Delay prediction

Experimental feature

The Skrym delay inference model is still in training, which means you should, for the time being, be cautious with any automation related to Skrym events identifying a shipment as delayed.

Shipping data is inherently difficult to interpret. That's why Skrym is applying statistical inference and ML models to help identify shipments that are likely to be delayed.

Our goal is to give you information as early as possible when a shipment will be delayed, so that you can proactively communicate with the recipient and prevent angry calls to customer service.

How does it work?

Skrym uses a variety of data points related to the time and date of the shipment, as well as:

• Aggregated statistics for the transporter
• Average delivery times between major transport hubs
• Expected event chains and notification spans