yogape/logistics-operations

Logistics Operations Database

About this Dataset

What's Inside

A complete operational database from a fictional Class 8 trucking company spanning three years. This isn't scraped web data or simplified tutorial content—it's a realistic simulation built from 12 years of real-world logistics experience, designed specifically for analysts transitioning into supply chain and transportation domains.

The dataset contains 85,000+ records across 14 interconnected tables covering everything from driver assignments and fuel purchases to maintenance schedules and delivery performance. Each table maintains proper foreign key relationships, making this ideal for practicing complex SQL queries, building data pipelines, or developing operational dashboards.

Who This Is For

SQL Learners: Master window functions, CTEs, and multi-table JOINs using realistic business scenarios rather than contrived examples.

Data Analysts: Build portfolio projects that demonstrate understanding of operational metrics: cost-per-mile analysis, fleet utilization optimization, driver performance scorecards.

Aspiring Supply Chain Analysts: Work with authentic logistics data patterns—seasonal freight volumes, equipment utilization rates, route profitability calculations—without NDA restrictions.

Data Science Students: Develop predictive models for maintenance scheduling, driver retention, or route optimization using time-series data with actual business context.

Career Changers: If you're moving from operations into analytics (like the dataset creator), this provides a bridge—your domain knowledge becomes a competitive advantage rather than a gap to explain.

Why This Dataset Exists

Most logistics datasets are either proprietary (unavailable) or overly simplified (unrealistic). This fills the gap: operational complexity without confidentiality concerns. The data reflects real industry patterns:

• Fuel prices track the 2022 diesel spike and 2023-2024 decline
• Driver turnover sits at 15% annually (industry standard)
• Equipment utilization averages 65% (typical for dry van operations)
• On-time delivery performance ranges 85-95% (realistic service levels)
• Maintenance intervals follow Class 8 PM schedules

Dataset Structure

Core Entities (Reference Tables):

• Drivers (150 records) - Demographics, employment history, CDL info
• Trucks (120 records) - Fleet specs, acquisition dates, status
• Trailers (180 records) - Equipment types, current assignments
• Customers (200 records) - Shipper accounts, contract terms, revenue potential
• Facilities (50 records) - Terminals and warehouses with geocoordinates
• Routes (60+ records) - City pairs with distances and rate structures

Operational Transactions:

• Loads (57,000+ records) - Shipment details, revenue, booking type
• Trips (57,000+ records) - Driver-truck assignments, actual performance
• Fuel Purchases (131,000+ records) - Transaction-level data with pricing
• Maintenance Records (6,500+ records) - Service history, costs, downtime
• Delivery Events (114,000+ records) - Pickup/delivery timestamps, detention
• Safety Incidents (114 records) - Accidents, violations, claims

Aggregated Analytics:

• Driver Monthly Metrics (5,400+ records) - Performance summaries
• Truck Utilization Metrics (3,800+ records) - Equipment efficiency

Key Features

Temporal Coverage: January 2022 through December 2024 (3 years)

Geographic Scope: National operations across 25+ major US cities

Realistic Patterns:

• Seasonal freight fluctuations (Q4 peaks)
• Historical fuel price accuracy
• Equipment lifecycle modeling
• Driver retention dynamics
• Service level variations

Data Quality:

• Complete foreign key integrity
• No orphaned records
• Intentional 2% null rate in driver/truck assignments (reflects reality)
• All timestamps properly sequenced
• Financial calculations verified

Use Case Examples

Business Intelligence: Create executive dashboards showing revenue per truck, cost per mile, driver efficiency rankings, maintenance spend by equipment age, and customer concentration risk.

Predictive Analytics: Build models forecasting equipment failures based on maintenance history, predict driver turnover using performance metrics, and estimate route profitability for new lanes.

Operations Optimization: Analyze route efficiency, identify underutilized assets, optimize maintenance scheduling, calculate ideal fleet size, and evaluate driver-to-truck ratios.

SQL Mastery: Practice window functions for running totals and rankings, write complex JOINs across 6+ tables, implement CTEs for hierarchical queries, and perform cohort analysis on driver retention.

