Client Overview
Custom Logistics is a leading global provider of transport and logistics services, managing complex supply chain networks. Its vast operations track real-time data on shipments, vehicle routes, and inventory levels. With substantial growth and seasonal fluctuations, Custom Logistics sought to enhance efficiency, customer service, and competitiveness.
Technical Challenge
Their traditional databases struggled with increasingly large data volumes. Performance issues led to inaccurate tracking, delivery delays, and inability to swiftly address changes. The need grew for scalable infrastructure to power real-time monitoring, predictive analytics, and supply chain optimizations.
Objectives
Implement a flexible, low-latency database solution to:
- Ingest and analyze real-time data at massive scale
- Enable rapid enhancements and changing data requirements
- Ensure data security and compliance
- Position Custom Logistics for innovation and future growth
Our Approach
After assessing options, Custom Logistics selected Microsoft Azure Cosmos DB for its global distribution, multi-model capabilities, and security features purpose-built for large, mission-critical systems.
Technical Solution
Azure Cosmos DB integrates with existing data sources for real-time ingestion across the supply chain infrastructure. Multi-model flexibility easily adapts to new data types without complex structural changes. Global data replication guarantees low-latency analytics. Robust encryption protects sensitive logistics data.
Implementation
Phased integration established real-time monitoring through operator apps and management dashboards. Gradual migrations eliminated downtime while optimizing parts of the infrastructure. As needed support during rollout helped navigate technical hurdles.
Business Impact
Cosmos DB empowered data-driven optimizations around routing, cargo loading, and traffic prediction. Accurate real-time tracking enhanced customer service. Adaptable resources resulted in cost savings. The modernized foundation sets the stage for AI/ML innovations.
Results
- 90% reduction in database response times
- 100% SLA uptime
- 10x improvement in data ingestion rates
- 5x more supply chain data sources integrated
