Warehouse Pickup Optimization: The Operator's Guide (2026)

TL;DR

• In most order-picking operations, travel is commonly cited as roughly half of all pick labor. Half the cost of picking is not picking. It is walking.
• Pick optimization is not one problem. It is five layers, and they only pay off in order: slotting, batching, routing, zoning, wave release. Most warehouses buy the last layer first.
• Routing is the weakest lever you have. Slotting is the strongest, because the fastest trip is the one nobody had to take.
• None of it works until your location data is accurate. An optimized route over wrong locations is a faster way to reach an empty shelf.
• Your ERP records where an item is supposed to be. It was never built to decide where it should go, based on what your orders actually pull together. That is a layer you add on top, not a reason to replace anything.

What is pick optimization?

Pick optimization is the practice of reducing the labor cost of fulfilling orders by changing four things: where inventory is stored, which orders get collected together, the path a picker walks, and how the floor and workload are divided. The single biggest cost it targets is travel time.

Here is the number that reframes the whole exercise. Across the warehouse research literature, travel distance is commonly cited as around 50 percent of the labor cost of order picking (De Koster, Le-Duc and Roodbergen, 2007, is the canonical survey). Read that again. In a typical picking operation, half of what you pay a picker to do is walk. Not scan. Not grab. Walk.

So the instinct is to make the walk smarter. Better routes. Faster pickers. A handheld that beeps. That instinct is not wrong, it is just aimed at the last layer of the problem instead of the first.

The five layers, and why order matters

Order picking decomposes into five layers. They compound, and they only pay off in sequence:

1. Slotting. Where each SKU physically lives. The highest-leverage layer. Everything downstream inherits it.
2. Batching. Which orders a picker collects in one trip.
3. Routing. The path walked for a given batch.
4. Zoning. How the floor and the workload split across pickers.
5. Wave release. When work is released under a live stream of incoming orders.

The reason the order matters: optimizing a lower layer cannot fix a bad decision in a higher one. You can compute a perfect route through a badly slotted warehouse and you still walk too far, because the items were stored in the wrong places to begin with. Fix slotting and even a mediocre route is short.

Why routing is the weakest lever

Routing is where almost everyone starts, because it is the most visible and the easiest to buy. It is also the layer with the least headroom.

The common routing heuristics (S-shape or serpentine, return, largest gap, combined) are well understood and already close to their ceiling. S-shape routing, the one most warehouse systems default to, tends to land within roughly 20 to 30 percent of the theoretical optimal path. Largest gap usually beats it. Getting from a decent heuristic to a near-optimal one saves you the last slice of a single layer. It does not touch the other four.

Routing optimizes the walk. It cannot delete the walk. That is a slotting decision.

Slotting: where the walking gets deleted

Slotting is the decision of where each item physically goes. It is the strongest lever for one reason: the fastest pick trip is the one that never had to happen. If the items that get ordered together are stored together, a three line order stops being a tour of the building.

Two ideas do most of the work here.

Velocity slotting (the Cube-per-Order Index). COI is an item's storage volume divided by how often it is ordered. Place low-COI items (small and frequently ordered) closest to the pick face and the dock. Under classic single-command assumptions this is provably optimal, and it is the baseline every warehouse management system already tries to approximate. It is necessary and it is not enough.

Affinity slotting (the part your ERP does not do). Velocity slotting treats each SKU alone. Affinity slotting asks a harder question: which items get ordered together, and are they stored near each other? Co-locate the pairs and triples that show up on the same orders and you collapse multi-line picks into short walks. The catch is that this is a genuinely hard optimization problem (formally a quadratic assignment problem, which is NP-hard), not a sort in a spreadsheet. It needs the actual order history and real optimization, which is exactly why it rarely gets done well, and why it is the most valuable thing to get right.

The gate: location accuracy comes before all of it

Here is the part that rarely gets said out loud. None of the five layers matter until your location data is accurate.

An optimized route over wrong locations is not an improvement. It is a faster way to arrive at an empty shelf. If the system believes a SKU is in A-12 and it is actually in D-04, every algorithm downstream is optimizing fiction. The picker still hunts. The route was perfect and useless.

This is the single most common reason optimization projects underdeliver, and it is invisible on a slide. In operations with tens of thousands of SKUs, many without barcodes, location drift is constant: put-away in the wrong bin, partial moves, mis-scans, cycle counts that never catch up. Before you optimize anything, you need a reliable way to know where inventory truly is and to write that truth back into the system of record. Computer vision and OCR that read a SKU and its location, then update the ERP, are one practical way to close that loop.

