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Modernization of Global Logistics Platform with AI-Driven Microservices Architecture
  1. case
  2. Modernization of Global Logistics Platform with AI-Driven Microservices Architecture

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Modernization of Global Logistics Platform with AI-Driven Microservices Architecture

n-ix.com
Manufacturing
Logistics
Industrial Technology
Consumer Products & Services

Legacy Logistics Platform Limitations

Existing monolithic logistics platform lacks scalability and efficiency across 400+ warehouses globally. Manual processes create operational bottlenecks, while outdated computer vision algorithms and fragmented DevOps practices hinder automation and real-time tracking capabilities.

About the Client

Multinational engineering and technology company operating 440+ subsidiaries across 60+ countries with core sectors in Mobility, Industrial Technology, Consumer Goods, and Energy & Building Technology

Platform Modernization Goals

  • Implement cloud-native microservices architecture for global scalability
  • Integrate AI/ML and computer vision for automated logistics operations
  • Establish DevOps CI/CD pipelines for continuous delivery
  • Enable real-time package tracking and predictive analytics
  • Support multi-cloud deployment flexibility

Core System Capabilities

  • Microservices-based inventory management
  • Computer vision for package/barcode detection
  • Natural Language Processing for document verification
  • Real-time delivery status tracking
  • Predictive maintenance and route optimization
  • Multi-platform mobile application for warehouse operations
  • Automated damage detection using image analysis

Technology Stack Requirements

Azure Kubernetes Service (AKS)
Docker/Kubernetes containerization
TensorFlow/PyTorch for ML models
NVIDIA Jetson for edge computing
Spring Framework with JDK
PostgreSQL/MongoDB for data storage
Terraform/Helm for infrastructure as code

System Integration Needs

  • Existing ERP systems
  • Warehouse Management Systems (WMS)
  • IoT sensor networks
  • Cloud provider APIs (AWS/GCP/Azure)
  • Third-party logistics platforms

Operational Requirements

  • Support 400+ warehouses with sub-second latency
  • 99.99% system availability
  • Horizontal scaling to 10,000+ concurrent users
  • Multi-cloud deployment capability
  • GDPR-compliant data processing
  • Real-time data streaming with Kafka

Expected Business Outcomes

Enables 300% faster warehouse onboarding, reduces manual logistics work by 60%, and improves inventory accuracy to 99.8%. The microservices architecture will support rapid deployment across 60+ countries while AI-driven automation reduces operational costs by an estimated €2.5M annually per 100 warehouses.

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