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How Altitude Sports Rebuilt Packaging For Their Apparel Catalog
How a retailer increased mailer usage by 18%
Gowtham Ramachandran
May 19, 2026
If you ship apparel, you know this story. Your packages are light. Your carriers don't care. They charge on volume, not weight, and every cubic inch of empty space is a line item on your next invoice.
Altitude Sports lives this at scale. The Montreal-based outdoor retailer ships over 1.2 million orders a year. Jackets, base layers, ski gear, shoes. Most of it light, much of it soft, most of it getting dim-weighted on the way out the door.
They already had cartonization. It came built into their WMS. It wasn't enough.
This is the story of what they did about it, and the part of the project that turned out to matter more than the algorithm. To break it down, I spoke with Kosta Cherezov and Dan Norton at Paccurate, who led the build with Altitude's team. Kosta ran the day-to-day implementation. Dan worked on the broader integration and the structural decisions around rule ownership.
What’s Inside
Where Built-in Cartonization Fell Short
Most WMS platforms ship with a cartonization engine. It reads the order, checks your available boxes, picks one. For a lot of operations, that's fine.
For Altitude, it missed in two specific places.
The Item Problem
The WMS modeled every item as a rigid object with fixed dimensions. A jacket is not a ski boot. One compresses, one doesn't. But the system treated both the same, which meant compressible apparel got packed into boxes sized for items that don't compress. Empty space in every shipment, multiplied across a million-plus orders a year.
The Mailer Problem
Altitude was using mailers, but not as much as they should have been. Mailers are cheaper than boxes when the item allows it, and they absorb less dim-weight damage because they conform to what's inside. The catch is that most WMS platforms model a mailer the same way they model a box: a rigid container with fixed dimensions. A mailer expands and contracts based on contents. If your system can't represent that, it under-recommends mailers and defaults to boxes.
As Kosta Cherezov at Paccurate put it: "WMS treats the mailer as a rigid container with a fixed set of dimensions. But we know that actually a mailer can get bigger or smaller based on what you put inside of it."
Two gaps, same root cause. The WMS was modeling packaging the way software finds it easy to model, not the way it actually behaves on the floor.
The Fix: Two Rules That Changed The Math
Altitude brought in Paccurate, a cartonization platform their WMS partner Deposco recommended. Two rules did most of the work.
Alternate Dimensions
Every soft item got tagged with a compression factor:
C10 items shrink 10% when packed
C30 items shrink 30%
Categories got flagged based on what the Altitude team knew about each one
The cartonization engine then ran the math against the compressed dimensions, not the rack dimensions. Same item, smaller footprint, smaller box, smaller dim-weight charge.
Dynamic Mailer Modeling
Instead of treating each mailer as a fixed-dimension container, the system modeled each mailer size as a flexible envelope that expands within known limits. A mailer that was previously rejected because the WMS thought items wouldn't fit became viable. Mailer recommendations went up. Box recommendations for items that didn't need a box went down.
Neither rule is conceptually hard. The work was in identifying which items got which compression flags, which mailers had which expansion ranges, and how the rules interacted on orders with mixed contents. Once those were defined, the algorithm did its job.
Making Packing Rules Accessible
Altitude started working with Paccurate in early in the year. The plan at that point was straightforward. Compression rules, mailer logic, item flags would all be configured inside Deposco, Altitude's WMS. But this would have required a custom integration on the Deposco side, with future rule changes routed through the same process. Paccurate came up with a better approach.
They'd been building a system internally that let customers manage rules directly on Paccurate's side, in a UI separate from the WMS.
Deposco still handled the order data and received Paccurate's packing decision. But the compression rules, mailer definitions, and item flags now lived on Paccurate's side, in a self-serve interface Altitude's logistics team could edit directly. Adding a new mailer size, changing a compression factor, flagging an item category - all of it became a few clicks instead of a ticket.
Dan Norton at Paccurate described the dynamic the new approach addressed:
"There's tension between operations and IT when it comes to cartonization. IT owns the tool, in the sense that they built the integration. But operators are the ones that use it. They're the ones that know what the packing rules need to be."
Moving the rules to ops meant the people closest to the floor could easily change them without routing every change through a separate team.
Results
Three numbers stood out after launch:
Mailer utilization: 60% → 78% (+18 pts)
Fill rate in the first month: +6%
Packer training time: -15%
A few things behind those numbers.
The mailer jump came from two places: The dynamic mailer modeling routed more orders into mailers that fit. And a separate Paccurate analysis flagged that Altitude was missing an XL mailer size. PacSimulate, Paccurate's simulation tool, ran historical orders against alternate packaging configurations and showed that a larger mailer would absorb a meaningful chunk of orders currently going into boxes. Altitude added the size. Mailer usage moved.
Training time dropped because the judgment calls disappeared: Packers used to make calls on the floor about which container to use. The new system removed most of those calls. Recommendation came in clear, packer executed.
Transportation savings showed up unevenly across the carrier mix: Some of Altitude's domestic Canadian lanes are flat-rate and don't penalize volume. The big wins came on international shipments and remote Canadian territories. Francis Lapointe, Altitude's Logistics Manager, put it this way: "Some of our carriers don't care about volume, with flat fees for local shipments. However, for international shipments and deliveries to remote Canadian territories, the savings are substantial."
Signals It's Time To Look
Here are five things to check in your own data. The first two tell you whether you have a problem. The last three tell you how urgent it is.
1. Your dim-weight exposure as a percentage of total shipping spend
Pull your last 90 days of carrier invoices. For each shipment, compare the billed weight to the actual weight. If billed weight is higher, the carrier dim-weighted that package, and you paid for volume you didn't ship.
Calculate the delta as a percentage of your total shipping spend.
