Warehouse Operations Analytics

When the warehouse feels unpredictable, analytics is the cure for guessing

Warehouse operations can feel like weather. Some days everything flows. Other days, a small problem turns into a storm. The difference is rarely luck. It is usually visibility. If you can see where time is being lost and why, you can fix the constraint before it wrecks the day. That is what warehouse operations analytics is for. It turns the warehouse from a place where you react into a system you can manage.

Brands tend to ask for analytics when growth makes the old habits fail. Volume goes up, channels multiply, and the cost of a miss gets bigger. A missed cutoff can trigger customer churn. A B2B compliance miss can trigger chargebacks or cancellations. If you cannot measure what is happening, you cannot improve it, and you cannot explain it credibly to the people who need answers.

What warehouse operations analytics should actually cover

Analytics is not the same as reporting. Reporting tells you what happened. Analytics tells you why it happened and what will happen next if nothing changes. In a warehouse, analytics should span receiving, inventory accuracy, picking, packing, staging, carrier handoff, and for B2B, compliance milestones. If you only analyze outbound, you will miss inbound causes. If you ignore carrier handoff, you will blame carriers for problems that started on the dock.

Joel Malmquist, VP of Customer Experience at G10 Fulfillment, described the scope that should shape operational measurement. "An SLA is a Service Level Agreements for Receiving, Outbound, and B2B." That scope is a blueprint for analytics. If you want to protect SLAs, your analytics has to track the stages that create SLA outcomes, not only the final result.

Why definitions are the first analytics decision

Analytics fails when people are analyzing different things with the same words. Shipped is the classic trap. A warehouse can mark shipped when the order is completed. Customers treat shipped as carrier acceptance and tracking movement. Retailers treat shipped as compliant, routed, and documented on time. If you do not define your milestones clearly, your analytics will produce clean charts that generate messy arguments.

Malmquist explained why he avoids the word shipped as a catch-all. "The reason I don't say ship is because sometimes it will be marked as completed, but the carrier doesn't actually pick it up right away, but the tracking goes back to Shopify." Warehouse operations analytics should therefore treat warehouse completion and carrier acceptance as separate events, and it should track the gap between them. That gap is a major driver of customer perception and a frequent source of SLA confusion.

Receiving analytics: the upstream signal that predicts downstream stress

Receiving problems rarely announce themselves loudly. They show up later as backorders, missed cutoffs, and frantic searches for product that is physically in the building but not available in the system. That is why receiving analytics is high leverage. If you can see receiving backlog and aging in real time, you can prevent tomorrow's outbound misses.

Malmquist described receiving SLAs in clock-based terms that fit perfectly into analytics. "For receiving, the SLA is covers the time from the moment that we get a container on the dock with inventory in it, and how much time we have to count that in, and stow it away into the locations that we're going to pick from." Receiving analytics should therefore include age on dock, time to count, time to stow, exception rates, and the correlation between receiving delays and outbound holds. When you track that correlation, you stop blaming outbound teams for problems that started upstream.

Inventory analytics: the difference between what you have and what you can ship

Inventory analytics is where operational truth meets business promises. If inventory is inaccurate, the storefront lies, and the warehouse wastes time. If inventory is accurate, picking is faster, backorders are rarer, and customer communication is calmer. Warehouse operations analytics should therefore include cycle count variance, location accuracy, and inventory availability timing, because those are the metrics that connect inbound work to outbound performance.

Inventory analytics is also what keeps promotions from turning into chaos. If your available-to-promise signal is wrong, a successful campaign can produce oversells, cancellations, and angry customers. This is where analytics is better than simple reporting. It helps you predict the risk of a promotion by showing how inventory accuracy and receiving throughput are behaving before the spike hits.

Picking analytics: where travel time, slotting, and replenishment show up in numbers

Picking is often the biggest labor cost in fulfillment. Picking analytics should therefore go beyond lines per hour. It should analyze travel time, pick path design, zone congestion, replenishment-driven interruptions, and exception frequency. When lines per hour drops, the cause might be layout, not effort. Analytics helps you prove which it is.

Picking analytics also benefits from segmentation. Single-line orders behave differently than multi-line orders. Small items behave differently than bulky items. If you blend everything into one average, your analytics will hide the truth. When you segment, you can see which order profiles are creating the load and which process changes will help most.

Packing analytics: where accuracy and speed collide

Packing is where the warehouse proves correctness. Packing analytics should therefore track throughput and error rates together. If throughput rises while error rates rise, you did not improve. You shifted cost downstream into returns, reships, and customer support time.

