Even though there is no uniform definition of AI, it basically addresses the development of computer systems to understand and mimic human behaviour. AI as ‘a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation. Apart from applications such as self-driving cars or speech recognition, AI also has a wide range of uses in supply chain management, such as forecasting, routing, supplier selection, and inventory management.

Various approaches have been proposed in the last decades for each of those fields. Some of them address not only one single domain of application, e.g. routing, but also a combination of multiple domains, such as inventory routing. However, at vertices we focus primarily on inventory management problems where AI-based methods are particularly useful when an optimal order policy is either infeasible or too expensive to implement.

The planning engine has always been at the core of manufacturing management systems from Material Requirements Planning (MRP) to Manufacturing Resource Planning (MRPII) to today’s Enterprise Resource Planning (ERP). For much of that evolution, the effectiveness of the planning process was limited by the initial design of MRP planning, which was designed to work within the limitations of early computer technology.