Arvato, a global leader in logistics and supply chain management, unveils "Leleka", an advanced robotic arm designed to automate the picking and sorting of fashion items in warehouse environments. Developed entirely in-house by Arvato’s Research & Development team, Leleka is the perfect example for Arvato’s commitment to innovation and operational efficiency and has just been put into live operation at the Hannover site in Germany.
The robot operates by utilizing AI-driven vision capabilities to automate the process of picking and sorting large boxes containing hundreds of fashion items. With high precision and decision-making accuracy, the order fulfillment is extremely reliable and efficient. Integrated with the BagSorter system in Hanover, the robotic arm identifies the optimal spot to grasp each product, ensuring safe and efficient movement. It checks for product visibility, proper orientation, and barcode readability, managing irregularly shaped items arranged randomly within containers.
The primary motivation behind Leleka's implementation is to alleviate the most tedious and monotone manual tasks, such as moving products within the warehouse. While facing increasing volumes, recruiting new employees for basic warehouse processes is becoming more difficult as well. Combining a robotic arm with AI algorithms based on image processing, Leleka is designed to handle complex logistics processes with exceptional accuracy, enhancing the quality of our operations. Therefore, Leleka aligns perfectly with our automation strategy and serves our clients’ business needs.
“Leleka offers long-term financial benefits, addresses labor shortages, and reduces employee exhaustion by automating routine tasks. It enhances operational stability and efficiency, ready to support Arvato’s logistics processes around the clock”, says Sławomir Grzeskowiak, Head of Arvato’s Research & Development team that developed Leleka.
While maintaining the existing logistics processes, Leleka operates with the efficiency of a human worker but without productivity drops caused by fatigue. The system is optimized for 24/7 operation, ensuring continuous performance. It operates autonomously, with minimal human intervention required for tasks such as replacing bulk containers. Maintenance staff receive specialized training for initial startup, daily monitoring, and simple maintenance tasks. Currently, human-robot collaboration involves manual feeding of full containers, but future plans include automating this stage as well.
“The development of Leleka posed significant challenges, particularly in adapting theoretical concepts to real-world conditions. Products may be chaotically arranged in the containers, necessitating complex criteria adjustments. Through rigorous testing and adaptation, our project team successfully addressed these challenges and we gained valuable experiences and tons of data for future implementations”, explains Grzeskowiak.