Joel Malmquist described a level of accuracy that is hard to achieve without disciplined verification and scanning. "We have over 99.9% ship accuracy of these orders, which when you look at it on a unit level, such as unit shift versus unit errors, I almost couldn't believe it when I came here, how well we're doing on B2B shipping." Analytics should treat that as a measurement target and a process clue. It suggests scan discipline and verification are built into the flow, which is exactly what analytics should test and validate when performance changes.

Carrier handoff analytics: the gap that makes customers think nothing happened

Carrier handoff analytics is the bridge between warehouse performance and customer perception. A warehouse can complete orders on time and still create a bad customer experience if packages sit staged and miss pickup. If you do not measure carrier acceptance timing, you cannot separate warehouse delay from carrier delay, and you cannot improve the right part of the system.

Malmquist explained why this gap is so common. "The reason I don't say ship is because sometimes it will be marked as completed, but the carrier doesn't actually pick it up right away, but the tracking goes back to Shopify." Warehouse operations analytics should therefore track completion to acceptance gap by carrier, by pickup window, and by dock workflow. When the gap grows, you can investigate staging discipline, appointment adherence, and carrier coordination before customer tickets spike.

B2B analytics: compliance is measurable, and retailers measure it for you

B2B operations add compliance tasks that D2C does not have. Routing guides, label placement, pallet build rules, plus EDI and ASN timelines are all measurable. If you do not measure them internally, retailers will measure them for you, then send the bill. That is why warehouse operations analytics for B2B must include compliance completion rates and timing, not just shipped quantities.

Bryan Wright, CTO and COO of G10 Fulfillment, explained why B2B needs a different foundation. "Our WMS system was written from day one around B2B, which is very different." He described the requirements that analytics should treat as milestones. "They have routing guides that make you specific labels on and put them in a specific place on the box, and you have to send EDI, ASN, electronic information in a timely fashion." If those events are tracked, you can identify repeat failure patterns and fix them before they become chargebacks.

Why strict retailer deadlines make predictive analytics worth the effort

Retailers enforce deadlines without exceptions, which makes prediction valuable. If you can forecast that a PO will miss its routing window, you can add labor, adjust priorities, or communicate early. If you cannot, you find out the problem when the order is canceled or penalized.

Holly Woods, Director of Operations at G10 Fulfillment, described the reality in a way that should shape B2B analytics. "Target has a deadline for delivery and that's it, no exceptions. They'll just cancel the order." She also described how compressed timelines can get when inbound arrives late. "When it came in, it had to be completed, received, shipped, labeled, ready for routing to a carrier by that next morning." Analytics that highlights these compressed cases, and the stage where time is being lost, helps you protect revenue rather than just explaining why it was lost.

Why scan-based events are the raw material for analytics that works

Warehouse operations analytics lives or dies on data quality. Scan events are the most reliable raw material because they record physical reality. Manual updates create timing drift and gaps, which lead to bad conclusions. If the timestamps are not trustworthy, the analytics will be decorative rather than useful.

Wright described the foundation of trustworthy warehouse data. "A good WMS tracks inventory through the warehouse at every point that you touch it." He explained how granular that tracking can be. "At any point in time, I know that Bobby has this product on fork 10 right now, and if I needed to go find that product, I just got to go find Bobby on fork 10." When analytics is built from scan events, you can trace performance changes to specific workflow events, which is how you improve the system instead of guessing at causes.

Why real-time visibility turns analytics into daily prevention

Analytics has the most value when it is visible during the day. If you only see metrics after the shift ends, you cannot prevent misses, you can only explain them. Real-time views allow teams to act while there is still time to protect cutoffs and retailer windows.

Maureen Milligan, Director of Operations and Projects at G10 Fulfillment, described what customer-facing portals provide. "What these real-time portals provide our customers is 100% visibility." She added what that experience looks like. "They can actually watch those progressions going on." Connor Perkins, Director of Fulfillment at G10 Fulfillment, described what customers can see when visibility is built correctly. "Our clients get best-in-class visibility and transparency. They can see their daily orders, they can see KPIs, and they can see historical transactions." Analytics becomes more powerful when customers and teams can see the same progressions and make decisions from the same facts.

Where G10 fits if you want analytics that leads to better days

Warehouse operations analytics should help you protect SLAs, improve throughput, and maintain accuracy without relying on guesswork. G10 focuses on scan-based execution and customer-facing visibility built on ChannelPoint WMS, so analytics is grounded in real warehouse events. The goal is clear: understand where time and risk live, fix constraints quickly, and keep fulfillment predictable as you scale.

If you want to see what warehouse operations analytics looks like in practice, ask for a walkthrough of a live day in the portal, including one exception case. You should be able to start from an SLA metric, drill into scan events, and see exactly where the bottleneck is forming, so you can grow with fewer surprises and more control.