Modern fulfilment centres operate in highly dynamic environments where customer orders, inventory availability, robot locations, and workforce capacity are constantly changing. Efficiently assigning work is critical to maintaining throughput, reducing delays, and making effective use of both people and automation.

The Intelligent Task Assignment & Optimization platform focuses on determining the most effective allocation of work across warehouse associates and autonomous mobile robots. By continuously evaluating operational conditions and business priorities, the platform helps improve resource utilisation, reduce unnecessary movement, balance workloads, and increase overall fulfilment efficiency.

As part of the engineering leadership team, I worked on designing and evolving the task assignment capabilities within the Assisted Picking ecosystem. The objective was to build a flexible optimisation framework capable of adapting to changing warehouse conditions while supporting different operational strategies and customer requirements.

The platform became a key component of warehouse operations, enabling organisations to make better real-time decisions while improving productivity, scalability, and operational consistency.

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Technology Stack

• Java
• Spring Boot
• Apache Kafka
• PostgreSQL
• Redis
• Docker
• Kubernetes