How We Helped NFI Industries Predict Spot Market Risk & Optimize Route Guide Performance
NFI, a leading $3B+ logistics provider, delivers comprehensive 3PL and 4PL services, including distribution, warehousing, and transportation across the U.S....
At a Glance
NFI, a leading $3B+ logistics provider, delivers comprehensive 3PL and 4PL services, including distribution, warehousing, and transportation across the U.S. With over 17,000 employees, they manage a vast network of carriers and loads across the U.S.
Challenge
Hindrance in forecasting route guide depth and preventing avoidable spot market exposure
The client struggled with limited visibility into which shipments were likely to fall out of their routing guides and end up in the spot market, leading to unexpected rate hikes and reactive decision-making. Their teams relied heavily on historical trends and manual monitoring, making it difficult to proactively identify high-risk shipments.
Moreover, the absence of predictive insights restricted their ability to optimize planning, manage brokerage operations efficiently, and deliver consistent pricing to customers. These challenges created operational inefficiencies and prevented NFI from fully leveraging their tender and shipment data for forecasting.
Solution
Delivering a logistics-focused predictive model powered by Databricks
With these challenges impacting shipment planning and cost management, the client approached zeb to build a predictive model that could forecast route guide depth and identify shipments likely to move into the spot market. They needed a partner capable of managing data engineering, building ML models, and operationalizing insights within their workflows.
Our structured approach included:
- Predictive Model Development: Developed machine learning models in Amazon SageMaker to forecast route guide depth and spot market risk using historical tender patterns, lane behavior, carrier performance, and utilization metrics.
- Data Preparation & Analysis: Engineered tender and shipment datasets using AWS Glue and Amazon Athena, performing cleansing, structuring, and exploratory analysis to identify key drivers influencing routing behavior and spot market entry.
- SageMaker-Powered Training & Deployment: Implemented scalable model training, automated versioning, and production deployment using Amazon SageMaker, orchestrated through AWS Step Functions and Amazon EventBridge to integrate predictions seamlessly into NFI's daily operational workflows.
- Visualization & Knowledge Transfer: Delivered interactive KPI dashboards using Amazon QuickSight, enabling clear visibility into route guide depth, lane performance, and spot market utilization. Provided documentation, ML handover, and training to ensure smooth operational adoption and ongoing maintenance.
Benefits
Predictive visibility, cost savings, and improved shipment reliability
- Smarter Shipment Planning: Proactive notifications helped brokerage teams manage lanes more effectively and reduce spot market exposure.
- Cost Optimization: Better forecasting minimized reliance on high-cost spot transactions and improved overall routing efficiency.
- Enhanced Customer Experience: Predictable pricing and stable routing improved service reliability across key lanes.
- Scalable Predictive Foundation: A strong base for expanding ML models across other logistics operations within NFI.
Conclusion
By combining AWS-powered machine learning with deep logistics expertise, zeb enabled NFI Industries to reduce uncertainty, strengthen planning accuracy, and gain predictive control over their routing ecosystem.
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