Work order repair exceptions
Malleable turns maintenance exceptions into evidence-backed repair plans, pulling together work orders, manuals, asset history, and supervisor judgment before dispatch.
Detect Fault
Collect alert details, asset, line, severity, and production impact.
Read History
Review asset history, manuals, prior failures, and maintenance notes.
Recommend Repair
Propose likely cause, repair path, parts, technician skill, and urgency.
Supervisor Review
Ask supervisor to decide safety, downtime, or production trade-off.
Dispatch Work
Assign technician with supervisor decision, evidence, and downtime notes attached.
- 1Detect Fault: Collect alert details, asset, line, severity, and production impact.
- 2Read History: Review asset history, manuals, prior failures, and maintenance notes.
- 3Recommend Repair: Propose likely cause, repair path, parts, technician skill, and urgency.
- 4Supervisor Review: Ask supervisor to decide safety, downtime, or production trade-off.
- 5Dispatch Work: Assign technician with supervisor decision, evidence, and downtime notes attached.
Why This Works Better with Malleable
Start simple, improve over time
You just describe what you want to happen. The workflow integrates everything—no separate forms, brittle automations, or AI copilots to wire together. Iterate on your process without rebuilding.
No separate systems to integrate
The diagram you see is what executes. AI reasoning, data handling, and notifications all happen as part of the workflow—not as separate automations you need to configure.
Results
- Repair packets are complete
- Supervisors review real trade-offs with context
- Technicians receive context-rich work orders
Integrations
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