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Application of the Internet of Things in the Logistics Field

Custom Solutions 2025-10-02 46 views

With the increasing complexity of global supply chain networks and the rapid development of the e-commerce economy, the logistics industry is facing multiple challenges such as improving efficiency, controlling costs, achieving end-to-end visibility, and upgrading services. Traditional logistics models suffer from numerous pain points in information collection, transportation tracking, warehouse management, and other areas, making it difficult to meet modern society's demand for efficient, transparent, and secure logistics services. The widespread application of Internet of Things (IoT) technology has brought new transformative momentum to the logistics industry. Through intelligent sensing, real-time connectivity, and data-driven approaches, IoT enables dynamic monitoring and intelligent scheduling of the entire logistics process, significantly enhancing operational efficiency and customer experience.

This article will systematically outline the core technical architecture, key application scenarios, and typical cases of IoT in the logistics field. It will provide an in-depth analysis of its crucial role in enhancing logistics intelligence and driving the industry's digital transformation, while also looking ahead to future technological evolution trends and innovation directions.

Core IoT Application Scenarios in Logistics

End-to-End Transportation Visibility
Utilizes technologies like GPS, RFID, and NB-IoT to achieve real-time location tracking and status monitoring of goods from warehouse to final destination, enhancing transportation transparency and security.
Transportation status at a glance
Intelligent Warehouse Management
Leverages sensors, automated sorting, robots, etc., to enable warehouse environment monitoring, automated inventory counting, and intelligent scheduling, improving warehouse efficiency and accuracy.
Smart and efficient inventory management
Cold Chain Logistics Monitoring
Uses temperature/humidity sensors, data loggers, etc., to achieve real-time monitoring of environmental parameters during cold chain transportation, ensuring the quality of sensitive goods like fresh produce and pharmaceuticals.
Temperature control fully traceable
Logistics Security & Loss Prevention
Deploys door sensors, vibration sensors, video surveillance, etc., to detect risks like unauthorized opening, impact, or loss in real-time, enhancing logistics security protection capabilities.
Timely risk warning and response

Logistics IoT System Implementation Process

Perception Layer – Logistics Data Collection
GPS Positioning Terminal
RFID/Barcode Tags
Temperature/Humidity Sensors
Door/Vibration Sensors
Network Layer – Data Transmission
WiFi/Ethernet
4G/5G/NB-IoT
LoRa/Satellite Communication
Edge Gateway Data Aggregation
Platform Layer – Data Processing & Analysis
Logistics Big Data Platform
Intelligent Scheduling & Route Optimization
Real-time Visual Monitoring
Risk Analysis & Security Management
Application Layer – Intelligent Services & Decision-Making
Anomaly Warning & Automatic Alerts
Management Dashboard & Mobile APP
Customer & Partner Collaboration
Intelligent Decision-Making & Operational Optimization
Traditional Logistics Model
Information silos, data sharing difficulties
Transportation status invisible, high risk of loss
Manual inventory counting, low efficiency
Cold chain breaks difficult to trace
Intelligent Logistics System
End-to-end visibility, real-time data sharing
Real-time transportation status monitoring, safe and controllable
Automated inventory counting, intelligent scheduling
Cold chain temperature control fully traceable

Best Practices for Intelligent Logistics System Implementation

1
Logistics Business Process Mapping
Comprehensively map the entire logistics process, identify key nodes and pain points, define digital transformation goals, and ensure the technical solution aligns closely with business needs.
2
Intelligent Sensing & Network Deployment
Deploy GPS, RFID, temperature/humidity sensors, etc., and build a multi-layer wireless communication network to ensure comprehensive data collection and stable transmission.
3
Platform Integration & Data Governance
Build a unified logistics data platform, integrate multi-source data from transportation, warehousing, customers, etc., and improve data governance and security management systems.
4
Continuous Optimization & Intelligent Decision-Making
Based on big data and AI analysis, continuously optimize transportation routes, warehouse scheduling, and risk warnings to enhance operational efficiency and customer experience.

Typical Case: Intelligent Logistics Upgrade for a National Express Delivery Company

Project Background

Company Scale: 2000+ nationwide outlets, daily parcel volume exceeding 20 million
Challenges Faced: Multiple transportation links, high risk of loss/damage, customer experience needing improvement
Transformation Goals: Achieve end-to-end visibility, intelligent scheduling, and cold chain quality assurance
Investment Scale: 300 million RMB, transformation period 18 months

Construction Content

Deployed 50,000 GPS positioning terminals
Applied 100 million RFID tags
Installed 20,000 sets of cold chain temperature/humidity monitoring devices
Built an intelligent logistics management platform

Intelligent Logistics Upgrade Effect Comparison

99.5%
Transportation Visibility Coverage
-80%
Reduction in Loss/Damage Rate
+35%
Improvement in Cold Chain Quality Assurance
+25%
Increase in Customer Satisfaction

Intelligent Logistics Development Trends & Challenges

Development Trends

Automation & Unmanned Operations: New models like unmanned warehouses, delivery vehicles, and drones are accelerating deployment.
AI Deep Empowerment: Widespread
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