UPS Logistics Optimization using SQL

📌 Project Overview

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.

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🗂 Dataset Structure

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

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🛠 SQL Techniques Used

• 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

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📊 Key Business Insights

• 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

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🎯 Business Recommendations

• Implement dynamic route optimization
• Improve warehouse staffing and sorting capacity
• Introduce preventive buffers for weather-related disruptions
• Deploy SLA monitoring dashboards with automated alerts

📁 Repository Structure

UPS-Logistics-Optimization-SQL/

├── UPS_Logistics_Analysis.sql  
├── UPS_Logistics_Optimization_Presentation.pdf  
└── Data/

💡 Outcome

This project demonstrates how SQL-driven analytics can move beyond reporting to enable operational optimization and measurable performance improvement in logistics networks.