Retail
Real-Time Event-Driven Platform for Supply-Chain & Inventory Efficiency
Client
Walmart Canada
Industry
Retail & Supply Chain
Services
ML Engineering
Duration
Enterprise Grade
Executive Summary
Engineering resilience through Walmart Canada's transformation.
We engineered a scale-ready infrastructure for a global retail leader, capable of handling millions of SKU updates per second. Our solution ensures optimal shelf availability and highly accurate global inventory management.
The Technical Challenge
- Overcome legacy batch processing limitations that caused significant inventory lag.
- Address inaccuracies in stock levels that led to missed sales and customer frustration.
- Scale the systems to handle unpredictable and massive peaks in consumer demand.
Our Engineering Approach
- Architected a high-throughput event mesh powered by Apache Kafka for real-time data flow.
- Deployed auto-scaling machine learning inference to manage and predict demand spikes.
- Developed a unified global inventory view integrated across thousands of retail locations.
Measurable Outcomes
Business Impact
99.9%
Inventory accuracy achieved across all primary global distribution hubs.
40%
Reduction in out-of-stock incidents during high-volume peak seasons.
Real-Time Visibility for Global Retail Operations
By transitioning to a pure event-driven architecture, we eliminated hours of latency. This enables truly just-in-time replenishment cycles that keep the world's largest retailer moving efficiently.
Technical Stack
Apache KafkaKubernetesAzure CosmosDBPyTorch
Core Disciplines
Supply ChainReal-timeInventory Management
