What better rail ETAs mean for grain shipments

Podcast host Steven Kilger talks with Andy Adams, senior solutions engineer at TransmetriQ discusses how the company aggregates rail data from across North America to improve estimated arrival times for grain shipments. The platform uses machine learning to predict ETAs 20 to 30% better than standard industry methods. Adams explains how cloud-based tracking helps grain elevators and feed manufacturers plan staffing and operations more efficiently. The system automatically finds and monitors rail cars and containers, replacing spreadsheet-based tracking methods common at smaller operations.