City-Level SLA & Operations Bottleneck Analysis (SQL)

Business Problem

Operations teams observed SLA degradation and delivery delays across cities. This project analyzes order-level data to identify underperforming regions, measure SLA %, and uncover operational bottlenecks.

Dataset

Order-level operational data containing:

• Order ID
• Order Date
• City
• Promised Delivery Time
• Actual Delivery Time
• Order Amount

Analysis Performed

• Classified orders as SLA Met or SLA Breached using CASE logic
• Calculated overall SLA percentage
• Analyzed SLA performance city-wise
• Measured average delivery delays to assess severity
• Tracked month-over-month SLA trends

Key Insights

• SLA performance varies significantly by city
• High-demand cities tend to show lower SLA percentages
• Average delay analysis highlights severity beyond simple SLA breaches
• Monthly trends help identify emerging operational risks

Tools Used

• MySQL
• Aggregate functions
• CASE statements
• GROUP BY and filtering

Outcome

This analysis supports data-driven decisions for capacity planning, SLA improvement, and operational optimization.

How to Use

1. Create the database and table using schema.sql.
2. Insert sample data and run analysis queries from queries.sql.
3. Review query outputs to understand SLA performance, delivery delays, and city-level bottlenecks.