Reducing Shipping Costs with Automated Cartonization

When warehouse staff choose boxes by eye, they often pick one that's "good enough" rather than one that fits perfectly. This results in "shipping air"—oversized boxes that inflate costs due to dimensional weight pricing.

Cartonization solves this by determining the mathematically optimal box for a given order. Switching from manual selection to an API-driven approach like BinSolver can directly improve profit margins.

Dimensional Weight

Carriers charge based on Dimensional Weight (Dim Weight). This is a calculation derived from the package's volume rather than just its physical weight.

Scenario A involves paying for 11 lbs of empty space. Across thousands of shipments, this inefficiency becomes a significant expense.

Complex Packing Logic

Simple 2D volume summation is insufficient for real-world packing. A robust algorithm must account for:

• Orientation: Constraints for upright-only items (e.g., liquids).
• Nesting & Padding: Item geometry and necessary protective material.
• Splitting: Determining the most cost-effective split when an order exceeds a single box's capacity.

Optimization via SDK

The BinSolver SDK allows you to define box inventory and costs. Setting the objective to minCost instructs the algorithm to find the cheapest combination of boxes.

from binsolver import PackRequest, Item, Bin

# 1. Define items
items = [
    Item(w=10, h=10, d=10, quantity=5)
]

# 2. Define bins with explicit costs
bins = [
    Bin(id="small", w=12, h=12, d=12, cost=0.50),
    Bin(id="large", w=25, h=12, d=12, cost=1.20)
]

# 3. Optimize for cost
#    The solver will choose the combination of bins
#    that results in the lowest total 'cost'.
request = PackRequest(
    items=items,
    bins=bins,
    objective="minCost"
)

Inventory Insights

Automating cartonization provides data on box usage frequency, enabling you to optimize your packaging inventory by stocking only the sizes you actually need.