Delivery Operations Availability Analysis - SQL Queries

Overview

This repository contains SQL queries designed to analyze delivery operations availability, focusing on:

• Downtime & Uptime Analysis due to operational delays.
• Weather Impact on Deliveries & Service Availability.
• Regulatory Constraints Affecting Deliveries.

Each query is structured for scalability, performance optimization, and seamless integration with BI tools such as Power BI, Mode Analytics, Tableau, and Looker.

Queries Included

Facility Downtime & Uptime Analysis

Objective: Analyze facility downtime, uptime percentages, and affected packages to assess service disruptions.

Key Metrics:

• Total Downtime Events & Duration (Seconds & HH:MM:SS Format).
• Operational Uptime (%) per Facility & Country.
• Number of Orders & Packages Affected. Use Case: Identifying bottlenecks in service utilization and improving operational efficiency.

Weather Impact on Deliveries

Objective: Quantify the impact of severe weather conditions (high winds) on orders, flights, and packages.

Key Metrics:

• Max Wind Speed & Duration of High Winds (Minutes).
• Total Affected Facilities, Orders, and Packages.
• Disruption Ratios (Facilities, Orders, and Packages).
• Use Case: Predicting and mitigating weather-related delivery disruptions.

Regulatory Constraints on Deliveries

Objective: Analyze delays and cancellations due to regulatory constraints on orders and flights.

Key Metrics:

• Launch Delay (Minutes) between package commitment & flight launch.
• Order Delay (Minutes) between order confirmation & delivery.
• Regulatory Off-Nominal Events Affecting Orders & Flights.
• Use Case: Understanding compliance-related delays and optimizing logistics planning.

Conclusion

These queries provide critical insights into delivery operations, covering downtime, weather disruptions, and regulatory constraints. By pre-aggregating necessary calculations, this dataset is ready for integration into BI tools for further analysis and visualization.

Future Enhancements:

• Adding geospatial analytics for better impact assessment by region.
• Implementing machine learning models to predict disruptions based on historical trends.