Transportation delays are one of the many potential disruptions supply chain managers face, and numerous factors play a role. Shippers often don’t know there is a problem until a shipment is delayed. This inevitably creates tension between shippers, their LSPs or carriers, and their customers.
This e-book describes how to use machine learning technology to generate foresighted ETAs that build on robust in-transit visibility capabilities to provide the accurate time-of-arrival estimates essential to mitigate disruption.
Robust in-transit visibility shows where goods are at any given time, but when shipments deviate from their planned schedule, just knowing where they are isn’t 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 to your extended supply chain. This allows you not only to know the status of shipments, but also to evaluate alternative options based on their total inventory and impact on sales.
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