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A line twin that sees a bearing failure a week ahead

An example furniture-component plant in Šiauliai: six machines in 3D, their sensors and the week's production plan. The AI notices the edge bander's bearing has started to wear and proposes swapping it during Thursday's changeover. Pick another slot, compare it with running to failure and change the assumptions: the business case updates.

Rėkyva ComponentsLine twin
  1. 1Monitor
  2. 2AI forecast
  3. 3Plan

This is the line's digital twin: six machines with their status from their own sensors. Click any machine to see its readings.

Week's OEEas planned
78.4 %
Availability
91 %
Performance
89 %
Quality
97 %
Business case

Waiting for the AI forecast

Downtime avoided
–
Saved per incident
–
Per year
–
€310/h · 5×
Week's OEEas planned
78.4 %
Availability
91 %
Performance
89 %
Quality
97 %
Business case

Waiting for the AI forecast

Downtime avoided
–
Saved per incident
–
Per year
–
€310/h · 5×
Tue 13Wed 14Thu 15Fri 16Sat 17Sun 18Mon 19
Line 2
No shiftsNo shiftsKitchen frontsKitchen frontsWardrobe sidesWardrobe sidesDrawer frontsDrawer frontsShelvesBuffer
KB-3
Risk
Machine healthAI watches 38 sensors
HealthTo service

Models trained on 90 days of data

Line 2 · cabinet parts38 sensorsShift 1 · Tuesday 10:20Data updated 4 s ago

This is the line's digital twin: six machines with their status from their own sensors. Click any machine to see its readings.

The plant, its people and its orders are invented. The AI analysis in this example is acted out with prepared data. Prices and hourly rates are typical 2026 values for Lithuanian manufacturers.

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Want a system like this for your business?

We build the line twin, the sensor data collection and the AI failure forecast around your machines, sensors and production plan, and connect it to your ERP and maintenance log. In the first call we look at the data you already have.

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