Shipium Models | AI models purpose-built for complex supply chains
Meet the ship-ai suite of models
Customers model and predict the speed, cost, and accuracy of their shipping in real-time with Shipium’s unique machine learning capabilities.
How Shipium's Machine Learning Works
The ship-ai-transit model predicts days-to-delivery, or the number of business days it takes for a shipment to go from origin to destination.
The ship-ai-cost model compares fully-loaded carrier rates — including surcharges and accessorials — to find the most cost-effective carrier for each shipment.
The ship-ai-volume model predicts daily volume and manages allocation to ensure that volume commitments are met at the lowest possible cost.
Shipium’s platform is built atop a suite of enterprise machine learning models that help shippers manage the complexity of modern supply chains. By combining deep domain expertise with an industry-leading data science program, we enable shippers to continuously move toward their desired mix of speed, cost, and accuracy.
Shipium is powered by 10+ machine learning models to support every use case.
Here are the most widely used.
ship-ai-transit model
Improve Upstream Decisions and Delivery Accuracy
The ship-ai-transit model predicts days-to-delivery, or the number of business days it takes for a shipment to go from origin to destination.
ship-ai-cost model
Drive More Accurate Rate Shopping
ship-ai-volume model
Manage Carrier Volume Allocation
The ship-ai-volume model predicts daily volume and manages allocation to ensure that volume commitments are met at the lowest possible cost.
More Aggressive Delivery Estimates, While Maintaining Enterprise Accuracy
Our models predict faster transit times compared to carrier service SLAs for the same ground method in 40% of cases
Shipium’s flexibility helps us optimize for what’s best for Duluth while still getting the benefits of automation across all our shipping operations.
Brad Comstock Sr. Director Enterprise Technology, Duluth Trading Company
99.1%
OTD during 2024 Peak
11%
Reduction in Late Deliveries, 2025 YTD