Predictive analytics in inventory management has not been the traditional domain of knowledge graphs and semantics; however, it is a surprisingly natural fit. This talk will review knowledge graphs in the supply chain and look into the details of implementation. In our central case, using a semantic model, we build a ‘digital twin’ of a complex inventory management supply chain. Data from heterogeneous sources - including warehouse management systems, point of sale systems and weather data – are then imported into the knowledge graph. Using the graph we carry out analytics, optimization, scheduling and Monte Carlo simulations. A complex set of operations built around the central supply chain knowledge graph. The net result is a predictive analytic system that delivers real value to the enterprise (up to a 50% reduction in inventory). The knowledge graph can be extended to include product information and other central commercial data use cases. This presentation will draw on the production delivery of TerminusDB to the largest retailer in Ireland.