CIGO Tracker AI Handle Time Predictions

Incorporate SKU-level assembly data into the delivery order.

CIGO AI predicts service time based on historical assembly speed for those specific SKUs.

System schedules stops with dynamic buffers.

Driver tracks actual time vs. predicted time in-app.

AI updates future predictions for those SKUs.

Technician logs into CIGO app to see daily route.

AI predicts handle time based on the specific machine model and historical repair logs.

Real-time ETAs are shared with hospital staff.

Dispatchers monitor 'Service Window Compliance' via the dashboard.

Automated reporting identifies equipment types that consistently exceed predicted times.

CIGO maps 'Friction Scores' for specific high-rise addresses.

Handle time is automatically increased for addresses with known parking issues.

Route optimization engine clusters stops to minimize 'walk time' between deliveries.

Customer receives SMS when driver is 2 stops away based on AI calculation.

Fleet manager reviews 'Dwell Time' analytics to optimize route densities.

Identify recurring delays at specific commercial receiving bays.

AI flags these locations as high-variance.

Dispatchers receive alerts to schedule these stops during off-peak hours.

Historical wait times are baked into the 'Service Time' for that stop.

Overall route reliability increases by 25%.

Digital compliance forms are integrated into the CIGO driver workflow.

AI measures time-to-complete for different form types.

Predicted handle time includes 'Paperwork Buffer' based on load volume.

Real-time sync ensures the next pickup is notified of delays immediately.

Compliance data is exported for annual audits.