AI-powered freight optimization-from reactive to predictive logistics
A global industrial manufacturer operating multiple business units and a highly complex, global distribution network. The organization manages large-scale movement of finished goods across plants, internal distribution centers, and customer locations. With annual air freight spend in the tens of millions of dollars, the client’s logistics operations, while cost-intensive, are operationally critical.
The supply chain was trapped in a reactive loop, where delays surfaced too late, forcing expensive air freight decisions such as frequent last-minute switches from ocean to air. Without early risk signals, millions in logistics spend were being lost to preventable expedited decisions and actions, rendering cost control nearly impossible.
Key challenges
Lack of proactive decision-making tools
High dependence on expedited air freight
Pressure to deliver measurable hard savings
The solution
Predictive intelligence
7-day early alerts
ML-based forecasting
Risk prediction engine
Conversion probability scoring
Prescriptive optimization
Alternate sourcing options
Cost-based recommendations
Linear optimization engine
Feasibility-driven decisions
1
Rollout
Predict
Optimize
Iterations
Staged rollout
2
Governance and tracking
KPI tracking
Savings audit
Monthly tracking
Finance validation
3
Data and integration
ERP integration
Data feeds
Dashboards
Analytics
Cost reduction
Lower air freight usage
Direct logistics savings
Reduced expedited shipments
Operational efficiency
Improved planning accuracy
Reduced last-minute changes
Better inventory utilization
