Production Exception Manager
Interactive demonstrationOne place where a production deviation is detected, explained, costed and resolved — instead of three systems and a phone call.
The decision it supports
Given this deviation, what is most likely causing it and what is the least costly intervention right now?
What changes
Shorter time from deviation to intervention, fewer repeat incidents, and a decision record that survives the shift handover.
Data domains
- Production
- Assets and maintenance
- Quality
- Finance and costing
The workflow
- 01A deviation is detected on a line from live process and quality signals
- 02The operator opens the exception and sees the telemetry timeline around it
- 03The probable cause is proposed with the evidence that supports it
- 04Comparable historical incidents and how they were resolved are shown
- 05Production and cost impact are estimated for each option
- 06The operator selects an intervention
- 07A work order or line instruction is raised in the operational system
- 08The outcome is recorded and becomes evidence for the next occurrence
Platform capabilities most relevant here
Eventstreams
Let operational applications react to what is happening now, not what happened yesterday.
Eventhouse / KQL
Ask questions of high-volume machine and event data fast enough to act on the answer.
OneLake
One governed copy of enterprise data that analytics, AI and applications can all build on.
Ontology
Encode the vocabulary, relationships and rules your business already works with.
AI functions
Apply language models to business data as a normal part of a data pipeline.
Fabric Apps
Build the application where the governed data already lives, instead of copying it out.
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.
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