Every minute of unplanned downtime costs money. What if you could spot the warning signs days ahead, protect yield, and keep your supply chain moving without drama?
We turn sensor data, PLC signals, SCADA feeds, and production logs into clear decisions. Why wait for a breakdown when the pattern is already there?
Our model watches vibration, temperature, load, and operator events, then flags the assets most likely to fail. That means maintenance happens when it should, not when the line is already down.
Track OEE, scrap rates, and root causes without digging through spreadsheets.
Connect PLC/SCADA, SAP, Oracle, and transport systems into one live operational view.
See what’s likely to be needed before the work order lands. Why scramble for bearings at 2 a.m.?
Cluster tasks, reduce shutdowns, and align interventions with production gaps.
Spot inefficient lines and power-hungry assets before they quietly eat margin.
The preview is built like a control room briefing: big signals first, then the drill-down. How else would a production manager want it?
Hover-ready risk indicators show which machine is drifting, what changed, and how soon action is needed.
Shift, operator, and material-lot views help you isolate the root cause behind low-output spikes.
A dip in yield is rarely random. Temperature, pressure, material lot, machine age, and operator shift can all tell a different story. Ready to see which one matters most?
Clicking a low-yield spike expands the contributing factors.
That’s where the model helps. It catches small pattern shifts before they become scrap, rework, or another emergency callout.
One Yorkshire textile mill used the same approach to identify a misaligned loom and lifted fabric yield by 8%. Could your site do the same?
Maintenance issues rarely stay in one department. They hit inbound materials, inventory planning, and dispatch too. Why not connect the dots before bottlenecks pile up?
Track delivery times, quality rates, and cost pressure across your supplier base.
AI highlights parts that should be replenished now, not after a line stoppage.
Follow material movements so production, warehousing, and logistics stay aligned.
Ageing machinery, unpredictable stoppages, and expensive callouts were eating into output. Sound familiar?
Unplanned stoppages were becoming routine, and the team had no reliable warning system.
We connected IoT sensors and a predictive dashboard that flagged vibration anomalies 72 hours before failure.
The site saved 22 hours every month and cut annual maintenance costs by £45,000.
“The dashboard didn’t just predict failures. It changed how we plan maintenance. We’ve moved from reactive to proactive, and the line feels calmer because of it.”
Tell us about your current maintenance approach, your critical assets, and the pain points you’re seeing. We’ll analyse your top three failure-prone machines and show you what the dashboard can surface. Why delay the first win?