Optimizing Last-Mile Delivery Using Cloud Data Engineering
Built cloud-native predictive pipeline for last-mile logistics improving delivery success rates by 8–12%
Role
Cloud Data Engineering & Predictive Modeling Lead
Timeframe
Spring 2025
Team Size
6 engineers and data scientists
Tools & Technologies
Key Impact
8–12% improvement in delivery success rates, 10–15% cost reduction
Context
Delivery firms needed predictive models to optimize logistics, facing operational inefficiencies that led to delays, failed deliveries, and high costs.
Problem
Operational inefficiencies led to delays, failed deliveries, and high costs, impacting customer satisfaction and profitability.
Approach
Built GCP pipeline with Pig flattening for data preprocessing
Developed PySpark models for delivery success prediction
Implemented Kafka streaming for real-time data processing
Created D3.js visualizations for operational insights
Established automated alerting system for delivery optimization
Outcome
Improved delivery success rates by 8–12% through predictive modeling
Reduced operational costs by 10–15% via route optimization
Forecasted courier workloads with high accuracy
Optimized delivery routes with real-time alerts and recommendations
Next Steps
Deploy predictive models in live logistics networks across regions
Integrate with IoT sensors for enhanced real-time tracking
Expand to include weather and traffic data for improved predictions