Transportation delays are one of the many potential disruptions supply chain managers face, and numerous factors play into them. Shippers often don’t know there’s a problem until after a shipment is late. This inevitably causes friction between shippers, their LSPs or freight forwarders, and their customers. 

This ebook describes how machine learning technology is used to generate predictive ETAs that build on robust in-transit visibility capabilities to provide the accurate arrival time estimates essential for mitigating disruptions.

Robust in-transit visibility highlights where goods are at a moment in time, but when shipments deviate from their planned timeline, knowing where they are is not enough. To make intelligent remediation decisions you need reliable ETAs. By combining in-transit visibility with accurate predictive ETAs, you can connect your logistics ecosystem with your extended supply chain. This allows you to go beyond just knowing the status of shipments to evaluating alternative options based on their overall inventory and revenue impact.

Please CLICK HERE to download the white paper.

Source: https://www.supplychainbrain.com/articles/36413-are-we-there-yet-realistic-etas-for-goods-on-the-move