Ziro Data links machines, parts, suppliers and batches in one knowledge graph, so maintenance happens before breakdowns and defects trace back to their source.
An industrial knowledge graph built on your existing ERP, MES and sensor data.
The data problems automotive and manufacturing teams bring to us most. Each links to the service that fixes it.
Equipment wear is missed because sensor anomalies are not analysed, until the whole line stops.
Predictive modelsSupplier quality data never meets robot settings on the assembly line.
BI dashboardsWithout tracing a faulty part to its batch, a defect can mean recalling 100,000 vehicles instead of 1,000.
Data pipelinesIn high-volume production, the smallest unplanned stop or defective part carries heavy costs.
Hours of sudden stoppage × the cost of each lost hour, without predictive maintenance.
Scrapped or reworked units × unit cost, because machines are not tuned from live data.
The extra cost of recalling a whole production run when parts cannot be traced to their batch.
For a vehicle plant or parts supplier, these three leaks add up to
at least $1.2 million a yearlost without a production knowledge graph.
An example from our model, not a guaranteed figure. The audit calculates yours.Set your lines, monthly downtime and the value of an hour of output. The estimate shows yearly savings from predictive maintenance.
Illustrative estimate, not a promise. Assumes predictive maintenance prevents 30% of unplanned downtime. Your real figure comes from the plant audit.
What changes when your data is connected in one knowledge graph. Results marked "Target" are what we aim for with every client.
Ordinary dashboards count events. A knowledge graph connects them, so the system understands why things happen and can act on it.
Machine performance linked to the part or chassis being built
Each part linked to its supplier and batch number
A sensor drifting out of range schedules maintenance and orders the spare part
"Which welding robots caused the 5% rise in chassis scrap last week?"
The graph traces scrap back through machines, settings and supplier batches.Four starting points. Each answers one question your team asks every week, and links to the full service.
IoT, PLC and SCADA data reviewed, quality data matched, and a map of where money leaks.
Answers: Where does our plant lose the most?Data Health CheckAnomaly detection on sensor data, with maintenance scheduled before failure.
Answers: Which machine will fail next?Predictive modelsScrap and rework by line, machine, shift and supplier batch.
Answers: What is driving scrap this week?BI dashboardsEvery part linked to supplier, batch and vehicle.
Answers: Exactly which vehicles need recalling?Data pipelinesStart with a fixed-price audit. Move up only when the numbers justify it.
7-day review of IoT, PLC and SCADA data, quality data matching, and a map of industrial losses.
$3,500Fixed priceStart with the auditERP, MES and IoT in one knowledge graph, predictive maintenance dashboards and part traceability.
Setup + annual licenceLicence by lines or robots coveredAsk about coreAI agents that tune robot settings on live anomalies, manage spare parts and trigger safe stops.
Base + share of savingsPerformance-basedAsk about scaleResults in brackets are filled in once the client approves publishing them.
No new platform to buy. We connect what you have and keep everything in your accounts.