Field Service Prioritisation
Rank field work by what it protects — safety, supply and cost — and match it to the crews and parts actually available.
The decision it supports
Of all the work we could send crews to do, what should we do next and who should do it?
What changes
More of the right jobs done with the same crews, fewer repeat visits, and a defensible reason for every deferral.
Data domains
- Field service
- Assets
- Customers
- Grid and network
Interactive preview
Field Service Prioritisation is described here as an opportunity rather than shown as a working demonstration. The interactive build for this industry is Asset Health & Field Operations Twin — it uses the same primitives, the same governed data model and the same decision-to-action pattern this application would.
We build one flagship per industry properly rather than four shallow ones. The pattern transfers.
The workflow
- 01Open and predicted work is consolidated across the region
- 02Each job is scored against safety, supply risk and cost
- 03Crew skills, locations and parts on hand are matched to the work
- 04The scheduler compares plans for the day or week
- 05The agreed schedule is written back to the field system
- 06Completion and outcome feed the next plan
Platform capabilities most relevant here
OneLake
One governed copy of enterprise data that analytics, AI and applications can all build on.
Curated business entities
Agree once what an order, an asset or a customer means — then reuse it everywhere.
Data science and ML
Predict what is likely to happen next so people can act before it does.
Fabric Apps
Build the application where the governed data already lives, instead of copying it out.
Application state
Remember decisions, assignments and approvals — the things a report cannot hold.
Enterprise APIs
Turn a decision into a change in the system that actually runs the process.
Not every application needs every capability. This is the subset that this decision actually depends on.
Talk about building this