AI-Driven Load Optimization for a Leading 3PL Provider
Client Overview
Industry: Third-Party Logistics (3PL)
Location: United States
Operations: International freight management with multiple carrier partnerships
Business Challenge
- Inefficient Load Planning: Manual processes led to suboptimal load planning, underutilized capacity, and higher transportation costs.
- Diverse Client Needs: Each customer prioritized different factors—some focused on speed, others on minimizing cost, and some required specialized equipment (e.g., hazmat, boom trucks, long beds).
- Complex Constraints: Needed a system to manage both Less Than Truckload (LTL) and Full Truck Load (FTL) scenarios, across single and multi-customer loads.
- Lack of Real-Time Decision Support: Planners lacked tools to identify consolidation or optimization opportunities in real time.
Our Solution
We built a comprehensive, AI-powered transportation optimization module integrated into the client’s logistics platform.
Core Capabilities
Load Consolidation Engine
- Consolidates loads across single or multiple customers based on location, delivery window, and freight type.
- Optimizes trailer utilization while respecting constraints like hazmat compatibility or vehicle type.
AI-Based Optimizer
- Dynamically selects best-fit carriers and load plans based on:
- Least Cost
- Fastest Delivery
- Special Requirements (e.g., hazmat, boom trucks)
- Suggests optimal plans to dispatchers via a real-time dashboard.
Approval-Driven Optimization
- AI provides load optimization suggestions with rationale.
- Operations team can approve, reject, or adjust the recommendation before dispatch.
Support for LTL and FTL
- Determines best load mode (LTL vs. FTL) using shipment volume, timing, and historical cost-performance data.
- Consolidates LTLs where cost-benefit thresholds are met.
Customer Preference Profiles
- Customizes optimization logic per customer (cost-focused, speed-focused, etc.).
- Supports contracts with varying SLA expectations.
Business Impact
| Metric | Before Our Solution | After Implementation |
|---|---|---|
| Average Truck Utilization | ~60% | 85–90% |
| Load Planning Time | 2–3 hours daily | ~30 minutes |
| Shipment Cost per Mile | Baseline | Reduced by 18% |
| On-Time Delivery (SLA Adherence) | 82% | 94% |
| Multi-Customer Load Handling | Manual, limited | AI-driven, scalable |
Tech Stack Highlights
- AI/ML: Python (Scikit-learn, PyTorch), heuristic & rule-based planning
- Backend: Java Spring Boot
- Frontend: Angular + Material UI
- Database: PostgreSQL
- Integration: REST APIs, JSON/XML, Excel import/export
- Cloud: AWS EC2, S3, RDS