Pick Path Optimization: Cut 30% Picker Travel Without Moving a Shelf

It’s 9:45 a.m. on a Monday when you watch your fastest picker walk past aisle 7, loop back from aisle 12, return to aisle 7, then head all the way to the back of the warehouse for one item.

The order had six line items. Five of them were clustered in a 2,000 sq ft zone. That sixth item — a single unit tucked in the wrong bin — added six extra minutes of walking.

Multiply that by 80 orders and a three-person team, and you just burned 24 extra picker-hours this week. Not because anyone was slow. Because the pick path wasn’t optimized.

That’s the real cost of pick path drift. And fixing it doesn’t require automation, a new floor plan, or a capital project. It requires three things: the right routing logic, smart slotting, and a system that builds the path for the picker instead of leaving it to chance.

Why Picker Travel Is the Biggest Lever in Your Warehouse

Before you can optimize anything, you need to understand where the time actually goes.

Academic research on warehouse operations consistently finds that travel and walking accounts for 50–55% of total order picking time. Not the actual picking. Not scanning. Not confirmation. Just walking from one location to the next.

Order picking itself — the entire activity — represents roughly 55% of total warehouse operating expenses, according to multiple peer-reviewed studies published in logistics and operations research journals.

Put those two together:

If picking = 55% of total warehouse operating cost
And travel = 50–55% of picking time

Then travel = roughly 28–30% of your total warehouse operating cost

That means nearly a third of what your warehouse costs to run goes toward people walking. Not working — walking.

A 20% reduction in travel distance translates to roughly a 6–8% drop in total operating cost, with no new headcount and no capital spend. That’s why pick path optimization is the single highest-ROI lever available to most small and mid-size warehouses.

Key insight: A typical picker in a conventional warehouse walks 15–20 km per eight-hour shift, according to academic studies on warehouse operations. Most of that distance is determined by how the pick list is sequenced — not how fast the picker moves.

The 4 Classic Routing Strategies (and When Each One Wins)

Routing strategies have been studied extensively in operations research. There are four classic approaches, each with different tradeoffs depending on order density, warehouse shape, and pick list size.

S-Shape Routing

The picker traverses every aisle that contains at least one pick, end to end, in a serpentine pattern — left side down aisle 1, right side back up aisle 2, left side down aisle 3, and so on.

Best for: Dense orders where most aisles have multiple picks. The overhead of entering and fully crossing an aisle is worth it when there are 5+ picks inside it.

Problem: On sparse orders — say, 3 picks spread across 8 aisles — S-shape forces you to walk entire aisles for a single pick. You’re paying the full travel cost of each aisle whether it has 1 pick or 10.

Return Routing

The picker enters each aisle from the same end (the main aisle), travels to the deepest pick in that aisle, and comes back out the same way. Then moves to the next aisle.

Best for: Long, narrow aisles where picks cluster toward one end. Better than S-shape when most picks are in the front half of each aisle.

Problem: On a full order spread across the whole aisle, return routing forces a lot of backtracking. You go in, come back out, walk past the next aisle, go in, come back out.

Largest-Gap Routing

The picker enters each aisle and exits at whichever end is closer to the largest gap in that aisle — the biggest empty stretch between two adjacent picks. By always exiting at the optimal end, the picker avoids retracing the gap.

Academic research comparing routing strategies has found that the largest-gap method consistently outperforms pure S-shape routing on random storage configurations.

Best for: Mixed orders where pick density varies by aisle. It adapts to the actual spread of picks in real time rather than applying a fixed rule.

Problem: More complex to implement manually. Practically, this is where software picks up the work — a WMS calculates the optimal exit point for each aisle automatically.

Optimal (Algorithmic) Routing

Modern WMS systems use graph-based algorithms that model the warehouse as a network of nodes and edges, then calculate the true shortest path across all picks in the order simultaneously — not aisle by aisle, but all at once.

