Abstract
"Real-time supply chain data from IoT sensors across logistics stages. Includes GPS tracking, RFID inventory, environmental sensors, supplier performance metrics, and delivery patterns for predictive analysis."
Description
Dataset Purpose
Captures comprehensive real-time supply chain operations leveraging Internet of Things (IoT) technologies. Designed for developing intelligent supply chain management systems with predictive analytics, adaptive risk mitigation, and real-time tracking capabilities.
IoT Sensor Data
- GPS Sensors: Real-time location tracking of shipments and vehicles
- RFID Tags: Inventory identification and automated warehouse management
- Environmental Monitors: Temperature, humidity, shock detection for sensitive cargo
- Fleet Telematics: Vehicle performance, fuel consumption, route efficiency
Supply Chain Metrics
- Supplier Performance: Delivery timeliness, quality ratings, reliability scores
- Delivery Patterns: Transit times, route optimization, delay patterns
- Demand Forecasting: Historical sales data, seasonal trends, inventory turnover
- Transaction History: Order volumes, fulfillment rates, return rates
- Warehouse Operations: Storage utilization, pick/pack times, order accuracy
Logistics Intelligence
- Route Optimization: Dynamic routing based on traffic, weather, and delivery windows
- Risk Prediction: Potential supply chain disruptions and mitigation strategies
- Inventory Management: Stock level optimization, reorder point predictions
- Cost Analysis: Transportation costs, fuel efficiency, operational expenses
Data Characteristics
- Records: Continuous data streams from multiple logistics stages
- Coverage: Suppliers, manufacturers, warehouses, distribution centers, last-mile delivery
- Real-Time Updates: Live tracking with minute-level granularity
Applications
Predictive supply chain analytics, automated logistics decision support, risk management systems, route optimization algorithms, warehouse automation, and end-to-end supply chain visibility platforms.
Data Preview
| Timestamp | Shipment_ID | GPS_Latitude | GPS_Longitude | RFID_Tag | Temperature_C | Humidity_% | Transit_Status | Supplier_Score | Delivery_Delay_min |
|---|---|---|---|---|---|---|---|---|---|
| 2025-01-10 08:00 | SH001 | 51.5074 | -0.1278 | RFID_A123 | 18.5 | 45.2 | In_Transit | 92.5 | 0 |
| 2025-01-10 08:30 | SH002 | 48.8566 | 2.3522 | RFID_B456 | 22.8 | 52.1 | At_Warehouse | 88.3 | 15 |
| 2025-01-10 09:00 | SH003 | 40.7128 | -74.0060 | RFID_C789 | 16.2 | 38.5 | Delivered | 95.8 | 0 |
Showing first few rows for preview
Cite This Dataset
Kaggle (2026). Smart Logistics IoT Supply Chain Dataset. [Dataset]. Kaggle. https://www.kaggle.com/datasets/programmer3/smart-logistics-iot-dataset
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Original source: Kaggle (2026). Visit official page for more details.
Indexed by IoTDataset.com on Jan 14, 2026
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