The correct sequence is not negotiable: location accuracy first, then slotting, then batching, then routing. In that order, or not at all.

Why your ERP does not do this for you

Dynamics 365, SAP, whatever you run, the ERP knows where an item is supposed to be. It records the location, it directs the pick, it books the transaction. It was never built to tell you where that item should have been in the first place, based on what your orders actually pull together.

That is not a flaw in the ERP. It was simply never its job. Microsoft did not build order-driven, continuous re-slotting into Supply Chain Management, and SAP did not either, because the ERP is a system of record, not a system of decision. Warehouse work in Dynamics lives in the Supply Chain Management app (the Finance and Operations family, with old AX lineage), and it will faithfully execute the slotting you give it. It will not derive a better one from your history.

That gap is the layer worth building: AI systems that sit on top of the ERP you already run, learn from your own order history, and decide placement instead of just recording it. No rip and replace. The ERP stays the system of record. The optimization layer feeds it better decisions.

The ROI, in plain arithmetic

Because there is a real number under this, here is the benchmark math. Treat every figure below as an industry benchmark and an illustration, not a promised result.

Take a mid-sized operation with 60 pickers at a fully loaded 45,000 dollars a year. That is 2.7 million dollars a year in pick labor. If travel is around half of pick time, roughly 1.35 million of that is people walking. Published slotting studies put the travel reduction from re-slotting in the range of 10 to 30 percent. Use 15 percent, deliberately conservative, and you are looking at about 200,000 dollars a year that stops being spent on walking.

The KPI to watch is the one your floor is already measured on: lines picked per labor hour (LPLH). Slotting moves LPLH by shortening the average distance between picks. That is the language to use with a distribution VP, not "AI."

One honest floor: below roughly 25 to 30 pickers, the savings stop clearing the cost and friction of integration. Small operations should fix location accuracy and basic velocity slotting and stop there.

The traps that quietly kill slotting projects

Slotting is a 25-year-old category. Blue Yonder, Manhattan, Körber and Optricity have sold it for decades. Anyone claiming to have invented it is telling an experienced operator they do not know the field. The value is not novelty. It is doing the unglamorous parts that make projects actually execute:

• Move cost. A plan that says "relocate 4,000 SKUs" never gets executed, because the labor to move them swamps the quarter. Real slotting runs against a move budget, for example no more than 200 moves a week, so the warehouse converges toward the optimum without stopping to do it.
• The replenishment tradeoff. Tighter forward-pick slotting means smaller slots, which means more replenishment trips. Optimize picking in isolation and you can push the net negative. Forward-pick slot sizing has to be co-optimized, not assumed.
• Congestion. Cluster popular items together and you also cluster your pickers together. The affinity that shortens walks can create aisle jams that eat the savings. Managing that tension is the actual hard part, and it is where most spreadsheet slotting quietly fails.

Who this pays for (and who it does not)

Order-affinity slotting only pays when orders are multi-line. This is the qualifier that decides everything.

It pays for B2B wholesale distribution and retail store-replenishment DCs: auto parts, industrial and MRO, electrical and plumbing and HVAC, pharma and medical, food service, apparel replenishment. These operations have orders that pull many lines at once, which is where affinity value lives. Good fit looks like more than 2,000 order lines a day, an average above 2.5 lines per order, 2,000 to 50,000 active SKUs, 30 or more pickers, and piece or case picking rather than full-pallet.

It does not pay much for single-line direct-to-consumer e-commerce. If most orders are one item, there is no "ordered together" to exploit, and you get velocity slotting value only. Qualify on the order profile first and the ERP second.

How to start without buying anything

You can see your own number before you commit to a project. The order data needed already lives in your ERP: order ID, SKU, bin location, and timestamp, for the last 90 days. In Dynamics that is one OData or data-management export.

Run that history through a slotting analysis and it returns two things: your projected travel reduction, and the top 200 SKU moves that would produce most of it. It is your data, your baseline, and your number, which is why it is worth far more than any vendor benchmark. If the projected reduction does not clear your integration cost, you have learned that cheaply and you walk away.

Where Cognilium fits

We build custom AI systems that sit on top of the ERP you already run. For warehouse operations that means the decision layer the ERP was never meant to provide: learning from your own order history to improve where inventory lives, and closing the location-accuracy loop that everything else depends on. No rip and replace. We have delivered 50+ projects with a 96 percent client satisfaction rate since 2019, for clients across the US, UAE and Pakistan.

If your pickers are walking a marathon to fill a three line order, the problem is probably not your pickers. It is where the inventory sits. Talk to an engineer and we will look at what your own order history says.