Comparison of routing methods (illustrative, based on academic benchmarks):

Method              Avg. travel vs. optimal
------------------  -----------------------
S-shape             +40% excess distance
Return              +50% excess distance
Largest-gap         +15% excess distance
Algorithmic         Baseline (optimal)

Worth knowing: You don’t need to implement a graph algorithm manually. What you need is a system that sorts pick lists by location code in the right order — aisle by aisle, bay by bay — and lets the picker follow the screen instead of making navigation decisions on the fly.

The 3-Part Framework for Pick Path Optimization

Routing logic is only one third of the equation. Real pick path optimization has three components, and they work together.

Part 1: Location Codes That Sort Cleanly

Every pick path strategy depends on sortable location codes. If your bins are labeled A-3, A-10, A-2, B-1, those don’t sort in walking order — they sort alphabetically and numerically in ways that send pickers backward.

The fix is zero-padded, consistent naming:

Good (sorts in walking order):
A-01-01  →  A-01-02  →  A-02-01  →  A-02-02  →  B-01-01

Bad (sorts wrong):
A-1-1  →  A-10-1  →  A-2-1  →  B-1-1

When your location codes sort correctly, your WMS can sequence the pick list automatically — no custom routing algorithm required. Klovio’s pick path system is built on this principle: pick list sequencing relies on clean location naming, and fixing the naming scheme is usually the fastest path to better routing.

Part 2: Slotting — Putting the Right Items in the Right Place

Slotting is the practice of positioning SKUs based on how often they’re picked.

Your top 20% of SKUs probably appear in 60–80% of your orders. If those SKUs are scattered across the warehouse, every order triggers a cross-floor walk. If they’re clustered near packing, most orders are resolved in a small zone.

Slotting impact example (illustrative):

Before slotting:
  Top 20 SKUs scattered across 6 aisles
  Average order touches 4.2 aisles
  Average pick walk: 18 minutes

After slotting (top SKUs moved to aisles 1–2):
  Average order touches 2.1 aisles
  Average pick walk: 11 minutes
  Time saved: 7 minutes/order × 80 orders = 9.3 hours/day

The logic behind slotting:

• A-items (top movers): nearest to packing, waist-height bins, no climbing
• B-items (medium movers): mid-warehouse, still in comfortable reach
• C-items (slow movers): far from packing, high or low shelves

Run an ABC inventory analysis on 60 days of order data before you reslot. You want to know which SKUs are actually A-items in picking frequency, not just in revenue.

Part 3: Zone Picking as a Travel Limiter

Once your warehouse is large enough — roughly 10,000+ sq ft, 3+ pickers on shift — the best structural solution is zone picking.

Zone picking doesn’t optimize a route within the whole warehouse. It eliminates whole-warehouse routes entirely. Each picker owns a section; the order moves between zones rather than a picker moving between everything.

Zone picking travel reduction (illustrative):

Warehouse: 15,000 sq ft, 3 zones of 5,000 sq ft each
Without zones: picker covers up to 15,000 sq ft per order
With zones: picker covers up to 5,000 sq ft per order
Maximum travel per pick: reduced by ~67%

You can read more about how zone, batch, and wave picking methods compare in Wave Picking vs Batch Picking vs Zone Picking — but for the pick path conversation specifically, zone picking is the structural ceiling on how far a picker can walk.

The Pick Path Audit: How to Measure Before You Optimize

You can’t improve what you don’t measure. Here’s the 4-step audit that tells you where pick path waste is hiding.

Step 1: Time a picker on three full shifts. Don’t coach them. Just observe. How much of the shift is walking vs. active picking? If walking exceeds 45 minutes per hour, you have a path problem.

Step 2: Plot last week’s orders on your floor plan. Which aisles got hit most? Are your top-10 SKUs clustered near packing or spread out? That visual immediately shows slotting opportunities.

