Automation & Operations
Order Drop
Early Warning
Hourly anomaly layer for BigHaat order movement, designed to surface unusual shifts earlier in the day.
Changed
Created a faster signal layer for operational response instead of waiting for end-of-day diagnosis.
Took away
Decision support matters most when it connects signals to urgency and clear follow-up decisions.
Tools / frame
Context
Order movement can change quickly across channels and categories. Teams need early signals when something unusual starts happening.
Problem
When order movement shifts across channels or categories, teams need a clear signal that helps them investigate sooner.
Contribution
Built hourly anomaly detection with Python, dashboards, and alerting logic so teams could spot drops and investigate sooner.
Tools used
Impact / learning
Created a faster signal layer for operational response instead of waiting for end-of-day diagnosis.
Decision support 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.