Under 5%: dim weight is background noise
5% to 15%: a real line item and worth a closer look
Over 15%: one of your top three cost drivers and you're underinvesting in the fix
2. Override rate on the packing floor
Spend a shift watching your packers. Count how often they take the WMS's box recommendation versus picking a different box themselves.
Under 10%: the system is mostly right and you're tuning at the margins
10% to 20%: worth investigating which categories or order types are driving the overrides
Over 20%: your packers are doing the packing math the system should be doing, which is a labor cost hiding in plain sight and means your reported packing data doesn't match what's going out the door
3. Mailer-to-box ratio against your catalog mix
Pull your shipment data for the last quarter. What percentage went out in mailers versus boxes?
Then pull your item master and estimate what percentage of your orders could have gone in mailers (non-fragile, soft, within mailer dimension limits). The gap between those two numbers is your mailer opportunity.
Altitude's gap was 18 percentage points (60% actual, 78% achievable). If your gap is under 5 points, you're already doing well. If it's 10 or more, you have a meaningful amount of money sitting in your box-versus-mailer decisions.
4. Surcharge line item trajectory
Go back 24 months on carrier invoices. Chart your dim-weight, oversized, and length-plus-girth surcharges as a percentage of base rate.
Flat or declining: surcharges are growing in line with base rates
Climbing (which is the case for most shippers right now): surcharges are growing faster than base rates, which means the ROI math on packing optimization gets better every year
5. Planned automation capex
Check your next 18 months of automation roadmap. On-demand packaging , automated packout, goods-to-person systems.
Any of these is a trigger. The machine is only as good as the packing decision feeding it. Automate the packout process while your packing logic is still recommending wrong boxes 20% of the time, and you've automated a bad decision at higher speed.
Kosta's framing: "If you're trying to have a box-on-demand machine, how do you make sure that box machine is making the right box?"
How to weigh the signals
One signal alone isn't enough to move. Two or more, especially if override rate is one of them, usually is.
The order of operations:
Start with signal 1 (dim-weight exposure). It's the easiest to calculate and tells you the size of the prize.
Then check signal 2 (override rate). It tells you whether your current system is the bottleneck or whether it's something else.
Signals 3 through 5 sharpen the case for timing. They don't change whether you should act. They change how urgently.
If signals 1 and 2 both flag as problems, you're past the evaluation question. You're into the execution question. That's when it's time to run a simulation against your historical orders and see what the ceiling looks like.
What To Take From This
Four things stood out, each useful on its own.
1. Generic cartonization undersells your packaging
Most WMS cartonization engines make one core assumption: every item is rigid, every container is rigid, and the job is to solve a 3D bin-packing problem with fixed shapes. That assumption holds for warehouses shipping rigid goods in standardized boxes. It breaks the moment your catalog has compressible items, flexible containers, or any packaging behavior that changes based on contents.
This shows up as specific decisions the system gets consistently wrong. Apparel packed into oversized boxes. Mailers rejected in favor of boxes when the items would fit. Void fill compensating for decisions that shouldn't have needed compensating. The algorithm isn't broken. It's working with a model of your packaging that doesn't match how your packaging actually behaves.
The fix is giving the algorithm a more accurate picture of what it's working with. Which items compress and by how much. Which containers expand. Which combinations are feasible on the floor versus only on paper.
The broader point: any optimization system is only as good as its model of reality. When the output feels consistently off, the first question is whether the model matches the operation. This applies to slotting algorithms, labor forecasts, demand planning, and routing.
2. The bottleneck is rarely the algorithm
Go-live is the moment everyone focuses on. But the value of a cartonization system doesn't come from the configuration at launch. It comes from the hundreds of small rule adjustments that happen over the following two years, as you add new SKUs, change packaging lines, renegotiate carrier contracts, and watch the floor evolve.
If every one of those adjustments has to route through IT, the adjustments don't happen. Or they happen six months late. Or they happen once and then ops stops asking because the friction isn't worth it. The savings that compound over time never get captured, even though the system is technically capable of capturing them.
Altitude's project is a clean example. The fact that their logistics team could tune rules on an ongoing basis without filing tickets is what makes the savings compound.
The broader point applies to any system where the people who know the right answer are different from the people who can change the configuration. Pricing rules. Slotting logic. Safety stock parameters. Returns routing. If you're evaluating a tool, ask who will own rule changes on day 500, not day 1. If the answer is "IT, through a ticket," the tool will underperform regardless of how good its core capability is.
3. Run the simulation
Paccurate's pre-sale analysis told Altitude they were missing an XL mailer from their lineup. That finding came out of feeding historical order data into a simulation and asking what the optimal packaging mix would have been.
The findings are often actionable on their own timeline. Adding a missing mailer size is a small capex investment that can move your dim-weight exposure measurably. Identifying a box size that doesn't earn its place lets you consolidate your packaging lineup and reduce inventory carrying cost. These are wins you can capture in a quarter, separate from any longer-term software decision.
4. Get your data in order before anything else
Cartonization runs on dimensions and weights. If your item master is incomplete or unreliable, no amount of software will help you. The diagnostic is only as good as the data feeding it, the rules only as accurate as the items they reference.
Before evaluating any tool, run through your data:
Item master: do you have accurate dimensions and weights for every active SKU?
Catalog turnover: does your data capture process keep up with new SKU launches?
Packaging inventory: are your current box and mailer dimensions documented and current?
Carrier rate structure: do you know which lanes price on volume versus flat rates?
The same logic applies to most supply chain tech decisions. A WMS upgrade depends on process discipline. A TMS depends on lane volume and carrier mix. A forecasting tool depends on data hygiene. The tool can be excellent and still fail if the foundation isn't there.
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