Most warehouse systems only show you what’s happening at a single site. But what if you could compare performance across your entire network and immediately see what’s working, where, and why?
Even when teams use the same systems, follow the same processes, and work toward the same goals, performance can vary widely from one facility to another. Some sites fly through orders. Others struggle with the exact same SKUs. And often, there’s no easy way to understand what’s driving the difference.
Traditional metrics like units per hour (UPH) or average pick time tell part of the story, but not enough to pinpoint root causes or replicate what’s working across the network.
In this post, we’ll show how inVia Logic WES provides cross-site visibility into your operation, helping you identify hidden inefficiencies and scale high-performing processes across every facility.
Seeing the ‘Why’ Behind Performance Variations
It’s easy to assume that if two warehouses are following the same processes and using the same systems, their performance should be more or less identical. But in practice, that’s rarely the case.
Many differences, such as how teams operate, how fast they pick certain SKUs, how decisions are made on the floor, or where exactly the inventory is placed, can lead to significant performance gaps over time. And without the ability to compare sites directly, those gaps often go unnoticed or unexplained.
Traditional metrics like units per hour (UPH) or average pick time tell part of the story, but not enough to pinpoint root causes or replicate what’s working across the network.
The most effective way to surface those insights is by capturing the operation in high resolution.
inVia Logic captures every scan, pick, and movement in real time, down to the millisecond. This granular data becomes the foundation for AI-powered insights that reveal how each site, team, and SKU is performing. When this level of data is collected and interpreted at scale, patterns begin to emerge. AI can establish a baseline of what’s typical, flag deviations, and highlight where one site is outperforming another.
inVia Logic collects thousands of data points each day, then identifies patterns and outliers.
Comparing Sites, SKU by SKU
One of the essential visualizations in the inVia Logic Business Intelligence dashboard enables you to compare how long it takes to pick the same SKU at different facilities. It’s a powerful way to uncover performance gaps that might otherwise go unnoticed.
In this view, each dot represents a SKU that’s picked at different sites. The x-axis shows the average pick time per unit at Site A, and the y-axis shows the same metric at Site B. A diagonal line represents equal performance—if a SKU takes 5 seconds to pick at both locations, it will fall on that line.
But in many cases, the data clearly pulls to one side. That’s what happened here: the majority of SKUs were picked significantly faster at one site, with points clustering well below the diagonal.
This isn’t just a chart. It’s a diagnostic tool. It shows where, and by how much, performance varies, helping teams identify the underlying causes: Are there layout differences? Is one team skipping steps? Are workers more experienced?
inVia Logic BI Dashboard: SKU pick-speed comparison across sites.
Comparing Worker Performance Across Sites
Another visualization in the inVia Logic BI dashboard maps individual worker performance across facilities, highlighting differences in both travel time and pick time. Each dot represents a single worker. The x-axis shows their average travel time, while the y-axis shows their average pick time. The color indicates which site they’re from, and the size of the dot reflects the total number of picks they’ve completed.
This kind of comparison becomes especially useful when evaluating performance across different types of facilities. It helps distinguish between human variation and systemic design differences in layout, training, or workflow.
inVia Logic BI Dashboard: User comparison across sites.
Beyond the Dashboard: How AI Pinpoints Root Causes
The power of an AI-enabled WES isn’t just in the graphs and data—it’s in what it uncovers beneath them.
After surfacing performance differences across sites, SKUs, or individual workers, inVia Logic AI steps in to identify the patterns behind those gaps. It learns what “typical” looks like for each process and flags when something’s off, whether it’s a SKU that takes twice as long to pick at one site, or a team that’s falling behind despite having similar resources.
These insights go beyond surface-level metrics. They help operations teams get to the root cause of inefficiencies, replicate what’s working, and shift from observing problems to solving them.
Want to see how AI can improve your warehouse?
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