DHL Supply Chain

DHL Supply Chain, the global leader in contract logistics, operates an extensive network of warehouses serving enterprise clients across multiple industries. Picking operations — the labor-intensive process of locating and retrieving items for fulfillment — represent a persistent bottleneck in warehouse throughput. As e-commerce volumes and customer service expectations continue to rise, manual picking struggles to scale without proportional labor cost increases. DHL needed automation that could flex across changing SKU mixes and facility layouts throughout its diverse client portfolio, while also addressing workforce challenges around ergonomics and retention — without displacing the human workers central to its service model.

To address these constraints, DHL Supply Chain announced a strategic partnership with Robust.AI in February 2024, piloting 'Carter' — a collaborative mobile robot built on reinforcement learning and optimization techniques that allow it to learn and adapt to real-time warehouse conditions. Unlike fixed automation, Carter continuously refines its routing and workflow decisions during operation, maximizing picking efficiency without constant reprogramming as conditions change. Deployment began with a live pilot at a DHL Supply Chain facility, with full rollout planned for later in 2024. Carter's embedded sensor array goes beyond its primary picking function, capturing operational data to surface insights on warehouse layout optimization, staffing allocation, and inventory positioning. The robot integrates into DHL's existing automation fleet — autonomous forklifts, robotic carton-unloading arms, and autonomous mobile robots — as a complementary layer rather than a replacement system.

Initial pilots demonstrated meaningful productivity increases in picking operations, confirmed by both organizations at the time of announcement in February 2024. While specific percentage improvements were not disclosed, Robust.AI CEO Anthony Jules stated that cooperative work between the teams validated the productivity gains at scale. Beyond throughput, Carter's sensor data is generating actionable intelligence extending well beyond the robot's primary task:

• Picking productivity: Meaningful throughput increases confirmed in live pilot conditions
• Secondary data value: Embedded sensors producing operational insights across warehouse layout, staffing, and inventory management dimensions
• Fleet integration: Carter deployed alongside existing autonomous forklifts, AMRs, and carton-unloading robotic arms without displacing existing automation investments

• AI-powered collaborative robots designed to augment rather than replace workers can align efficiency goals with improved employee experience — a differentiator in high-turnover warehouse environments.
• Deep operator domain knowledge combined with specialist AI expertise accelerates product refinement; DHL's logistics analytics directly shaped Carter's workflow optimization.
• Sensor-rich robots generate a secondary stream of operational intelligence (layout, staffing, inventory) beyond their primary task, compounding ROI over time.
• Piloting in a live facility before broad rollout reduces integration risk across a diverse multi-site logistics network.
• Treating warehouse automation as a layered portfolio — adding collaborative robots alongside existing AGVs and AMRs — enables incremental capability gains without wholesale system replacement.