FMCG

AI technology is already changing the FMCG industry. From global > regional > local forecasting to supply chain analytics to enterprise knowledge graphs, agentic architectures built on a back-bone of graph technology are no longer in proof-of-concept - they're live in production right now.

USE CASES

Bill-of-Material (BOM) Analytics: Profit Margin from Finished-Goods to Ingredients

We created a graph-based bill-of-material analytics platform for a global giant FMCG.

Key features:

Attribution of turn-over and profit at finished-good level to ingredient and packaging
Recommendations for product de-listing
Recommendations for ingredient substitution

Key elements of the solution architecture:

Full SAP data across all global sites
Databricks data lake
Graph database
React UI

Procurement Pricing and Coverage Agent: Raw Materials

We helped a multinational FMCG with a $5B+ raw materials portfolio to create an LLM + Knowledge Graph solution for procurement contract analysis and price extraction.

Key features:

Unstructured document analysis
Automated price extraction
Automated coverage extraction - volumes and prices
Natural language querying

Key elements of the solution architecture:

Google cloud stack
Vertex.ai
Spanner Graph
Gemini chat agent

Allocations: What to Do When Orders Outrun Inventory and Production

We designed and built a solution to efficiently allocate restricted stock to customers. Our product simplified the process of managing and distributing restricted stock, offering customers a seamless allocation process based on relationship prioritisation parameters set by the business.

Key features:

Automated allocation: effortlessly allocate restricted stock based on customizable criteria
Secure management: ensure data security and compliance with industry standards
Real-time tracking: monitor stock allocation and customer activity with ease

Key elements of the solution architecture:

Excel-first data ingestion
Graph database engine
Excel-first allocation outputs and recommendations