Justin Smith of Lee & Associates invites Bart Gadeyne of Optioryx onto the Industrial Sites Podcast for an “industrial insights” conversation focused on a practical warehouse challenge: how to improve operations without replacing the Warehouse Management System (WMS).
Together, they discuss what it really means to add an AI-driven optimization layer on top of a WMS, and where that approach can deliver fast, measurable impact for warehouses and 3PLs.
"warehouse management systems can do a lot of things, but the real optimization power is lacking."
Discussion Overview
Keep the WMS, add optimization on top
Bart’s core message is that most warehouses don’t need a rip-and-replace. They need better decisions in order batching, routing, packing, and slotting.
"we add that intelligence layer on top of warehouse management system. It's purely API connectable."
The biggest cost drivers
1) Picking productivity starts with reducing walking
In warehouses, travel time dominates picking labor. Optimizing pick routes and pick sequence is one of the fastest paths to measurable productivity improvements.
Bart shares the outcome range they typically see:
"reduce walk distance by 20 to 55%."
And what that often means in operational terms:
"Picking productivity increase of around 20%."
Picking optimization ROI calculator: calculate the business case of picking optimization in your operations
2) “Shipping air” is expensive
Packing is full of small choices that impact cost. Manual packing tends to oversize cartons because the penalty for choosing too small is immediate (repacking time), while the cost penalty of choosing too large shows up later.
"In 30% of the time, this person is going to take a bigger box than necessary."
"if you're doubting between two boxes and you go for the smaller one and it doesn't fit in, you lose time by restacking in the other one. Next time for sure, you will take the bigger box."
This is where 3D cartonization can bring quick wins by providing operators with calculated packing instructions, considering the available box types, handling rules and carrier rates.
3) Dimensional weight changes what “optimal” means
The episode emphasizes that packing isn’t only about fit, it’s about carrier pricing rules. With dimensional weight, the cheapest shipping choice can be non-intuitive.
"the boxes or pallets you ship are not only charged based on the weight of a shipment but also based on the dimensions."
Bart gives a clear example of cost-optimized packing versus “best effort” packing:
“The operator packed everything into the smallest possible box, but when you optimize for shipping cost instead of carton size, splitting the shipment into two parcels was the cheaper option - €60 less.”
Data is the enabler: better capture, better decisions
A recurring point is that optimization depends on having reliable item and shipment data, especially dimensions. Bart contrasts static dimensioning equipment with a mobile approach designed to be used on the floor.
"Static small dimensioners are $20,000. A big one hanging at the ceiling for pallet dimensioning cost around 50k or more."
"we built the first mobile dimensioning app for SKUs, but also for pallets, and it's being used at hundreds of cross docks at the moment"
And he makes the adoption point directly:
"We see a lot of static dimensioners that are just gathering dust because people need to go to that fixed location."
With mobile dimensioning technology you can now capture accurate measurements of SKUs, parcels, pallets and irregular items. Integrate it with your WMS, TMS, ERP system to directly feed your operations with accurate data.
From daily operations to strategic design: digital twins and simulation
Beyond day-to-day execution, the episode frames optimization as a way to make smarter layout and slotting decisions, based on the actual order profile, not assumptions.
"All these optimizations we link link it to a warehouse digital twin."
The goal is to test scenarios before committing to changes:
"simulate it because the these simulations will answer a lot of questions in a data driven way and you will yeah make decision based on data, not just on gut feeling."
What slows adoption
Even when the business case is clear, implementation can stall for human and organizational reasons.
Teams that have been doing the same work the same way for years often need structured training and buy-in before new workflows stick.
In 3PL environments, incentives can also be misaligned:
In a cost-plus model, reducing the 3PL’s operating cost can also reduce their profit, which means there may be little motivation to optimize unless the contract rewards efficiency.
"If that's a cost plus setup, that means if we lower the cost of the 3PL, they will earn less. So then there is absolutely no no incentive to to optimize"
And deeper WMS-linked improvements often depend on IT bandwidth:
Anything that touches the WMS usually competes for IT attention, so even approved projects can take anywhere from a month to six months to get prioritized and implemented.
The big picture
Most warehouses and 3PLs don’t need to wait for a major automation program or a WMS replacement to make meaningful improvements. The faster path is usually improving the decisions that already happen every day, how work is routed, sequenced, packed, and staffed, using better data and optimization that sits on top of existing systems.
The winning formula is rarely “technology replaces people.” It’s technology guiding people with clearer instructions, fewer manual judgments, and more consistent execution, especially in environments with turnover, temp labor, or frequent client onboarding (3PLs).
"it's always a combination of of people technology"