Sustainable Sourcing

Supplier ESG and operational data analyzed to predict risks and optimize sustainable sourcing decisions.

Business impact

• Carbon emissions — Lower emissions by selecting suppliers with verified sustainable practices and recycled content
• Supply chain resilience — Enhanced by real-time monitoring and predictive analytics to anticipate disruptions
• Responsible sourcing due-diligence assessments — Increased number and quality of ESG assessments improve supplier compliance and transparency

Data requirements

• Supplier ESG reports (Structured) — Provide structured data on environmental, social, and governance performance
• Process and energy consumption data (Numeric) — Used to analyze operational efficiency and carbon footprint of suppliers
• Recycling and waste management records (Structured) — Inform circular economy initiatives and recycled content usage
• Market and customs data (Structured) — Support supplier discovery and verification through external trade records
• Textual supplier communications (Text) — Enable natural language processing to extract relevant sustainability commitments

AI methods and techniques

• Predictive AI — Forecast supply chain risks and sustainability performance trends for proactive sourcing
• Generative AI — Generate supplier risk summaries and sustainability reports from unstructured data
• Symbolic AI — Apply rule-based compliance checks against sustainability standards and regulations

AI models and model families

GPT-4o, Claude, Llama

Industries

Real-world evidence

2 documented case studies on record.

Companies using this: Glencore, Trafigura Group Pte Ltd.