An average shipment emits tens of thousands of sensor and third-party data points. Our customers have hundreds of lanes that contain thousands of shipments per day.
Dots on a map and streaming temperature data are nice, but they fail to tell us what action needs to be taken, limiting visibility program ROI.
Manually extracting patterns, trends and insights is not scalable or optimal – the process must be automated, and it must be self-learning.
Whether it’s a delayed shipment or a temperature excursion, reacting to OTIF excursions puts products at risk, reduces operational efficiencies and strains customer relationships.
Machine learning-enabled prediction minimizes delays, prevents temperature excursions, and keeps customers happy.
If you don't know what's causing your historical excursions, it's impossible to identify and predict when, where and how they'll strike again.
Gain clarity by getting granular with your data.
Visibility should not be about responding to alerts and alarms. Action what matters and leave the rest.
Inject intelligence into your visibility control tower by quantifying risk on your lanes and live shipments and knowing exactly which factors to address to assess critical tradeoffs.
Whether it's a recommendation to enhance packaging thermal life, not using a reefer throughout certain months in certain regions due to overcooling, or modifying your SOP departure time to increase on-time-delivery (OTD), recommendations are generated through deep learning algorithms that are based on the most important thing that no one has: context.
Backed by the brightest minds in VC, risk and supply chain
