HAWK: Predictive Operations Intelligence Platform

Multi-site operational intelligence with subprocess-level bottleneck detection

Overview

HAWK is a multi-site operational intelligence platform built at Amazon WW Grocery that provides real-time visibility into operational performance across warehouse sites.

The platform combines distributed systems architecture with machine learning to detect bottlenecks, perform predictive analytics, and enable data-driven decision making.

Business Impact

35%
MTTR Reduction
Faster incident detection and resolution
40%
Process Variance Reduction
More predictable operations
30% → 85%
Manager Adoption
Increased platform usage
25%
Overtime Reduction
Better resource planning

Technical Architecture

  • •Hybrid system combining SNS/Lambda event processing with time-series and NoSQL databases
  • •Real-time analytics engine using SageMaker and LLM integration for intelligent insights
  • •Sub-200ms latency requirement met through careful optimization and caching strategies
  • •Microservices architecture enabling independent scaling and deployment

Key Features

  • ✓Subprocess-level bottleneck detection across multiple warehouse sites
  • ✓Comparative performance benchmarking between sites and time periods
  • ✓Predictive ETAs using machine learning models
  • ✓Cyclical pattern detection for capacity planning
  • ✓Real-time dashboard for operational monitoring

Technologies Used

Cloud

AWS LambdaSNSSQSEC2CloudWatch

Databases

DynamoDBTime-series databasesElasticsearch

Analytics

SageMakerLLM IntegrationCustom ML models

Languages

JavaKotlinPython

Key Learnings

  • →Building systems that serve millions of daily operations requires careful attention to performance and reliability
  • →Cross-functional collaboration is essential for understanding operational requirements and delivering impactful solutions
  • →Machine learning can significantly enhance operational intelligence when properly integrated with domain expertise
  • →End-to-end ownership from design through operations ensures accountability and enables rapid iteration

Frequently Asked Questions

How does HAWK detect bottlenecks?

HAWK uses real-time data streams from warehouse operations combined with machine learning models to identify where processes are slowing down. It analyzes subprocess-level metrics and compares against historical patterns and peer sites.

What was the biggest technical challenge?

Meeting the sub-200ms latency requirement while processing data from multiple warehouse sites. We solved this through a hybrid architecture with local caching, optimized queries, and strategic use of time-series databases.

How is the platform maintained and scaled?

HAWK uses microservices deployed on AWS with CI/CD pipelines. Each component can be scaled independently based on demand, and we have automated monitoring and alerting through CloudWatch and custom dashboards.

What role did AI/LLMs play?

We integrated LLMs to generate intelligent insights from operational data, provide natural language explanations of anomalies, and suggest optimization strategies based on historical context.