Case Study

30% faster processing across 6 locations, once they could see where time was lost.

DaVinci Micro Fulfillment | Multi-location | 6 warehouses

Performance Prevention

DaVinci Micro Fulfillment warehouse operation

Average processing time decrease

Exception rate

Locations optimized

The situation

Processing was too slow. Nobody could pinpoint why.

DaVinci Micro Fulfillment operates 6 fulfillment centers. Orders were processing slower than they should have been, leadership knew that. Exception rates were running at 7.5%, well above acceptable levels. But identifying the root causes was a different problem entirely.

WMS reports showed what shipped and when. They showed order counts, throughput numbers, exception flags. What they couldn't show was why. Why was one station consistently slower than the others? Why were exception rates high on certain shifts? Why did processing times vary so much across locations running the same workflows?

The data gap wasn't in what the digital systems tracked. It was in what they couldn't see: the physical reality at each station. Time lost between orders. Operators improvising around unclear SOPs. Exceptions that triggered manual interventions with no visibility into how long those interventions actually took.

DaVinci had the numbers. They didn't have the context behind the numbers. And without that context, every improvement effort was guesswork.

What changed

Per-station activity data revealed exactly where time was wasted.

Rabot was deployed across all 6 DaVinci locations. Cameras at every station began capturing the physical activity of every order, not just what the WMS logged, but what actually happened during each packout.

For the first time, DaVinci could see the full picture. Per-station, per-operator activity data showed exactly where time was being lost: idle gaps between orders, excessive time on dunnage and packaging steps, stations waiting on exception resolution with no visibility upstream.

The data didn't just confirm what leadership suspected. It revealed problems they hadn't known existed. Specific stations were bottlenecks, not because of the operators, but because of the workflow design at those stations. Certain exception types were consuming disproportionate time across specific locations. The same SOP was being executed differently at different sites.

With that visibility, DaVinci's operations team made targeted changes. They restructured workflows at bottleneck stations. They automated steps that were consuming manual time. They standardized SOP execution across locations using Rabot's work instructions so that every operator, at every site, followed the same process.

The improvements weren't theoretical. They were specific, measurable, and driven by data that had never existed before.

The results

The numbers across 6 locations.

Average processing time

Decrease across all locations

7.5% → 2%

Exception rate

Dropped across locations

Locations optimized

Capacity utilization improved

Processing time reduction measured as an average across all 6 DaVinci locations. The 30% reduction reflects the combined impact of bottleneck identification, workflow restructuring, and standardized work instructions.

The exception rate improvement (7.5% to 2%) was observed and partially replicated. Results varied by site and by the specific nature of exceptions at each location.

"After installing Rabot, we were able to say 'look at how much time we're wasting on every order.' Rabot enabled us to give those areas attention and automate steps for a more seamless packout workflow."

Drake Meyer

VP Operations, DaVinci Micro Fulfillment

What this story proves

Performance. Prevention.

Performance

Run tighter every week.

DaVinci knew processing was slow but couldn't pinpoint why. Per-station activity data revealed exactly where time was wasted: idle gaps, exception bottlenecks, inconsistent workflows across locations. With that visibility, they restructured operations and cut processing time 30%.

Prevention

Stop errors before they ship.

Exception rates were running at 7.5% with no root-cause visibility. Rabot showed where and why exceptions were occurring, enabling targeted fixes. The rate dropped to 2%, and the team could see problems forming before they became costly.