How to Reduce Warehouse Travel: Increasing Order Picking Efficiency with Warehouse Optimization Software

Picking drives ~50–60% of warehouse labor cost, and much of that spend is lost to travel between pick faces. While automation can help, its ROI is mixed, especially for 3PLs and e-commerce ops with seasonal swings, so many sites first turn to software optimization. Standard WMS picking logic rules don’t adapt to changing order mixes; an optimization layer does, continuously improving routing, clustering and slotting. The foundation is a digital replica of your layout (aisles, rules, congestion points) to test “what-ifs” safely. Net result: less walking, higher lines-per-hour, lower cost per order, and a practical path to continuous improvement, with or without later automation.

Order picking is one of the biggest cost drivers in warehouses. Multiple industry and academic sources estimate that picking consumes ~50–60% of warehouse labor/operating cost, which is why even small gains in pick efficiency drop straight to your cost-per-order.

Zoom in further and you’ll find that travel time (the walking or driving between pick faces) typically dominates a picker’s day. Travel can account for about half of total labor time in conventional picking, and some facilities report associates walking miles per shift.

Less travel means faster tours, higher lines-per-hour, and lower cost per order, and because work releases can be sequenced more smoothly, you also improve on-time shipment performance.

The logistics industry is under constant pressure to do more with less. For many, automation seems like the obvious answer, robot fleets, goods-to-person systems, and automated storage promise faster fulfillment and reduced labor reliance. Even for many large, high-volume operations, these investments can be tricky.

But automation is not always a quick win. Even in “robots-as-a-service” models, costs include unit fees, minimum commitments, and integration effort. For 3PLs or e-commerce operations with seasonal swings, the question becomes: do you size the system for peak and carry the cost year-round, or risk under-capacity when demand spikes?

The ROI isn’t always straightforward.

That’s where warehouse optimization software (WOS) enters the picture. Unlike automation, it doesn’t require new infrastructure or equipment. Warehouse optimization software improves routes, clustering, and slotting within your existing setup. It’s faster to implement and easier to scale, making it especially practical for operations that need flexibility.

The right approach isn’t either/or.

Many warehouses find that starting with optimization gives them quick productivity gains and a leaner baseline. Later, if they decide to add automation, those systems perform better because they’re working on already-optimized processes.

A WMS is built for inventory control and task execution, receiving, confirmations, and managing waves. But when it comes to picking, most WMS rely on simple, static business rules: fixed pick paths (e.g., aisle-by-aisle or a single S-pattern everywhere) with no clustering logic. These rules don’t adapt to changing order mixes, so much of the potential for higher productivity is left untapped.

Optimization software goes further. Instead of relying on fixed patterns, it uses algorithms to calculate the best route and cluster for each picker in real time, factoring in constraints like cut-off times, order priorities, and aisle congestion.

This makes continuous improvement possible, because the system adjusts tour by tour rather than following the same rule every time.

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1) Smarter Pick Path Optimization

Classic routing methods, S-shape, return, midpoint, largest-gap, aisle-by-aisle, have been studied for decades. Each works best in different situations, but no single method is ideal everywhere. If you always use an S-shape, for example, you over-travel on sparse orders; if you always use return, you miss efficiency in dense areas.

Modern optimization engines evaluate the actual pick set and facility layout each time to choose the shortest possible path. To do this effectively, you need an up-to-date map of your warehouse with aisles, cross-aisles, and one-way rules built in. (Practically, you’ll need an up-to-date map of your warehouse, more on that below.)

2) Smarter Clustering

Clustering reduces travel by letting pickers handle more lines per tour.

Good clustering respects capacity limits (cart slots, weight, volume), order due times, and location overlap. The challenge is that finding the “best” cluster quickly becomes complex as order pools grow. Optimization software can evaluate millions of possible combinations and build more efficient batches than static WMS rules, which is why many operations see significant gains when moving to algorithm-driven clustering.

3) Dynamic Slotting

Basic slotting rules, like putting fast movers at the front, are helpful but limited. Smarter slotting also considers:

• Items that are often ordered together (co-occurrence).
• Seasonality and promotions so locations adapt to demand shifts.
• Ergonomics and replenishment to keep heavy or bulky items in optimal positions.

Even well-slotted warehouses see order mix change daily. That’s why leading DCs now use dynamic slot maintenance alongside routing and clustering optimization.

While full slotting overhauls are rarely practical, the key is to apply slotting in smaller, operational steps. Instead of reshuffling the entire warehouse, teams can make small, targeted adjustments, for example, switching a few SKUs between racks on a daily or weekly basis based on demand data.

All of the above, pick path optimization, clustering, slotting, run on a warehouse map that contains:

• Layout: aisles, racks, bays, cross-aisles, obstacles.
• Aisle rules: one-way vs. bidirectional, speed/slowdown zones.
• Congestion points: narrow passes, hot zones, dock approaches.

Simple and complex warehouse layouts typically can be replicated within a few minutes, using the warehouse layout builder. The system can then calculate the shortest possible pick paths based on that virtual layout.

Once you have this replica, you can run what-if tests without touching the physical warehouse. For example: “What if we add a cross-aisle?” or “What if we change cart capacity?” This ability to test before you invest is the foundation of continuous improvement: adjust one variable, measure the results, and keep refining.

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Labor shortages, rising shipping costs, and the time it takes to train new staff all make warehouse productivity a pressing concern. Research and industry benchmarks consistently show that picking is the largest share of warehouse labor cost, and that travel accounts for most of the picking time.

The levers that matter most are clear: routing, clustering, and slotting.

An optimization layer on top of your WMS can help by:

• Identifying the most efficient routing, rather than relying on basic WMS picking rules.
• Building clusters that account for cart capacity, due times, and real location overlap.
• Continuously re-optimizing as new orders and constraints come in.

The foundation is a digital replica of your warehouse. With it, you can test routing, clustering, and slotting scenarios in software before making physical changes.

This approach allows teams to measure potential gains, reduce unnecessary travel, and support continuous improvement, without large-scale reconfiguration or capital-intensive automation.