3PL Provider Prevents $3M+ ROI Loss on Robotics Investment With Digital Twin Technology

We started by modeling the fulfillment center’s existing operations using data from scanners, conveyors, and forklifts through AWS IoT Core and AWS Kinesis Data Stream. This gave us a live, accurate baseline of how goods moved, from receiving to packing.

Inside AWS TwinMaker, our engineers mapped every station, route, and timing cycle to visualize the “as-is” process. This became a digital twin of the fulfillment center.

That’s where we saw the first friction in the client’s current workflow:

• long dwell times in picking
• uneven conveyor loads during shift changes
• occasional queueing at packing lines during peak hours

Next, we integrated the vendor’s AMR specifications (speed, payload, battery cycles, and routing logic) into the digital twin. Using AWS SageMaker, we simulated real order patterns: mixed-SKU runs, large multi-item orders, seasonal peaks, and single-item express shipments.

The robots were about 40% faster at picking, but the rest of the system couldn’t keep up. Packing queues grew, conveyors filled, and workers had to wait for space to clear.

That imbalance meant the facility couldn’t reach the promised throughput. Comparing the simulated performance to the vendor’s ROI model, the return came out roughly 30% lower, the equivalent of more than $3M lost over the project’s payback period. That meant the client needed to improve their workflow.

We used the twin to test simple workflow changes: things like adding short-term packing buffers, tweaking robot routes, and adjusting handoff timing between zones.

ML models inside the simulation tracked utilization, travel distance, and idle time for each scenario. They showed that adding labor or hardware barely helped.

The best fix turned out to be simple: add a small buffer before packing and tweak the robot routing. It gave robots some space to drop goods when packers were full, so the whole line kept moving.

We visualized the final design in 3D and created dashboards comparing the vendor’s original setup with the optimized workflow we proposed. The vendor’s model predicted profit. Ours showed how to actually achieve it.

The Ops team began implementing the optimized workflow across the site. The leadership team then approved the investment, confident the new robotics system will provide the expected ROI.