procurement spend using NLP and ML models with 95%+ accuracy across UNSPSC and custom taxonomies.
Optimize supplier selection balancing cost, quality, delivery, risk, and sustainability using genetic algorithms and linear programming.
Extract key clauses, identify risks, and benchmark terms across contract portfolios using transformer-based NLP models.
Deploy AI agents that handle routine procurement activities including purchase order creation, price negotiation, and supplier communication.
Forecast procurement needs using ensemble ML models combining historical patterns, market signals, and business drivers.
Integrate Scope 3 emissions data and ESG scores into sourcing decisions for carbon-optimized procurement.
# Clone the repository git clone https://github.com/virbahu/procurement-optimization.git cd procurement-optimization # Create virtual environment python -m venv venv source venv/bin/activate # Install dependencies pip install -r requirements.txt # Run the API server uvicorn src.api.main:app --reload --port 8002
Method Endpoint Description POST/api/v1/spend/classifyClassify procurement spend GET/api/v1/spend/analyticsGet spend analytics dashboard POST/api/v1/sourcing/optimizeRun sourcing optimization POST/api/v1/contracts/analyzeAnalyze contract with NLP POST/api/v1/agents/purchaseTrigger autonomous purchase GET/api/v1/forecast/demandGet demand forecast
- supplier-risk-scoring — Supplier risk assessment for sourcing
- supply-chain-ai-agents — Multi-agent procurement automation
- demand-forecasting-engine — Advanced demand prediction
inventory-optimization — Inventory-aware procurement planning
- sage-compliance-engine — Procurement compliance
- Backend: Python 3.10+, FastAPI, Pydantic
- ML/AI: scikit-learn, XGBoost, PyTorch, Transformers
- Optimization: PuLP, SciPy, DEAP (genetic algorithms)
- NLP: spaCy, Hugging Face Transformers
- Database: PostgreSQL, Elasticsearch
Agents: LangChain, CrewAI
- Containerization: Docker, Kubernetes
Quantisage — Where Growth Meets Compliance
- Email: info@quantisage.com
Founder: Vir — vir@quantisage.com
This project is licensed under the MIT License — see the LICENSE file for details.
virbahu/procurement-optimization
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