Automation & Operations
Order DropEarly Warning
Order DropEarly Warning
OperationalAutomation
Hourly anomaly detection that surfaced order drops early enough for faster operational follow-up.
Context
Order movement can change quickly. Teams need early signals when something unusual happens.
Problem
Manual diagnosis can be slow when order drops happen across channels, categories, or operational workflows.
Contribution
Built hourly anomaly detection with Python, dashboards, and alerting logic to support faster diagnosis of order drops.
Tools used
PythonAnomaly detectionDashboardsAlerts
Impact / learning
Created a faster signal layer for operational response and diagnosis.
Data Science work matters most when it connects signals to urgency and clear follow-up decisions.
Future direction
Extend the case with alert examples, diagnosis workflows, and how teams can prioritize causes.