The Five Data Points That Most Logistics Teams Miss

On the surface, everything runs smoothly. Orders ship on time, reports show solid performance and the metrics all hit their marks — but behind those numbers, something feels off.

Pickers are skipping routes. Inventory numbers don’t line up. Scanners stop working, or no one bothers to use them. Productivity is dropping, and no one can point to a clear reason. In many cases, the problem isn’t what teams are measuring; it’s what they’re missing.

Most operations focus on a standard set of performance metrics, but tucked between the usual numbers are smaller signals that often go unnoticed. These overlooked data points can uncover training gaps, tech fatigue and outdated workflows that slow everything down. To help your operations be as efficient as possible, here are five issues to watch out for and how to spot them before they start affecting the bottom line. It is the same reason and problem that makes AI often fail to deliver on its promise to improve supply chain efficiency. The bad data problem Nucleus has spoken about here.

Inventory accuracy discrepancies. When the system says 20 units are on the shelf but only 17 are there, most teams move on without much thought, — but that difference might be a sign of something bigger. Errors like these often trace back to expired goods, theft or bad data entry. Over time, they lead to missed orders and wasted hours.

To stay on top of the problem, warehouses need regular audits that are more comprehensive than just counting boxes. Start matching inventory records against shipping reports, review return logs and damaged goods, looking for patterns. If you can fix the root cause early, then it can keep these mistakes from cutting into revenue.

Pick path deviations. Most WMS platforms suggest an optimized pick route. So what happens when pickers skip the system’s plan? Some leaders assume it’s a training problem or laziness, but it’s often something else.

Consider it from a worker’s perspective. Pickers may be avoiding awkward slotting, slow traffic zones or just making the route more efficient on their own. If enough people are doing it, the process might be the problem. Collect the data, map the real paths then ask the floor team what’s going on. They are the experts in the day to day operations, and can point out where processes may be failing them.

Partial picks and incomplete orders. When orders leave the warehouse missing items, the issue rarely starts at the packing station. Incomplete orders often point to deeper problems like slow replenishment, poor slotting or inventory stored in hard to reach areas. These moments signal that the system isn’t keeping pace with demand.

Watch when and where those misses happen. Do they spike during certain shifts? Are the same products frequently short? Tracking by time, SKU and location can uncover storage or timing issues that disrupt flow. Once those are addressed, order accuracy tends to improve without adding headcount or reworking your entire process.

Scan gaps and inconsistent RF usage. Scans are meant to capture every movement in the warehouse. What if employees are skipping scans or scanners keep failing? With these blind spots popping up, it becomes harder to trace mistakes, measure output or keep accurate records.

Scan gaps can mean the equipment is old, the process is clunky or the team doesn’t see the value. Either way, it’s a sign the system isn’t being used the way it was designed. Review scan data across shifts and roles. Then check for hotspots where gaps happen the most. The fix might be as simple as new hardware, or as involved as retraining and process changes.

Tech debt in workflow logic. Every growing warehouse builds up some tech debt. Old routing rules, customer preferences or automation settings can hang around for years. Even when the team upgrades equipment or software, those old workflows often stay in place.

One operation installed new sorting systems but left its original routing logic untouched. Orders kept following the long way around. The upgrade didn’t improve performance because the process itself was outdated. These workflow settings need regular review. Pull reports that show how often customer routing, batch rules or shipping preferences are used. If no one has touched them in a year, it’s worth a second look.

You don’t need a full system overhaul to start catching these signals. The data is already there. The challenge is knowing what to look for and asking the right questions.

Start by picking one area that’s been inconsistent. Look at the related data and then talk to the people closest to the process. They usually know where the friction is. From there, compare what the numbers say with what’s actually happening on the floor.

The trick is to link data with behavior. A drop in pick rate might be a problem, or it might just mean the team is adjusting to a new layout. Scan gaps could mean broken hardware, or they could point to process fatigue. Context matters. Numbers alone don’t tell you why something is happening.

When teams pay attention to these lesser known indicators, they can catch problems earlier, improve the process and scale more effectively.

Source: Supply Chain Brain

Image Source: iStock credit: Artemis Diana