This project analyzes delivery delays, route inefficiencies, warehouse bottlenecks, and delivery agent performance using structured relational logistics data.
The objective is to identify operational inefficiencies and recommend data-driven improvements to enhance SLA compliance and overall delivery reliability.
The analysis is based on five core relational tables:
- Orders – Order-level delivery details and timelines
- Routes – Distance, travel time, and traffic delay metrics
- Warehouses – Processing time and dispatch performance
- Delivery Agents – Agent-level performance metrics
- Shipment Tracking – Checkpoint-level delay insights
- Aggregations (AVG, COUNT, SUM)
- Window Functions (RANK, DENSE_RANK)
- Common Table Expressions (CTEs)
- KPI Calculations
- Delay computation using DATEDIFF
- Efficiency Ratio Analysis
- SLA Performance Benchmarking
- Overall On-Time Delivery Rate: 56% (Below 80% SLA Target)
- Top 3 routes contribute to over 50% of delayed shipments
- 30% of warehouses drive the majority of processing delays
- 72% of delivery agents operate below SLA threshold
- Intermediate checkpoints (2 & 3) are major congestion points
- Implement dynamic route optimization
- Improve warehouse staffing and sorting capacity
- Introduce preventive buffers for weather-related disruptions
- Deploy SLA monitoring dashboards with automated alerts
UPS-Logistics-Optimization-SQL/
├── UPS_Logistics_Analysis.sql
├── UPS_Logistics_Optimization_Presentation.pdf
└── Data/This project demonstrates how SQL-driven analytics can move beyond reporting to enable operational optimization and measurable performance improvement in logistics networks.