Averitt Express, a leading freight transportation and supply chain provider with international reach across more than 100 countries, operates a dedicated fleet serving a national chain of restaurants and retail stores. Deliveries run from a single distribution center near Nashville, Tennessee to over 600 retail locations across 45 states — each store typically receiving multiple shipments per week. In logistics operations of this scale, route and timing variability is unavoidable, but the downstream consequences are acute: store managers had no reliable way to predict truck arrival windows, forcing a choice between overstaffing docks to absorb delays or risking understaffed receiving when trucks arrived early. Both outcomes eroded labor efficiency across hundreds of locations simultaneously.
FourKites deployed its real-time supply chain visibility platform to bring Averitt's dedicated fleet loads under continuous tracking, using a structured onboarding process designed for rapid integration with existing transportation operations. The platform applies machine learning and predictive analytics to GPS and carrier data streams, generating up-to-the-minute ETAs that adapt dynamically as conditions change in transit. To reduce ongoing vendor dependency at scale, FourKites trained key Averitt personnel as superusers — equipping internal teams to operate and troubleshoot the platform autonomously. Averitt then extended platform access directly to its retail customer's store managers via iPads, enabling each location to monitor shipments from departure through final delivery without relying on centralized dispatch communication.
The implementation gave both Averitt and its retail customer the ability to calculate precise arrival windows based on live, high-accuracy data — a meaningful upgrade from static scheduled windows. Store managers gained direct visibility into real-time truck locations, allowing them to align dock labor to actual arrival times rather than planning around uncertainty.
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