Pack correctly every time
Wrong box sizes drive up dimensional weight costs. Our ML models learn from your shipping data to recommend the optimal box for every order.
The Problem
Box sizing is guesswork
Operators grab whatever box is nearby. Some overpack. Others underpack. Dimensional weight charges add up. Product knowledge stays in senior packers' heads.
Two recent audits made the pattern concrete: on a 2,492-SKU catalog only 25.1% of active SKUs had full dimensions, with the missing-dim SKUs concentrating on the highest-AOV bundles, exactly where DIM-weight surprises hurt most. On a different stack, 43% of shipping spend went through multi-item orders where cartonization could cut costs but a static rule engine could not.
DIM weight overcharges
Oversized boxes get charged for air. Carriers bill by dimensional weight, not actual weight. Money leaves the door with every oversized shipment.
Damage from underpacking
Boxes that are too small force items to shift. Products arrive damaged. Returns and replacements eat into margin.
Knowledge in heads
Senior packers know which box fits which product. New hires guess. When veterans are out, packing quality drops.
Bundle SKUs miss dims
On a recent 2,492-SKU catalog, only 25.1% had full dimensions; the missing-dim SKUs concentrated on multi-pack bundles, exactly where DIM-weight surprises hit hardest. The rate-shop quote was a default; the invoice was the real price.
Multi-item is the long tail
On a different audit, 43% of shipping spend went through multi-item orders. Single-SKU dim coverage doesn't fix multi-item picks; only a packing engine that knows what fits together does.
The Solution
ML-powered packing
Our machine learning models analyze your product dimensions, order patterns, and shipping history to recommend optimal box sizes. The system gets smarter with every shipment.
Machine Learning Box Sizing
- ML-powered box size recommendations
- Continuous learning from shipping outcomes
- Multi-item cartonization algorithms
- Fragile and special handling flags
- Void fill and dunnage guidance
- DIM weight savings tracking
Smart recommendations
ML models predict the optimal box size for each order. Recommendations factor in dimensions, weights, and learned packing patterns.
Learns from data
Models train on your shipping history. The system discovers patterns in what actually fits, not just theoretical dimensions.
Dunnage guidance
Recommend fill material and cushioning based on product fragility. Protect items without overpacking.
Multi-item cartonization
ML picks the right carton for complex multi-SKU orders. The model learns which combinations pack well together.
Continuous improvement
Models retrain on new data automatically. Recommendations get more accurate as your catalog and shipping patterns evolve.
Cost tracking
Track actual vs. recommended box usage. See where DIM weight savings are being captured or missed.
See how this works for your operation
Schedule a callThe Outcome
Lower shipping costs
ML box selection cuts dimensional weight charges. The system learns what works, so anyone packs correctly.
No more hunting for the right box for each order
Reduced DIM weight charges and shipping costs
Products arrive protected with right-sized packaging
Ready to cut DIM weight charges?
Our ML models will learn your shipping patterns and deliver packing recommendations that reduce costs.
Schedule a call