Project Overview
This anomaly detection system monitors thousands of metrics across a single retail organization’s operations to identify unusual patterns that could indicate problems before they impact business performance. The system combines statistical methods with machine learning to detect anomalies across both business operations and IT infrastructure, providing a unified view of potential issues.
Technical Solution
System Architecture
We built a comprehensive monitoring solution with:
- Real-time data ingestion from multiple business systems
- Multi-model anomaly detection using various statistical and ML approaches
- Automated alert prioritization based on business impact
- Root cause analysis to identify underlying issues
- Visualization dashboard for operational teams
- Feedback collection system to continuously improve alert quality
Detection Methods
The system employs multiple complementary methods:
- Statistical process control for trend analysis
- Seasonal decomposition for cyclical metrics
- Isolation forests for detecting outliers
- LSTM neural networks for sequential data
- Ensemble techniques to reduce false positives while maintaining high recall
Implementation Challenges
Key challenges we addressed included:
- Handling thousands of metrics with different seasonality patterns
- Minimizing false positives while maintaining sensitivity to critical anomalies
- Integrating with disparate data sources across the organization
- Determining appropriate alert thresholds automatically
- Creating actionable alerts that drive resolution
- Balancing detection speed with accuracy for different types of anomalies
Business Impact
The solution delivers substantial value:
Operational Metrics
- Issue Detection Rate: 42% improvement in identifying relevant anomalies
- Alert Precision: 95% of alerts are confirmed as actual anomalies
- False Negative Rate: Only 7% of actual anomalies go undetected
- F1 Score: 0.91, providing a balanced measure of precision and recall
- Mean Time to Detect Critical Anomalies: 2.8 minutes for high-impact issues
- Detection Time: <5 minutes from anomaly occurrence to alert
Business Outcome Metrics
- Revenue Protected: Over £8M annually through preventive action
- Inventory Shrinkage Reduction: 32% decrease in inventory losses
- Fraud Prevention Rate: 76% of attempted fraud detected and prevented
- Process-Specific Improvements:
- 41% reduction in stockout incidents
- 28% improvement in promotion execution compliance
- 35% faster response to supply chain disruptions
User Experience Metrics
- User Satisfaction Score: 4.2/5 from store managers and operations teams
- Alert-to-Action Time: Reduced from 45 minutes to 12 minutes
- Alert Utilization Rate: 83% of alerts result in measurable actions
Evaluation Methods
The system’s performance is continuously assessed through:
- A/B Testing: Controlled experiments comparing performance with and without the system across store groups
- User Acceptance Testing: Regular feedback collection from front-line users
- Simulation Testing: Backtesting against historical anomalies
- Continuous Monitoring: Real-time performance dashboard tracking all KPIs
Technology Stack
- Google Cloud Functions for serverless execution
- Python with scikit-learn for ML algorithms
- Cloud Storage for data persistence
- Cloud Monitoring for metrics collection
- Custom dashboards for visualization
- Automated notification system integrated with Slack and email