Cartonization refers to a step somewhere in an order lifecycle where the correct box configuration is determined for optimal shipping. It's a piece of the supply chain puzzle that's becoming more and more critical as ecommerce explodes and parcel carriers struggle with capacity. Typically three things are required to implement cartonization:
Adding any kind of automated cartonization to your fulfillment process can have dramatic effects on your bottom line, but you may find that not all solutions are created equal.
The most basic form of packing control relies on explicit rules for what item can go in which box. For example, "All cell phone batteries must go in Box 2, except for X condition." This approach may manifest as a rules engine that outputs instructions or at it's most crude, a literal post-it note on the warehouse wall. Rule of Thumb Cartonization is the least scalable and maintainable approach.
Modern packing considerations, as well as widely available technology, have rendered this approach outdated.
Now we're getting mathematical. Sort of. This is the type of cartonization baked into most WMS systems. Here's how it works: Software takes the total cubic volume of all items in a shipment, and then selects the smallest box that has a higher packable cubic volume. Unfortunately, while this approach is elegant and intuitive, it can require frequent intervention. For example, a 4 foot shovel may have a lower total volume than a box, but that doesn't mean it will actually fit.
It's also not ideal for identifying when it might be cheaper to split the shipment into two smaller boxes.
The only way to consistently automate packing decisions is to leverage a system that understands how items fit into cartons in the real world. That means a simulation of collisions and weight considerations, at least in some form. This method requires the least amount of intervention and can handle new SKUs and boxes without reconfiguration. And that 4 foot shovel won't be poking holes in any boxes.
Full simulation is a cornerstone of how Paccurate works. But it's not the whole story...
Although implementing 3d cartonization in your fulfillment process can yield more savings than more simplistic methods, we can go much further. The academic approach to "the bin packing problem," as found in MIT thesis papers, is geared toward packing the most items into a single box. This goal is useful, but not quite right for production shipping, where external factors affect what makes a packing solution cost-optimal. Material, labor, negotiated carrier rates and fees all have an impact on packing optimization.
Learn about how Paccurate accounts for these costs, or go down the rabbit hole of cost-optimal cartonization in our white paper.
If legacy cartonization software was a tricycle, newer cost-aware cartonization is the Harley Davidson of packing optimization. It uses full 3D cube logic like the best in class of the previous generation, but adds extensive business rules, accommodates complex item requirements, and most importantly optimizes for cost directly. Instead of just trying to find the fewest/smallest boxes, it evaluates carrier rate tables in real time and makes a packing decision based on what the carriers are actually incentivizing. It seems like a distinction without a difference. Surprisingly, the difference is massive. It’s often the difference between an ecommerce shipment being profitable, and not.
It’s often challenging to disrupt an old paradigm, especially one like box selection that’s been handled primarily with tribal knowledge in the warehouse. But in the case of modern cartonization, the results are hard to ignore. Compared to traditional containerization features, modern cartonization engines consider:
Most importantly, it has to actually work for all your inventory and shipping scenarios. That means support for fragility, compressibility, stacking, nesting, rolling, gift boxes, branded boxes... the list goes on. It has to do all this in real-time, and be an easy drop-in upgrade for whatever system you might have already. The result of this upgrade is consistent and dramatic savings on transportation costs, often by finding counter-intuitive ways of packing an order that a human can’t find on their own.