Vantage Field Ops
Work-order automation and predictive scheduling for 380 field technicians.
- Client
- Vantage Utilities
- Industry
- Utilities & energy
- Discipline
- Enterprise
- Duration
- 6 months, 2024
The problem
Vantagescheduled380techniciansfromacallcentreusingawhiteboardconventionthathadgrownoveradecade.Roughlyafifthofjobswereassignedtoatechnicianwithouttherightpartsorcertification,producingasecondvisit.
Each repeat visit cost around R1,900 in vehicle, labour, and rescheduling time, before counting the customer relationship.
What we did
We automated the assignment path first: work orders now route on skill, certification, parts availability, and travel time, with dispatchers handling exceptions rather than every job.
A demand model trained on four years of history schedules preventative work into the gaps, so technician utilisation rises without anyone driving further. Predictions are explained in the interface: dispatchers can see why a job was suggested and override it.
Technicians work in a mobile app that functions in basements and rural dead zones, closing jobs offline with photographic evidence that syncs when signal returns.
Services applied
Technology
- TypeScript
- Python
- Temporal
- React Native
- PostgreSQL
- AWS
- Grafana
- Claude
Gallery
Inside the build.
Results
What changed, measured after launch.
- Repeat visits from wrong assignment
- -0%Repeat visits from wrong assignment
- Annualised cost avoided
- R14.2mAnnualised cost avoided
- Technician utilisation
- +0%Technician utilisation
- Field staff on the platform
- 0Field staff on the platform
“The scheduling model is good, but the thing that changed our operation was how carefully they handled the exceptions.”
Nomvula RadebeGeneral Manager, Field Operations, Vantage UtilitiesRelated work
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Similar problem?
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