Our client, a Dutch conglomerate of feed factories, needed to accurately assess the carbon footprint of their products. The lack of a streamlined process to combine research, procurement, and production data hindered the factories’ ability to optimize their feed recipes for minimum ecological impact. This case study highlights how Versatyle utilized AI-driven solutions to address these challenges and improve sustainability.
- Accurately assessing the carbon footprint of feed products.
- Integrating research, procurement, and production data to optimize feed recipes.
- Enhancing sustainability efforts within the feed factories.
Versatyle leveraged its data expertise and AI-driven solutions to address the client’s needs effectively:
- Integration: The Enterprise Resource Planning (ERP) system of the feed factories was integrated with the Versatyle Data Platform™.
- Semantic Matching: A large language model was employed to semantically match raw materials from the ERP system with available research data on carbon footprints from open databases.
- Real-time Insights: The AI model calculated the carbon footprint of each ingredient, providing real-time insights into their ecological impact. This allowed for automatic substitution with more eco-friendly alternatives during production.
- Visual Dashboards: Versatyle established visual dashboards to track ongoing improvements. These dashboards enabled procurement to make informed decisions about sourcing materials with lower carbon emissions, further reducing ecological impact.
- Transparency: The solution provided visual evidence of the commitment to sustainability, which was shared with customers and supply chain partners, showcasing the progress made over time.
The feed factories’ ERP system is now continuously enriched with accurate carbon footprint data for every sourced material. This data-driven approach has enabled the factories to optimize their recipes, leading to a reduction in their global ecological impact.
This technology has a wide range of potentially disruptive use cases. It facilitates large-scale matching, categorization, and deduplication of products and materials based on their name and description. Its adaptability, relying on language rather than industry-specific knowledge, makes it suitable for various business contexts.