Supply Chain Knowledge Graph Data Model

To enable a consistent and structured digital representation of the brewery supply chain, we have developed a specialized knowledge graph data model tailored to the requirements of supply chain management. It defines the central entities, their properties, and relationships – forming the semantic foundation for supply chain management and optimization.

At the core of the Supply Chain model are the following domain-specific elements:

• Manufacturers: Companies that produce beer products, with attributes such as location, production capacity, and specializations
• Facilities: Production sites, breweries, tanks, and bottling lines with specific capacities, technologies, and equipment
• Products: Beer products and their variants with properties such as type, alcohol content, price, and production data
• Product Instances: Individual products with serial numbers and batch assignment
• Wholesalers: Distribution partners and distributors who source products from manufacturers and forward them to retailers
• Suppliers: Providers of raw materials, packaging, and supplies with ratings, lead times, and quality metrics
• Warehouses: Storage and distribution centers for raw materials and finished products with capacities and locations
• Raw Materials: Base materials for beer production such as malt, hops, yeast, water with quality grades and cost structures
• Components: Bottles, cans, labels, and other packaging elements
• Batches: Production batches with quantities, production date, and storage location
• Bill of Material: Recipes and ingredient lists for beer production
• Inbound Inspections: Quality checks of raw materials and components
• Distribution Channels: Logistics partners and transport routes

Data Quality and Traceability

All entities and relationships are enriched with metadata:

• Origin Information: Data sources and update timestamps
• Quality Metrics: Ratings, certifications, and compliance status
• Performance Metrics: KPIs for efficiency, costs, and sustainability

The semantic Supply Chain data model enables:

• Traceability: Complete tracking from raw materials to end product
• Optimization: Identification of bottlenecks and improvement opportunities
• Risk Management: Analysis of dependencies and alternative supply routes
• Sustainability Analysis: Assessment of carbon footprint and environmental impact

The data model is continuously updated from the knowledge graph and reflects the current structure of the supply chain.