Amy Dean, VP of Operations at SC Codeworks, discusses a holistic approach to warehouse performance 

Amy, can you start by sharing a bit about your background. What led you to specialize in warehouse optimization and helping logistics teams build scalable operations? 

I started my career in 3PL and had the opportunity to work across nearly every part of the business. That broad exposure gave me a deep appreciation for how tightly connected operations and IT really are, and how often they’re treated as separate worlds. I became passionate about bridging that gap and saw firsthand how continuous improvement and the right system practices could dramatically improve KPIs, win new business, and create happier, more empowered teams. 

Over the years, I’ve served in multiple leadership roles, including Vice President of IT, where I focused on implementing scalable systems, optimizing processes, and driving strategic growth. Now, as VP of Operations at SC Codeworks, I focus on aligning people, processes, and technology to build logistics solutions that actually work in the real world. I’m especially passionate about helping teams scale efficiently while delivering real (and unique) value to their customers. 

Many organizations still lean on traditional KPIs to measure warehouse performance. Why do you think these metrics fall short in today’s logistics environment? 

Traditional KPIs are a solid foundation and in many cases, they’re even tied to contractual obligations. But relying on them alone can lead to blind spots. You might be hitting your targets on paper, but that doesn’t always reflect the full picture of your operation. 

For example, if you’re meeting service-level goals but team morale is slipping, it could signal deeper issues like uneven workloads or ineffective processes. These don’t always show up on a dashboard, but they can lead to turnover or burnout that ultimately impacts performance and customer satisfaction. 

Focusing too narrowly on the same few metrics can cause you to miss hidden risks or opportunities for improvement. A more holistic approach to performance helps you stay proactive, not just reactive and puts you in a stronger position during customer reviews or contract negotiations, especially when you’re expected to bring fresh ideas and operational savings to the table. 

You’ve identified five often-overlooked data points. Can you walk us through the relevance of inventory accuracy discrepancies? 

Inventory is money and in logistics, it’s also trust. Your customers rely on the expectation that every unit entering your warehouse is accurately accounted for and ready to sell. That’s not just a goal; it’s the core responsibility of any warehouse operation. 

When discrepancies arise, they’re rarely just about numbers. They often point to deeper issues like inconsistent processes, a lack of accountability, data entry errors, or even something as simple as misunderstood pallet configurations. Left unaddressed, these issues can snowball into missed shipments, customer dissatisfaction, or in extreme cases, lost business. 

In some cases, discrepancies may also signal more serious problems, like shrinkage due to theft. That’s why it’s critical to treat inventory accuracy not just as a metric, but as an indicator of operational health. It’s one of the clearest reflections of how well your people, processes, and systems are working together. 

What are some real-world consequences of pick path deviations, and how can they signal deeper workflow issues? 

When pickers regularly skip certain locations, intentionally or not, it can push small problems further down the line, where they become much larger. For example, avoiding a damaged pallet or rack could lead to lot codes falling out of rotation, which is especially problematic in food-grade warehouses. If the damaged product sits too long, it can result in spoilage or even pest issues. Over time, this behavior skews inventory accuracy, which then impacts shipping performance and customer satisfaction. 

These deviations can stem from a range of root causes: damaged or unstable pallets, poor slotting, crowded aisles, or even training gaps that cause associates to favor certain areas over others. For instance, picking from bulk instead of rack locations because it feels easier or faster. 

Paying attention to these patterns gives you an opportunity to correct underlying problems before they turn into operational failures. It’s a good example of how seemingly small behavior on the floor can reveal much larger opportunities for process improvement. 

Partial picks and incomplete orders are often written off as minor mistakes. Why do you see them as more significant red flags? 

Sometimes, they do come down to simple training gaps or human error. But more often, they point to systemic issues like inbound shipments not arriving on time, delays in receiving processes, or a lack of proactive replenishment due to labor constraints. In other cases, they may indicate inventory problems, such as shortages, damaged goods, or items held up in QA. 

What makes these issues especially concerning is their ripple effect. Incomplete orders disrupt service levels, create extra work for your team, and, most importantly, erode trust with your customers. Over time, recurring issues can damage partnerships and make it harder to win or retain business. 

That’s why I see these events not just as isolated incidents, but as signals of larger inefficiencies or disconnects in the operation that need to be addressed proactively. 

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 ability to execute. Even small inefficiencies can become major bottlenecks as you grow. One of the best first steps is to walk the floor, observe where processes deviate from expectations, and make adjustments early. The tighter your operations are before scaling, the smoother your growth will be. 

Can you share a success story where addressing these issues led to a measurable improvement in warehouse efficiency or accuracy? 

We worked with a warehouse that was struggling with a growing number of incomplete orders. Leadership responded by throwing more labor at picking, thinking it was a productivity issue, but nothing improved. When we pulled scan data, we saw large gaps between scans, indicating that pickers were spending a lot of time searching for product or rerouting themselves mid-task. 

It was clear walking the floor that the warehouse was over capacity. Aisles were jammed, dock space was limited, and product was everywhere but the team was focused on shipping, assuming that pushing more product out would free up space. What they didn’t fully realize was that the real bottleneck was in receiving. There was product sitting in the building that hadn’t been received into the system, so it wasn’t available to pick even though it was physically there. 

The overcapacity created a domino effect: blocked aisles, unassigned locations, missed pallets at shipping, and a frustrated team unable to do their jobs efficiently. 

Once we uncovered the root cause, we redirected labor to receiving and focused on short picks to immediately improve fill rates. We also worked with the customer to implement pre-loads, which helped free up valuable dock space. With space under control, the team could begin consolidating locations and evaluating layout changes. The result: fewer incomplete orders, improved inventory visibility, and a much smoother flow throughout the building. 

What advice would you offer logistics leaders who suspect their operations are suffering from invisible inefficiencies but don’t know where to dig in? 

If you suspect invisible inefficiencies in your operation, start by stepping back from the day-to-day grind and take a fresh look at your data. Verify what the data is really telling you. Walk the floor, talk to your team, and confirm whether what’s happening in the system reflects what’s actually happening in the building. 

One of the biggest traps I see is leaders jumping to quick fixes or layering on workarounds. It might feel like progress, but it often just masks the real issues and creates more complexity in the long run. Instead, focus on small, visible wins that build trust with your team. When they start to see positive changes, they’re more likely to buy in as you tackle bigger improvements. 

Also ask: is the data you are capturing a true measure of performance, or just what’s easiest to report? Every operation has a different starting point based on where and how value is created. 

The key is to treat inefficiencies as symptoms and then use a mix of data, team insight, and floor observation to find the root cause.  

Amy Dean 

www.sccodeworks.com 

Amy Dean is an operations and IT expert with over a decade of experience in logistics. SC Codeworks is an all-inclusive software development company focused on the warehouse and transportation industries. Founded over 20 years ago, its seasoned professionals provide a state-of-the-art, scalable, and customizable Warehouse Management System (WMS).