Maintenance Prioritisation Workspace
Rank maintenance work by what it protects — production commitments, safety and cost — rather than by when the ticket was raised.
The decision it supports
Of everything we could do in the next maintenance window, what should we actually do?
What changes
Higher availability from the same maintenance capacity, and a defensible reason for every deferral.
Data domains
- Assets and maintenance
- Production
- Sales and customers
- Finance and costing
Interactive preview
Maintenance Prioritisation Workspace is described here as an opportunity rather than shown as a working demonstration. The interactive build for this industry is Production Exception Manager — 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 maintenance demand is consolidated across sites
- 02Each item is scored against production plan, risk and cost exposure
- 03Available windows, parts and crew capacity are matched to the demand
- 04The planner compares scenarios for the coming window
- 05The agreed plan is written back to the maintenance system
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.
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