Summary
Pickers walk the full length of an aisle for one item, pass the same locations twice in one tour, or cross the warehouse to reach the next pick. That walking comes from two decisions: which orders go on the same cart, trolley or pallet jack, and in which order the picker visits the locations.
Most WMSs make the second decision with a fixed walking order, set once when the locations were created. Pick path optimization plans a new route for every pick list instead. Sometimes the fixed order is good enough, and this article shows when it is, when it is not, and how to check it on your own pick lists.
How does pick path optimization work?
Pick path optimization plans the shortest walking route for each pick list, instead of the fixed order a WMS typically uses.
It is one of two decisions picking optimization software makes for every tour: which orders go on the same cart, trolley or pallet jack, and which route the picker walks.
Order clustering decides which aisles a tour visits. Routing decides the order of the stops and the way back.
Order clustering decides which aisles a picker visits at all. Orders that need the same aisles go into the same tour, filled up to what the cart or trolley can carry. Well-filled tours mean fewer trips, and other pickers stay out of those aisles.
Pick path optimization decides the order of the stops. It plans the shortest route the picker can actually walk, back to the drop-off point, with your one-way aisles and blocked zones. When an item sits in more than one location, it picks the one that fits the route.
The two depend on each other: the best route depends on which orders are on the cart, and the best grouping depends on the routes. Optioryx customers see 30 to 55% less walking when order clustering and pick path optimization run together.
The walking rules most warehouses use today
Without software, pickers follow a fixed walking rule. Our guide to picking methods and routing rules covers them in more detail.
| Walking rule | How the picker walks |
|---|---|
| S-shape (snake) | Walks every aisle that has a pick, from one end to the other |
| Return | Walks into each aisle and back out the same end |
| Largest gap | Walks in from both ends and turns at the biggest empty stretch |
| Optimized route | Gets a new shortest route for each pick list |
View this table on desktop to compare the walking rules.
A fixed rule gives every pick list the same walking pattern. An optimized route changes with the list.
How does your WMS set the pick path today?
In most WMSs, every location has a sequence number, and each pick list is sorted by it. The numbers usually run as a snake: up one aisle, down the next.
| Stop | Location | Seq. |
|---|---|---|
| 1 | A1-04 | 0104 |
| 2 | A2-09 | 0209 |
| 3 | A3-02 | 0302 |
| 4 | A4-11 | 0411 |
| 5 | A5-06 | 0506 |
| 6 | A6-01 | 0601 |
Every aisle walked end to end, even aisle 3 for one pick at the front.
Someone set those numbers once, when the locations were created. After that, they are kept up by hand: new locations get a number squeezed in between, and locations without a number drop to the end of the list.
The number says nothing about distance. The next location on the list can be next to the cart or at the far end of the next aisle, and the WMS treats both the same. If the list is grouped by order first, a multi-order cart can pass the same aisles several times; our article on cluster picking shows what that costs.
Some WMSs are starting to plan routes on real distances. Microsoft, for example, added a spatial location feature to Dynamics 365 that routes picking work on location coordinates. Check whether your WMS has something similar, and whether it is switched on.
When is the WMS sequence close enough?
A fixed snake comes close to the best route when there is little to choose. In these cases a better route saves little walking, and your time is better spent on slotting or order release.
In these cases the fixed order is often close to the best route. Look at slotting and order release first.
Research backs this up for simple layouts. A 2024 study in OR Spectrum compared walking rules with the best possible route in one block of ten aisles. The best fixed rule walked less than 5% further in all but one of the storage setups tested, and nearly 12% further in that one.
That only holds if the snake was laid out well for your floor. A sequence drawn up years ago, before re-slots and new racking, may no longer be close.
Where does a fixed walking order waste distance?
A fixed order wastes walking when the floor or the orders give it choices it cannot take. Seen any of these on your floor?
Each row is a reason to measure, not proof of a big saving: what it costs depends on how often it shows up in your orders. Multi-order carts have issues of their own, covered in our guide to multi-order picking.
Batch matters more than the route
A route can only shorten the walk between stops that are already on the list. The batch decides which stops those are, so grouping orders that share aisles shortens every route before any routing runs.