Sample Questions to Explore

1. Which routes generate the highest profit margin after fuel costs?
2. How does driver tenure correlate with fuel efficiency and on-time performance?
3. What's the optimal preventive maintenance interval to minimize the total cost of ownership?
4. Which customers have the highest revenue-per-load and best payment terms?
5. How do seasonal patterns affect equipment utilization and revenue?
6. What safety incident patterns exist by driver experience level?
7. Which city pairs have the most reliable on-time delivery performance?
8. How does truck age impact maintenance costs and downtime?

Data Format

All tables are provided as CSV files with headers. Relationships documented in the included schema file. Compatible with:

• PostgreSQL, MySQL, SQL Server
• Python (pandas, SQLAlchemy)
• R (tidyverse, DBI)
• Tableau, Power BI, Looker
• Jupyter notebooks, R Markdown

Column Descriptions

See individual table documentation for complete field definitions. Key identifier patterns:

• driver_id format: DRV00001 through DRV00150
• truck_id format: TRK00001 through TRK00120
• load_id format: LOAD00000001 through LOAD00057000+
• All date fields: ISO format (YYYY-MM-DD or YYYY-MM-DD HH:MM:SS)
• Currency: USD (dollars and cents)
• Distance: Miles
• Fuel: Gallons

Known Limitations

Not Included:

• Hours of Service (HOS) compliance tracking
• Weather impact modeling
• Customer payment histories
• Insurance claim details
• Detailed cargo manifests
• Electronic Logging Device (ELD) data

Simplified:

• Safety incidents reduced to basic claims data
• Maintenance descriptions generalized
• Customer contracts simplified to term length
• Route planning without traffic/construction

These omissions are intentional, adding unnecessary complexity without improving analytical value.

Competitive Advantages Over Similar Datasets

Compared to UCI/Kaggle logistics datasets:

• 10x more records
• Proper normalization (not flat files)
• Multi-year temporal depth
• Financial transactions included

Compared to synthetic data generators:

• Domain-specific realism
• Industry-standard metrics
• Authentic operational patterns
• Built by a practitioner, not an academic

Compared to proprietary datasets:

• Fully open (no NDA required)
• Unrestricted use
• Documented generation process
• Reproducible

Suggested Citation

If using in academic work or portfolio projects:

Yogape Rodriguez (2025).
Synthetic Logistics Operations Database (2022-2024). 
Kaggle Dataset. https://www.kaggle.com/datasets/yogape/logistics-operations-database

Acknowledgments

Dataset generated using operational knowledge from 12 years in Class 8 trucking operations. Created to support career transitions from operations roles into data analytics positions.

Update Schedule

Current Version: 1.0.0

Planned Updates:

• v1.1.0: Add Hours of Service compliance table
• v1.2.0: Include weather impact data
• v2.0.0: Expand to 5-year history (2020-2024)

Community Contributions

Suggestions welcome for:

• Additional analytical scenarios
• Jupyter notebook examples
• SQL query templates
• Tableau/Power BI dashboards
• Data validation scripts

Tags

logistics transportation supply-chain sql-practice operations-research fleet-management business-intelligence time-series synthetic-data trucking freight analytics-education

File Structure

logistics_data/
├── drivers.csv (150 records)
├── trucks.csv (120 records)
├── trailers.csv (180 records)
├── customers.csv (200 records)
├── facilities.csv (50 records)
├── routes.csv (60 records)
├── loads.csv (57,000+ records)
├── trips.csv (57,000+ records)
├── fuel_purchases.csv (131,000+ records)
├── maintenance_records.csv (6,500+ records)
├── delivery_events.csv (114,000+ records)
├── safety_incidents.csv (114 records)
├── driver_monthly_metrics.csv (5,400+ records)
├── truck_utilization_metrics.csv (3,800+ records)
└── DATABASE_SCHEMA.txt (relationship documentation)

License

MIT License - Use freely for commercial, educational, or personal projects. Attribution appreciated but not required.

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