Every machine in your fleet, on one screen.
Fleet Performance turns raw machine telemetry into a live health dashboard for your service and engineering teams. Output, OEE, downtime and early warnings across every customer site, built on the connectivity you already have.
Built on IXON, MQTT and OPC-UA · structured on Azure · delivered in Power BI.
See it in action.
An interactive Fleet Health dashboard, running on sample data. The same layout runs on your machines, live.
This is one example view, on synthetic data. On the same governed foundation we shape the screens to your machines, your KPIs and your roles, so a different layout is a configuration, not a rebuild.
You're looking at one view of it.
Everything here runs on the same governed data foundation. Build the views you need now, and the ones you need later, on top of it.
Fleet Performance
OEE, alarms and uptime for your team
Customer Service Portal
White-label, for your customers
Service Intelligence
Wear parts, stock & reorder
Your own view
Shaped to your KPIs, no rebuild
Built once, shaped to you. Because every view runs on one governed foundation, a different view, or a new one next year, is a configuration, not a rebuild.
Everything your team needs, on one screen.
At a glance: what's inside the Fleet Performance dashboard, and what it means for your day.
Fleet KPIs at a glance
Active errors, early warnings, fleet OEE, machines running and MTBF, in one header that updates through the shift.
Prioritised active issues
Every open fault and warning, ranked by urgency, with the affected machine, site and the recommended next action attached.
Early warnings with ETA
Trend signals like vacuum drift, motor-current rise and cycle-time creep, each with an estimated time-to-threshold.
OEE & production
Availability, performance and quality, plus output and rejects, per machine and per line, against design targets.
Fleet map across sites
Every connected machine and customer site on one live map, colour-coded by status, from one country to forty.
Reliability & history
MTBF, alarm history and recurring-fault patterns over time, per machine, component and machine type.
How Fleet Performance works.
One reusable path from machine signal to dashboard. Built once, then applied across every machine and customer site.
Built once. Every new machine and customer site inherits the same model, no rebuild.
The people behind the dashboards.
StriData was built by industrial data practitioners who got tired of two things: dashboards no one acts on, and machine data that stays stuck in the gateway. We work where operational reality meets business outcomes, one foot in IT and one on the floor.
We build the foundation and the analytics on top of what you already run. We are honest about scope, and we say no to work we can't do well.
“A dashboard only matters if someone acts on it. That is the bar we hold ourselves to.”
Questions teams ask us.
What if this view isn't exactly what we need?
That's the point. What you see is one example. Because it runs on a governed data foundation, a different view is a configuration, not a rebuild: we reshape the KPIs, layout and screens to your machines and your roles without touching the plumbing underneath. The modules show what's possible; your version is shaped in the Quick Scan.
What data source does it run on?
Machine telemetry, states and alarms. IXON is our primary supported platform and the fastest path; MQTT, OPC-UA and API-based setups are supported through a scoping engagement. We add the analytics layer on top, we do not reconfigure your routers or device templates.
Is the dashboard real-time?
Near real-time. Data is loaded incrementally (typically every 15 minutes for the pilot, tunable per setup). Live machine state, active errors and the day's production update through the shift.
Do you build the dashboards, or can our team?
Both. We deliver the Fleet Performance dashboards on the structured foundation, and because that foundation is open (a governed SQL layer), your own BI team can build directly on it too. No lock-in.
How long until we see a working dashboard?
After a Quick Scan and a short tag-mapping workshop, a first working dashboard on a pilot machine set typically lands in a few weeks. New machines and sites then inherit the same model.
What about the early-warning signals, is that AI?
No black box. The early warnings are transparent, trend-based rules on real signals (for example vacuum pressure drift or cycle-time creep) with an estimated time-to-threshold. An engineer always validates before acting.