Arvato and Microsoft join forces to develop self-managing warehouse of the future

The innovative research project “Moonshot” aims to implement self-improving processes within the warehouse environment by leveraging generative artificial intelligence (AI), robotics, and cloud computing. Arvato and Microsoft will showcase the visionary outlook and initial use cases of this project at the Gartner Supply Chain Symposium/Xpo 2024, 10 - 12 June 2024, Barcelona, Spain. The Gartner® Conference, held at the Barcelona Convention Center, is expected to attract around 1,600+ peers and vet new technologies at the Supply Chain/Xpo.

Arvato will feature its own booth, hosted by logistics experts from its Tech, Consumer Products, and Healthcare business units, who will be available to discuss the joint research project with Microsoft and the evolving challenges in supply chain management.

“We launched the Moonshot project with Microsoft in March, aiming for the ambitious goal of creating an autonomous warehouse," says Paul Brolly, Vice President of Global Business Development at Arvato. "Our approach involves examining various warehouse processes to automate and enhance them through robotics, digitalization, and AI. The focus isn't solely on individual processes. In the future, AI could handle the initial overall warehouse layout for new orders based on customer requirements, which we would then refine with our expertise. This approach saves time and effort.”

Microsoft, as Arvato's technology partner, plays an essential role in this initiative. "Microsoft's expertise in data platforms, compute capacities, Gen AI forecasting, computer vision, augmented reality, and cognitive services like speech recognition and text analysis is vital in realizing our vision of a self-managed warehouse," emphasized Brolly.

More importantly, Arvato can already demonstrate the practical applications of the Moonshot project. One use case optimizes picking routes using AI to reorganize task orders through rapid data analysis, enhancing navigation for pickers. Another use case involves AI-controlled interpretation of carrier responses in transport complaints. The AI Smart Layer interprets these responses and independently triggers appropriate actions in the claim management tool, freeing up resources by reducing manual complaint processing to three to four percent.