A large terminal operator managing truck, rail, and barge movements struggled with congestion, long turnaround times, and inconsistent scheduling efficiency. Key data—including appointments, access logs, rack activity, and SCADA signals—was scattered across siloed systems. Manual coordination through emails, spreadsheets, and legacy scheduling tools created bottlenecks and prevented the terminal from operating at full capacity. Leadership needed a unified, intelligent scheduling solution to optimize throughput and reduce operational friction.
We helped the terminal achieve measurable improvements across throughput, congestion, and operational efficiency:
12% increase in throughput across key product lanes
Reduced truck and rail turnaround times, improving customer experience
Lower congestion and fewer scheduling conflicts during peak hours
Greater utilization of racks, berths, and loading equipment
Improved planning visibility, enabling supervisors to proactively manage constraints
THE SITUATION: Terminal operations require precise, real-time awareness of product availability, equipment readiness, staffing, and transportation schedules. Fragmented systems made it difficult to coordinate these elements effectively. Limited visibility created misalignment between arriving trucks, railcars, berths, and actual loading capacity. Supervisors lacked predictive insight into congestion, causing delays, rework, and poor customer experience for carriers and shippers.
THE PROBLEM: The operators faced significant data fragmentation, visibility gaps, and scheduling challenges that restricted throughput and increased congestion, including:
Fragmented scheduling data across TMS, SCADA, access control, and manual logs
Limited visibility into real-time capacity across racks, berths, and storage
Inefficient sequencing of truck and rail appointments
Frequent conflicts between product availability, equipment readiness, and staffing
No predictive insight into peak congestion or scheduling conflicts
The Frame Solution
Built a unified scheduling optimization platform that integrated operational data, introduced predictive intelligence, and automated conflict resolution—dramatically improving planning accuracy and terminal flow.
Integrated TMS, access logs, rack data, meter volumes, and SCADA signals into a standardized Lakehouse
Developed an optimization engine using ML + operations research to sequence appointments efficiently
Built predictive models to forecast congestion windows and resource constraints
