Optimize Pick Routes and Travel Paths

Every unnecessary meter in the warehouse costs time, labor, and money. In order picking, travel paths often account for 40 to 60 percent of total pick time – significantly more than the actual grabbing and scanning. Those who systematically optimize pick routes and travel paths increase throughput without additional staff, shorten cut-off times, and create buffer capacity for peak seasons.

This guide explains how to measure travel paths, strategically assign storage locations, control routes in the WMS, and avoid common mistakes – from small in-house warehouses to professional fulfillment centers.

What Is a Pick Route?

A pick route is the planned sequence of storage locations that a picker visits in optimal order for an order or pick wave. The goal is the shortest sensible distance, taking into account:

• physical warehouse structure (aisles, levels, zones)
• chosen pick strategy (single-order, batch, zone)
• item placement (slotting)
• order composition and priority

Travel paths and pick routes are closely linked: poor slotting creates long paths, even when the WMS mathematically optimizes the route. Conversely, perfect placement helps little if the route is guided arbitrarily or alphabetically instead of logically.

Process Flow: Pick Route in Fulfillment

Why Travel Paths Are the Biggest Lever

Time and Cost Impact

For an average e-commerce order with 3 to 5 line items, 50 to 200 meters of travel path accumulate per pick – depending on warehouse size and item distribution. With 200 orders per day and an hourly rate of 18 euros, inefficient paths alone generate thousands of euros per month in avoidable labor costs.

Throughput and Cut-off

Shorter paths mean more orders per hour. This directly affects same-day and next-day shipping: those who want to ship by 2 PM need reserve capacity in picking – and that primarily comes from optimized travel paths, not from rushing.

Error Rate and Fatigue

Long, chaotic routes increase interruptions, orientation errors, and fatigue – an indirect driver of pick errors. A clear, predictable route reduces cognitive load and improves concentration during scan-at-pick.

Time Shares in Order Picking

Fundamentals: Warehouse Layout and Slotting

ABC Analysis as a Starting Point

ABC analysis in the warehouse is the basis for sensible slotting: A items with high turnover frequency belong close to the picking zone and at ergonomic heights (gold zone between knee and shoulder). C items with rare movements can be placed farther away and higher or lower.

Slotting rules in practice:

1. 001. Fast movers in Zone A – maximum 10 to 15 meters from the packing station
2. 002. Place items frequently ordered together on adjacent locations (correlation slotting)
3. 003. Heavy and bulky items at the bottom and close to shipping, not at goods receipt
4. 004. Seasonal items temporarily re-slot – don't plan only once a year
5. 005. Returns and B-grade stock separate, so pick routes don't lead through restricted zones

Aisle Structure and One-Way Principle

One-way aisles in rack rows prevent opposing traffic and enable predictable serpentine routes. In narrow aisles with pallet jacks this is especially critical: reversing and turning cost more time than an additional meter straight ahead.

Warehouse Zones for Short Paths

Route Strategies in Detail

Serpentine Routing (S-Routing)

The serpentine route guides the picker aisle by aisle through the warehouse: one aisle from north to south, the next from south to north – without unnecessary backtracking. It is suitable for:

• single-order picking in manageable warehouses
• fixed aisle geometry without many cross connections
• manual pick lists when dynamic routing is not available

Return-to-Start vs. Nearest-Neighbor

Two classic algorithms in the WMS context:

Batch and Wave Picking: Routes Across Multiple Orders

With batch picking and wave picking, one travel path is planned for many orders. The efficiency gain is enormous – but only if:

• the route is sorted by pick locations, not by order number
• a clear sorting logic after picking exists (cart, bins, totes)
• the WMS consolidates duplicates (same location, multiple orders)

A batch with 20 orders and 80 line items can generate 70 percent less travel path with good route planning than 20 individual runs – with poor planning, the error rate increases.

