Catch machine failures before they cost a shift
A mid-sized industrial manufacturer
A manufacturer whose machine, ERP, and quality data sit in separate silos could see failures coming. The idea: connect the data, predict failures, and put an agent in front of the alerts to triage and draft the work order.
The opportunity
Sensor data, ERP records, and quality logs live in systems that do not talk to each other. Unplanned downtime is discovered after a line has already stopped, and the know-how to diagnose it walks out the door with experienced staff. Maintenance stays reactive and expensive. That is the opportunity.
Target outcome
target: less unplanned downtime
What we would build
No black box. These are the concrete pieces we would put in place, in the order they matter, with a human in the loop wherever the stakes are high.
- 1
Build pipelines to combine sensor, MES, and ERP data into one trusted, time-aligned view per machine
- 2
Train failure-prediction models on the combined history, validated against real downtime events
- 3
Put an agent in front of the alerts to triage severity and suppress the noise that wastes a crew's time
- 4
Draft work orders with the likely cause and the relevant past fix, pulled from captured expert notes
- 5
Build a live line-level dashboard so supervisors can see risk building before it becomes a stoppage
Representative stack
We choose tools to fit the job and your constraints, and favor open-source and local options where they fit. We are not tied to any one vendor.
Time to value
We start narrow, prove the result against your real baseline, and expand once it holds.
Where this connects
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Want to put this idea to work in your business?
Take the assessment. We start from the outcome you need and turn it into a fixed-price plan in one working session.