Step 3: Pull your accuracy report. High pick accuracy + low throughput usually means the route is inefficient, not the picker. Low accuracy + low throughput often means the path is forcing guesswork.

Step 4: Track picks per hour, not orders per hour. Orders vary in complexity. Picks per hour is a cleaner productivity metric that isolates route efficiency from order mix.

Pick rate benchmarks by warehouse type (illustrative, based on industry research):

Warehouse type          Picks/hour (manual)
--------------------    -------------------
Small (under 5k sq ft)  60–100
Mid-size (5–20k sq ft)  80–130 (with optimized paths)
Distribution center     150–250 (with zone + batch)

Watch out: If your picks-per-hour number sits below 60 in a well-staffed warehouse, the first thing to check is whether your pick lists are sequenced in walking order. An unsequenced list can cut throughput by 30–40% on its own.

The Pick Path Mistakes That Cost the Most

These are the four most common errors — the ones that show up repeatedly in warehouses that have good people but weak systems.

Mistake #1: Pick lists that aren’t sequenced. If your team is printing paper pick lists or looking at unordered digital lists, they’re deciding the route themselves — and human navigation is never as efficient as system-optimized sequencing.

Mistake #2: Slow-moving items in prime locations. You added a new SKU last quarter, it was popular at first, now it sells once a week — but it’s still in the bin right next to packing. Meanwhile, a SKU that appears in 40% of orders is in aisle 9. Reslotting quarterly based on real velocity data fixes this.

Mistake #3: No zone structure despite having 3+ pickers on shift. Three pickers in an unzoned warehouse will create traffic — they’ll hit the same aisles, wait for each other, and slow each other down. Zones eliminate the congestion problem structurally.

Mistake #4: Trusting shortcuts. Experienced pickers often develop personal routes based on memory. Those routes are almost never optimal — they’re optimized for what the picker remembers being fast, not for what’s actually fastest given today’s pick list. The fix is following the system’s pick path and measuring the result.

How to Implement Pick Path Optimization in 3 Phases

You don’t need to do this all at once. Three phases, in order:

Phase 1: Fix Your Location Codes (Week 1)

Audit your current naming scheme. Can it sort into walking order? If not, rename bins using a zero-padded, segment-consistent scheme. This alone — if your WMS sequences by location — can improve pick efficiency by 20–30% with no physical changes.

Phase 2: Reslot Your Top 20 SKUs (Weeks 2–3)

Pull 60 days of pick data. Identify your top 20 SKUs by pick frequency. Move them to the bins nearest packing. You don’t need to reslot everything — just the A-items. The impact is immediate.

Phase 3: Add Zone Structure If You Have 3+ Pickers (Month 2)

Once routing and slotting are working, add zone assignments for your picking team. This limits each picker’s travel universe and eliminates cross-floor congestion.

See how Klovio handles pick path sequencing and zone picking for the specific configuration steps once your location codes are clean.

What to Expect After Optimization

Done in sequence, this three-phase approach typically produces:

• Phase 1 (location code fix): 15–25% reduction in average pick list travel time
• Phase 2 (top-SKU reslotting): additional 10–15% reduction in travel per order
• Phase 3 (zone structure): eliminates cross-floor traffic; scales headcount without congestion

None of this requires capital investment, automation, or shutting down the warehouse to rearrange it. It’s a software and process change — and if you’re already using a WMS that sequences pick lists, Phase 1 might be as simple as updating your location naming scheme.

If you’re on Klovio’s mobile app, your pickers are already getting sequenced pick lists on their devices. The path is built automatically from your location codes. You control the quality of the path by controlling how you name and slot your bins.

Once you’ve optimized picking, the next bottleneck is usually packing throughput. Same principle: sequence the work, reduce unnecessary motion, and measure picks (or packs) per hour. One bottleneck at a time.

Ready to see what your pick path actually looks like? Start with how Klovio builds pick sequences, then review your location naming to confirm your codes sort in walking order.