In our experience, four things decide how far pickers walk. Slotting has the most impact, then order clustering, then wave release, then the route. Optimizing the route alone, with slotting and batching left as they are, pays the least.
Where items are stored counts as much. In the same 2024 study, the best route combined with storage by pick frequency gave tours nearly 60% shorter than a simple return route with random storage.
A MedTech distribution center we work with shows the order of effect. Grouping orders that share aisles on the same trolley came first, then route optimization and double-stacked trolleys.
Is pick path optimization worth it for your warehouse?
Your own pick lists answer this better than any benchmark, ours included. Replay them both ways on a map of your floor:
- Export two to four weeks of picked lists, with the order in which each line was picked. Which fields you need depends on your WMS; we go through it on a short call.
- Draw the layout: aisles, cross-aisles, one-way rules, blocked zones, start and drop-off points.
- Replay every list in the current WMS order and measure the meters walked.
- Replay the same lists with an optimized route.
- Replay once more with the orders regrouped first, to see how much of the gain sits in the grouping.
- Turn meters into hours with your walking speed, and hours into money with your labor cost.
A constructed example, not a measurement: 2 meters saved per pick line on 1.5 million lines a year is 3,000 km, or about 833 hours at 1 meter per second with a cart.
Where we have run it with customers, the results look like this. At a MedTech distribution center, picking productivity went up 27%, with 54% less walking, as the MedTech customer story shows. At Bleckmann, a fashion and lifestyle 3PL, Bleckmann's release reports "an average of 15 to 20%" less walking, "compared to non-optimised routes."
Some sites will see little. Goods-to-person systems have no carts to route, and where walking is a small share of the shift, the bottleneck sits elsewhere. Our article on improving picking performance covers those cases.
How Optioryx Pulse runs pick path optimization on top of your WMS
Optioryx Pulse sends an optimized route for each pick list back to the WMS you already run. Order clustering and the route come out of one calculation, and picking and slotting are optimized together, so routes run across a layout that has already been improved.
Most projects start with the replay test in the Pulse platform, on uploaded order history and a layout, before any integration. After go-live, pickers follow each route on a visual 2D guide, on the devices they already use or on a separate screen.
What changes for the picker
The optimization runs in the background. Pickers get their instructions the same way as before, on the same devices; only the order of the stops changes.
The first tours can feel counter-intuitive, because the route no longer follows the familiar snake. Showing the route on screen helps pickers trust it.
Where packing is part of the tour, such as trolley capacity, pick-to-box or pick-to-pallet, the instruction can be a 2D or 3D view of where each item goes. It can also be as simple as "item 1 goes in box 3".
Built around your floor and rules
The layout model includes one-way aisles, cross-aisles and blocked zones. Cart capacity, weight limits and the site's own rules go into the same calculation, such as heavy items first and fragile items last. A 3PL can run different rules per client.
At Bleckmann, the route planning works alongside the existing WMS and generates "a customised route that's specific to each pick list." Counted in picks per hour, Pulse has reached up to a 30% increase in pick rate across customer deployments.
Comparing tools? Our overview of picking optimization software compares six of them.
Questions?
Sometimes not. In a single-block warehouse with many picks per aisle and one start point, a well-laid-out WMS sequence comes close to the shortest route. It pays more with several blocks, sparse picks, separate start and drop-off points and multi-order carts. Replaying your own pick lists settles it.
A pick sequence is a fixed number on each location, set once, and every pick list is sorted by it. Pick path optimization calculates a new route for each list on a map of the layout, using walking distances, cross-aisles and one-way rules.
It depends on the layout. In a single-block warehouse, a 2024 study in OR Spectrum found the composite rule within 5% of the optimal tour for every storage policy tested but one. With several blocks or few picks per aisle, a route calculated per list adapts where a fixed rule cannot.
Yes, when both are part of the calculation. One-way aisles and blocked zones go into the layout map, so routes only use paths a cart can take. Heavy items can be sequenced first and fragile items last, within the cart's capacity and weight limit.
Compare the meters walked per pick line on the same orders, as-is and optimized, split by zone and order type. This works on past pick lists before anything changes on the floor. After go-live, track picks per hour against the same period before the change.