Predictive maintenance for a UK precision manufacturer
A precision engineering firm with 120 staff was losing production time to unplanned machine stoppages. We deployed shop-floor sensors, built predictive maintenance models and integrated alerts with their existing maintenance schedule — cutting downtime by 62%.
Project Overview
Industry
Manufacturing
Duration
18 weeks
Team Size
4 specialists
Client context
The client operates CNC machining and assembly lines supplying automotive and industrial customers. Maintenance had been largely reactive: engineers fixed equipment after failure, with preventive servicing scheduled on fixed calendars that did not reflect actual machine condition. Unplanned stoppages averaged 15 hours per week across three critical lines, affecting delivery promises and overtime costs.
The Challenge
Shop-floor data was fragmented — some machines had basic PLC logs, others had none. The maintenance team was skilled but small, and leadership was sceptical that predictive analytics would work without a large IT department. They needed a pilot that proved value on one production line before scaling, with minimal disruption to ISO-certified processes.
Our Solution
We instrumented priority assets with vibration and temperature sensors, aggregated readings into a cloud monitoring layer and trained anomaly models on historical failure patterns. Maintenance engineers received early warnings through their existing workflow tool, with recommended actions ranked by urgency and estimated impact on output.
Our approach
- 1
Walked the production floor with maintenance leads to identify the three assets with the highest downtime cost and feasible sensor placement.
- 2
Installed industrial IoT sensors and edge gateways, connecting to a secure cloud platform without exposing operational technology to the corporate LAN.
- 3
Collected six weeks of baseline data before enabling predictive alerts to avoid false positives from normal production variation.
- 4
Worked with engineers to label past failure events and tune models for the specific vibration signatures of each machine type.
- 5
Integrated alerts into the team's maintenance scheduling tool with clear severity levels and suggested inspection checklists.
- 6
Ran a three-month pilot on one line, documented ROI assumptions and created a rollout plan for remaining assets.
Downtime
Maintenance costs
Production capacity
Results achieved
- Reduced unplanned equipment downtime from 15 hours to 5.7 hours per week on the pilot production line
- Cut overall maintenance spend by 35% by shifting from emergency call-outs to planned interventions
- Increased effective production capacity by 15% without capital expenditure on new machinery
- Gave operations leadership a dashboard linking machine health to order fulfilment risk
Technologies Used
Project Timeline
IoT Deployment
Installed sensors on priority CNC and assembly assets, configured edge gateways and validated data quality on the shop floor.
AI Model Training
Collected baseline readings, labelled historical failures and tuned anomaly thresholds with maintenance engineers.
System Integration
Connected alerts to maintenance scheduling, trained shift leads and measured downtime against pre-pilot baselines.
Key takeaways for SMEs
- Start predictive maintenance on one high-cost line — proof beats theory on the shop floor.
- Maintenance teams must co-design alerts; models only work if engineers trust and act on them.
- Baseline data collection is not optional — rushing to predictions creates noisy, ignored warnings.
Related service
This case study reflects the kind of outcomes we pursue through our ai & automation work for UK SMEs.
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