Manufacturing
27% Reduction in Unplanned Downtime in 90 Days
Precision Engineering Manufacturer · West Midlands, UK · 120 staff across 2 production facilities
The Challenge
The manufacturer was losing an average of 14 hours per week to unplanned machine downtime. Maintenance was entirely reactive — equipment ran until it broke. Production scheduling was done in spreadsheets, with no real-time visibility into line performance or bottlenecks.
- 14 hours per week lost to unplanned machine breakdowns
- No predictive maintenance — fully reactive approach
- Production scheduling based on static spreadsheets
- Quality defects caught too late in the production cycle
What We Built
We connected their MES, IoT sensor feeds, ERP, and quality management systems through one AI layer. The system now monitors equipment health in real time, predicts failures before they happen, optimises production scheduling based on order priority and machine availability, and flags quality deviations early.
- Real-time machine health monitoring via IoT sensor analysis
- Predictive maintenance alerts with recommended action windows
- Dynamic production scheduling adapting to live conditions
- Early-stage quality deviation detection reducing scrap rates
Results
- 27% Unplanned downtime — Reduction within the first 90 days
- £185K Maintenance costs — Annual saving from predictive vs reactive approach
- 18% Production throughput — Increase from optimised scheduling
- 34% Scrap rate — Reduction from early defect detection
We went from waiting for machines to break to knowing exactly when they need attention. Our output is up, our costs are down, and the team actually trusts the schedule now.