Reducing Late Delivery Costs by 64% Using Predictive Analytics & Machine Learning

Business Problem

• Missed delivery commitments eroded customer trust.
• Surge in support calls & complaints.
• Risk of losing long-term contracts.
• $300 penalty per late delivery impacting profit margins.

Objective

Predict shipments likely to arrive late before dispatch, enabling proactive measures (express delivery, route adjustments, proactive communication) to reduce penalties and improve customer satisfaction.

Approach

• Analyzed 4,500 historical shipment records (region, distance, carrier, traffic, weather).
• Engineered new features like Distance_Weight_Ratio and Traffic_Weather_Risk.
• Tested multiple algorithms and selected Random Forest for best performance.
• Deployed as a REST API (FastAPI) for real-time and bulk CSV predictions.

Key Results

Business Value

The client transitioned from reactive firefighting to proactive logistics management. Beyond financial savings, customer satisfaction improved, and long-term contracts were safeguarded. The organization embraced data-driven decision-making, creating a foundation for future AI-driven improvements.