What challenges did dnata face before AI implementation?

Inefficient and error-prone cargo handling and paperwork Difficulty optimizing cargo build-up and space loading Need for precise cargo weight and dimension measurement for flight safety

What was the impact of the AI solution?

Improved cargo acceptance, storage, and tracking accuracy Reduced labor and time for cargo packing Optimized use of cargo space on flights Enhanced cargo security and regulatory compliance

Source

MicrosoftLive sourceProductionEvidence: Medium65/100

dnata Optimizes Air Cargo Operations with AI and 3D Computer Vision

Use case typeComputer vision checkoutUpdated Jun 13, 2026

dnata, a global aviation and cargo service provider based in Singapore, sought to enhance operational efficiency in cargo handling at Changi Airport. The challenges included improving cargo build-up, weight measurement, space loading, and reducing manual paperwork errors. Partnering with SPEEDCARGO and leveraging Microsoft Azure technologies, dnata implemented an AI-driven digital platform incorporating Azure IoT Edge, Azure IoT Hub, Azure Data Lake, and 3D Time-of-Flight camera-based computer vision technology. This system captures real-time cargo dimensions, integrates with dnata's systems, and automates cargo planning and manifest creation, improving capacity utilization and operational accuracy.

Organization: dnata
Industry: Logistics
Location: Singapore
Published: June 2021

Reported outcomes

Strategic outcomes

New product / capabilityAutomated cargo planning and trackingBetter decisions & insightImproved cargo measurement accuracyScale & capacityOptimized flight cargo space usageRisk & complianceEnhanced cargo security and compliance

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: dnata
Provider: Microsoft
Maturity: Production
Linked source: Microsoft Azure Dev Blogs

dnata, a global aviation and cargo service provider based in Singapore, sought to enhance operational efficiency in cargo handling at Changi Airport

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

13D Computer Vision for Cargo Dimensioning
2AI-based Cargo Load Optimization
3Workflow Automation for Ground Cargo Handling

Inefficient and error-prone cargo handling and paperwork
Difficulty optimizing cargo build-up and space loading
Need for precise cargo weight and dimension measurement for flight safety

Implemented SPEEDCARGO's AI platform using Microsoft Azure IoT Edge, IoT Hub, and Data Lake
Deployed 3D ToF camera technology for accurate cargo dimension capture
Automated load planning, manifest creation, and cargo tracking processes integrated with dnata's operational systems

Technologies

Azure IoT Edge Azure IoT Hub Azure Data Lake Azure AI 3D Computer Vision

Improved cargo acceptance, storage, and tracking accuracy
Reduced labor and time for cargo packing
Optimized use of cargo space on flights
Enhanced cargo security and regulatory compliance

Architecture

Cargo dimension data captured via 3D ToF cameras is routed to Azure IoT Edge devices and IoT Hub, then stored and analyzed in Azure Data Lake. SPEEDCARGO's platform integrates AI algorithms for load planning and manifests. The system provides real-time feedback to ground loaders and planners to optimize resource utilization and complies with airline and safety regulations.

Implementation partners1

SPEEDCARGO

Sources & evidence1

Evidence: Medium65/100Evidence strength