________________________________________________________________________________________________________________________
You mention scan gaps as a diagnostic tool. How can teams detect these gaps, and what might they reveal about training or tech adoption? Scan gaps are one of the most underutilized diagnostic tools in a warehouse. When used intentionally, they can offer real insight into training effectiveness, process compliance, and technology adoption.
The first step is to start reporting on them. Most WMS platforms allow you to pull timestamp data, so use that to track how long it takes between scans during key workflows. Look for patterns: if scans are happening too close together, it might indicate that operators are rushing through the process or simply going through the motions without actually following procedures.
Defining and benchmarking your productivity standards is also key. When you know what“ normal” looks like, it’ s easier to spot outliers that point to deeper issues.
From there, it’ s about digging into the root cause. A scan gap might be the result of a broken process, a manual workaround that’ s become a habit, a need for refresher training, or even technical problems like faulty scanners or poor network connectivity.
What’ s powerful about scan data is that it gives you a factual starting point for conversations with your team and ultimately helps you build a more efficient, accountable, and tech-enabled operation.
The idea of tech debt is more common in software than logistics. How does it manifest in warehouse environments, and what can leaders do about it? Tech debt is often associated with software, but it shows up just as frequently in warehouse operations. The key difference is that in operations, it’ s easier to miss because the impact builds slowly over time.
One of the most common examples is routing or sequencing logic set up during go-live or customer onboarding. These rules dictate how work flows through the building, but they’ re often left untouched, even after major changes like new racking or reconfigured pick zones. When that happens, the system’ s instructions no longer match the physical layout, and efficiency quietly erodes.
Leaders can get ahead of this by building a stronger bridge between systems and operations. Make sure any layout or process change is communicated early and walk the floor with both IT and operations stakeholders. This helps to spot gaps between how the system thinks work is happening and what’ s actually going on. Tech debt can also creep in through outdated customer label logic, changing packaging requirements, or stale item master data. Recognizing tech debt early and treating it as an ongoing operational risk can make a significant difference in long-term scalability and efficiency.
For companies looking to scale, how important is it to integrate these overlooked data points into their performance monitoring? Where should they start? When you’ re preparing to scale, consistency is everything. You need processes that are repeatable, reliable, and as automated as possible. The more you grow, the less room there is for manual workarounds or individual exceptions. Overlooked data points can quietly reveal where things aren’ t working as well as they should. If left unaddressed, these issues grow with your business.
Scaling already requires significant focus on real estate, equipment, labor, and customer demands. Your systems and processes should be the steady foundation not an added source of complexity or risk.
Start by identifying the key operational metrics, ideally the top two-to-five, that directly impact your team’ s day-to-day work. Your team is your biggest asset in any scaling effort, so it’ s essential to track the data points that reflect their efficiency, consistency, and
12