Sustainable Sourcing

Use AI to improve supplier sustainability risk management and supply chain transparency.

Business impact

• Supply chain risk — AI identifies and mitigates sustainability risks reducing disruptions and losses
• Supplier compliance rate — Monitors adherence to sustainability standards improving overall compliance
• Sustainability performance — Tracks and improves environmental and social metrics across suppliers
• GHG emissions from supply chain — Enables measurement and reduction of carbon footprint in sourcing
• Procurement cost efficiency — Optimizes sourcing decisions balancing cost with sustainability goals

Data requirements

• Supplier sustainability reports (Structured) — Provide direct data on environmental and social performance
• Customs and trade records (Structured) — Offer supplier activity and origin data for verification
• Web crawling and public data (Text) — Aggregate external information on suppliers’ sustainability practices
• IoT and sensor data (Numeric) — Enable real-time monitoring of supply chain environmental metrics
• Blockchain ledgers (Code) — Ensure traceability and transparency of supplier transactions

AI methods and techniques

• Predictive AI — Forecast supplier risks and sustainability performance trends
• Generative AI — Generate insights and reports from unstructured supplier data
• Agentic AI — Automate supplier engagement and compliance monitoring tasks

AI models and model families

GPT-4, Claude, Llama 2, Custom predictive models

Industries

Real-world evidence

5 documented case studies on record.

Companies using this: Accenture, Glencore, Matchory, Saferoad, Trafigura.