Case Study: M2M Cargo Tracking

Overview

Union Pacific Railroad has a wireless sensor network that continuously captures and analyzes a wide range of data from trains moving along the track. The collected sensor data feeds into a central coordinating system, which tracks both the train and its cargo, and continually updates their expected arrival times. The system also monitors and identifies exceptions, using SaaSafras's communication solution to send notifications to a first responder team that can proactively take action to reach out to train crews and help resolve any issues.

Situation

Union Pacific is one of America's leading transportation companies. Its principal operating company, Union Pacific Railroad, operates on more than 32,000 miles of railroad track covering 23 states across the western two-thirds of the United States. In the competitive world of transportation services, Union Pacific needed to provide up-to-date information to its supply chain customers. Throughout its railroad network, Union Pacific deploys sensors on the tracks to catch train information. The challenge was to use this information to provide its customers with accurate information on the status of their cargo: where it is, when it is expected to arrive, and if there are any exceptions that may cause a delay.

Solution

The central coordinating system implemented by SaaSafras aggregates incoming data from the network of railroad sensors and integrates this information with Union Pacific's supply-chain system to provide real-time updates on train and cargo status. It also monitors expected "missing events", such as when a train expected to pass through a sensor does not do so at the scheduled time. In this case, the coordinating system triggers a notification to system administrators and field engineers to proactively track the freight train and check software systems.

Benefits

Union Pacific can now offer the real-time tracking capabilities expected from their core supply chain customers, with an improved ability to pro-actively identify potential exceptions in the railroad network and address them quickly. The result is improved on-time performance and competitiveness.