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How a manufacturer cut unplanned downtime by 47% with predictive AI.
Machine breakdowns were stopping production lines every week, while the sensor data that could have warned them sat unused. Skillfi's fractional CTO brought that data onto one platform and built models that spot failures days before they happen. It's how our fractional CTO service works: senior technical leadership, exactly when you need it.
The machines were warning them. Nobody was listening.
The company makes moulded and stamped components for automotive and industrial customers across three plants. Every week, at least one critical machine broke down without warning and stopped a production line.
Each hour of downtime cost around $18,000 in lost output, overtime and rushed parts, and late deliveries were straining key customer contracts. Maintenance was either reactive or done on a fixed calendar. The machines already recorded vibration, temperature and pressure data, but it stayed inside each machine. There was no data team and no plan to use it.
We were sitting on years of machine data and still finding out about failures when the line stopped.
VP of Operations, client
Connect the data, predict the failure, fix it before it stops the line.
Our fractional CTO built the business case, chose the technology and led the work from pilot to all three plants. Maintenance planning changes were rolled out with fractional COO support.
Machine data in one place
Sensors on 120 critical machines connected through secure plant gateways to a unified data and AI platform, alongside two years of maintenance records.
Models that spot trouble early
Machine learning models trained on past failures to recognise the warning signs, scoring every machine's health every hour.
Alerts maintenance can use
Warnings sent straight into the maintenance team's work orders and phones, with the likely fault and suggested parts, so fixes are planned instead of rushed.
9 months, step by step
From reactive repairs to failures predicted days ahead, across three plants. Here's how the engagement unfolded.
- Month 1120 machines chosen
Assessment & business case
Downtime costs, machine data and maintenance records reviewed to pick the machines that mattered most.
- Month 2Pilot line live
Platform & first connection
The data platform set up and one pilot line's sensors streaming in securely, with plant IT and security signed off.
- Month 3–487% accuracy
First models
Failure models trained and tested on 24 pilot machines against two years of breakdown history.
- Month 5First failures prevented
Alerts in the workflow
Warnings connected to work orders and technicians' phones, with a weekly planning routine built around them.
- Month 6–83 of 3 plants
All three plants
Rollout to all 120 critical machines, maintenance teams trained and the first data engineer hired.
- Month 9$1.5M a year
Results & next steps
Savings measured against the business case and a roadmap agreed for quality and energy use cases.
From sensor signal to repair
How a warning sign on the shop floor becomes a fix scheduled days before anything breaks.
Live machine signals
- Vibration & temperature
- Pressure & motor load
- Cycle counts
One trusted data source
- Secure plant gateways
- Maintenance history joined
- Cleaned every hour
Health score per machine
- Failure risk each hour
- Likely fault identified
- Days-ahead warning
Fixed on your schedule
- Work order raised
- Parts ready in advance
- Repair in planned downtime
Nine months after kickoff
Unplanned downtime, hours a month
Across all three plants, measured over the last three months.
Our maintenance team used to be firefighters. Now they get a warning days ahead, order the part and fix the machine during a planned stop. The lines just keep running.
VP of Operations, clientSitting on machine data you're not using?
Our fractional CTO will build the business case, choose the right data and AI platform and lead your first AI use case from pilot to every plant. Want the operations side covered too? Add a fractional COO.
- A data & AI business case
- A connected data platform
- AI models that predict failures
- Alerts built into your workflow
30 minutes. No obligation.
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