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Industrial & Logistics AI Research Dataset Collection (2024 Edition)

Overview

This repository provides a structured collection of industrial and logistics datasets designed for artificial intelligence training, operational simulation, and supply chain optimization research.

The datasets represent realistic industrial environments including manufacturing production lines, warehouse inventory systems, fleet management operations, predictive maintenance logs, smart factory automation metrics, port logistics throughput, and global trade analytics.

All datasets are structured in CSV format and suitable for machine learning, forecasting models, anomaly detection systems, and industrial AI benchmarking experiments.

Dataset Domains

The collection includes:

Manufacturing Production Metrics
Warehouse Inventory Tracking
Fleet & Transportation Management
Supply Chain Delivery Performance
Predictive Maintenance Logs
Port & Maritime Logistics Throughput
Last-Mile Delivery Analytics
Demand Forecasting Data
Industrial IoT Sensor Monitoring
Transportation Cost Optimization
Export & Import Trade Statistics
Smart Factory Automation Performance

Technical Characteristics

Structured tabular datasets
Time-series compatible format
Multi-domain industrial coverage
Suitable for supervised & unsupervised learning
Supports predictive analytics & optimization models

Intended Applications

This dataset collection can be used for:

AI model training for industrial systems
Logistics optimization research
Supply chain risk analysis
Predictive maintenance modeling
Smart factory efficiency benchmarking
Transportation cost modeling
Industrial anomaly detection

Format

All datasets are provided in CSV format. Each file contains clearly defined headers for structured data processing.

License

MIT License – Free for research and educational use.

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