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.

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.

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

Our ML models train on your historical shipping data to predict optimal box sizes. The system learns product combinations, seasonal patterns, and packing efficiencies that even experienced operators miss. Recommendations improve automatically over time.

  • ML-powered box size recommendations
  • Continuous learning from shipping outcomes
  • Multi-item optimization 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 optimization

ML algorithms optimize complex multi-SKU orders. The model learns which combinations pack efficiently 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 call

The Outcome

Lower shipping costs

ML-optimized box selection reduces dimensional weight charges. The system learns what works, so anyone packs correctly.

Faster

No more hunting for the right box for each order

Cheaper

Reduced DIM weight charges and shipping costs

Safer

Products arrive protected with right-sized packaging

Ready to optimize box sizing?

Our ML models will learn your shipping patterns and deliver packing recommendations that reduce costs.

Schedule a call