Travel Path by Strategy (100 Orders/Day)

Technical Support: WMS and Labeling

WMS Routing

A warehouse management system calculates the pick route from storage location coordinates, aisle topology, and order data. Key factors are:

• Current master data: every item relocation must be updated in the system
• Weighting: express orders can receive shorter route priority
• Blocking logic: exclude blocked locations and quarantine from the route
• Feedback loop: evaluate actual travel times per route and adjust slotting

Without a WMS, serpentine sorting on the pick list is possible manually – sorting Excel or shop exports by aisle number is a first step, but does not scale beyond 50 orders per day.

Visual Orientation in the Warehouse

Clear aisle signage, colored zone markings, and consistent storage location logic (e.g. aisle-rack-level-bin) reduce search time. Floor markings for one-way aisles and pick direction complement the system route with physical guidance.

KPIs for Travel Paths and Pick Routes

Optimization Success After 3 Months

Step by Step: Optimize Pick Routes

Phase 1 – Analysis (Week 1)

1. 001. Time study with 10 representative orders: document travel time vs. grab time
2. 002. Heatmap of most visited storage locations from WMS logs or manual counting
3. 003. Update ABC classification and compare with actual locations
4. 004. Identify bottlenecks: narrow aisles, waiting zones, intersection points

Phase 2 – Slotting (Week 2–3)

1. 001. Relocate top 20 SKUs to near zone
2. 002. Correlation analysis: which items are ordered together in 30 % of orders?
3. 003. Plan seasonal reshuffling (e.g. before Christmas season)
4. 004. Review heavy items and hazardous goods positions

Phase 3 – Routing (Week 4)

1. 001. Activate serpentine or WMS routing and conduct training
2. 002. For batch picking: test sorting station and bin logic
3. 003. Pilot with one aisle or zone, then roll out

Phase 4 – Control (ongoing)

1. 001. Discuss KPIs weekly in team standup
2. 002. Re-evaluate slotting after assortment changes
3. 003. Peak simulation before Black Friday: test routes and staffing needs

4-Phase Optimization (Cycle)

Avoid Common Mistakes

Common pitfalls:

• Pick list sorted by order number instead of storage location
• ABC analysis created, but locations never adjusted
• One-way aisles signed, but staff cut corners
• Batch picking introduced without sorting control – error rate explodes
• New SKUs stored "where there is space" – slotting logic undermined
• Only relying on WMS, without checking physical aisle logic

Checklist: Pick Routes and Travel Paths

Before optimization:

• Travel path time share measured (goal: reliable baseline figure)
• ABC classes current and compared with storage locations
• Pick strategy documented and consciously chosen
• WMS coordinates of all active locations checked
• Aisle signage and storage location labels complete

After optimization:

• Travel path per order reduced by at least 15 %
• Staff trained on new route
• KPI dashboard or weekly report established
• Correlation slotting implemented for top 10 item pairs
• Peak test with increased order volume conducted

Quick-win route optimization:

• Check aisle direction
• Sort pick list by location
• Relocate A items
• Mark one-way aisles
• Batch only with sorting
• Activate WMS route
• Document time study
• Schedule monthly review

Practical Example: Medium-Sized E-Commerce Warehouse

An online retailer with 2,500 SKUs and 150 orders daily initially measured an average of 138 meters travel path per order. After ABC reshuffling (80 A items to near zone), serpentine sorting on the pick list, and switching to wave picking with WMS routing, the value dropped to 94 meters – with the same error rate.

The packing area was defined as a fixed start and end point. Items that appeared together in 25 percent of orders (e.g. filter + replacement nozzle) were placed on adjacent locations. Pick lines per hour increased from 38 to 51 – without additional staff.

Conclusion

Optimizing pick routes and travel paths is one of the fastest levers in fulfillment: lower costs, higher throughput, less stress in the team. Success depends on three pillars – proper slotting based on ABC and correlation, suitable pick strategy with clean route logic, and continuous measurement through clear KPIs. Those who optimize once and then forget lose the gains with the next assortment push.

Start with an honest time study, move top sellers to the near zone, and sort the next pick list by aisle – not by order number. Those are the first meters on the shortest path.

Related Topics

• Order Picking and Picking
• Pick Strategies Overview
• ABC Analysis in the Warehouse
• Avoiding Pick Errors
• Batch Picking and Wave Picking

Last updated: July 6, 2026