Self-Managed Warehouse

In the past, Warehouse Control Systems (WCS) focused on short-term gains, leading to inefficiencies in warehouse management. The Self-Managed Warehouse project changes this with a revolutionary approach using AI techniques like mathematical optimization, machine learning, and digital twin modeling. This project introduces a two-way digital twin, a virtual model that mirrors and interacts with every part of the warehouse in real-time. This model allows for seamless integration of maintenance and operational plans, ensuring optimal efficiency. The evolving digital twin covers all warehouse components, from storage systems to robotic arms, constantly improving and expanding in scope. This integration not only boosts efficiency but also enhances maintenance and planning, benefiting the entire warehousing sector. Moreover, the project's findings and advancements are shared with industry partners, fostering widespread improvements in warehouse management. This initiative marks a shift towards warehouses that are not just storage spaces, but smart, self-managing ecosystems, leading to a more sustainable and efficient future.

An efficient Warehouse Control System (WCS) is essential for an efficient, sustainable, and labor-friendly warehouse. For too long, warehouses have been managed using myopic WCSs that lack proper integration with the physical components and processes within the warehouse, favoring short-term rewards versus long-term insights. The Self-Managed Warehouse project aims at a drastic improvement in warehouse operations and maintenance across the sector. Using an efficient suite of AI techniques composed of mathematical optimization, machine learning and digital twin modeling, a platform will be built that enables coordination of maintenance services with operational requirements, improves warehouse performance, and helps with scenario and what-if analyses of the system. Using up-to-date representations of the real world, decisions on one side of the table will lead to updates on the other side, ensuring that all involved parties are operating with updated best plans and at best possible efficiency. Through the project a two-way digital twin will be iteratively improved, covering a larger and larger portion of the warehouse – from storage systems to conveyor belts to robotic piece picking arms and beyond. At the same time, the decision support algorithms integrated with the two-way digital twin will be expanded to be able to cope with the new components, enabling an increasingly holistic improvement of the warehouse, able to take the performance of the entire system – and links to maintenance restrictions – into account. Better understanding of and support for maintenance and planning in warehouses will be good for the entire warehousing sector, and the project aims to disseminate and share its major findings with the industry through regular meetings and interactions with partners within and without the project consortium.