Dynamic predictive maintenance
A global control tower over plants and production lines with uptime by line, predicted failures from streamed sensor data to investigate, a maintenance scheduler that matches the technician and the materials to each job and writes the work order, and a machine investigation that shows the sensor series and shares the operator's resolution with every plant running the same asset.
One working session with the people in the seat: which decisions, which systems, what a good week looks like.
The app you see above, running on your own exports instead of the simulated world. Every screen, every decision, your numbers.
Wired to your systems with your permissions, agents proposing, people approving, the ledger recording who did what.
Overview
Each plant watches its own machines and fixes what breaks. The same welder fails the same way in three countries and nobody sees the pattern, and when a job is planned the part or the certified technician is missing.
The workbench aggregates every plant: production volume against peak, performance rates and uptime per line. Failure models run on the streamed sensor data and surface predicted failures with the material and skill each needs; the scheduler finds the window, the technician from the HR roster and the material in the ERP and creates the work order. The machine investigation shows the root cause on the sensor series and posts it to the central team for the other plants.
Key features
- Predictive maintenance: real-time inference on streamed sensor data with failures surfaced for review.
- Staffing and material scheduling: ERP, HR and supply chain integrated so every job has the people and parts.
- Global control tower: plants and lines aggregated to find systemic issues and inefficiencies.
- Bidirectional communication: operator resolutions shared with central teams and other plants.
Decisions it produces
- Schedule maintenance for a predicted failure
- Approve the work order with technician and materials
- Flag an issue as systemic
- Share a resolution with the central team
Data sources
| Sensors | temperature, amplitude, current per machine |
|---|---|
| ERP | spare parts, work orders |
| HR | technicians, certifications, availability |
| MES | production volume, line uptime, yield |
Simulated data throughout. Book a call to see it on your own systems.
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