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Track record · IoT · construction

Machine data for failure forecasts and a CO2 footprint

A leading IoT machine-data provider in construction (over 500 employees, 75 million euros in revenue, over 80 percent market share) wanted to turn its data into new services: failure forecasts for rental companies and manufacturers plus a CO2 footprint per deployment.

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The challenge

The problem

Existing machine data was barely monetised. Customers needed interpretation of machine behaviour and utilisation, failure predictions and reliable CO2 figures for ESG requirements.

Analytics

The solution

How it was solved

Failure prediction ML links sensor values, usage data and failure history.
Cause, not symptom Forecasts including root cause prevent consequential damage.
CO2 footprint per deployment Consumption becomes measurable, ESG reporting reliable.
Fleet optimisation Optimise machine usage across construction sites.

The results

18,5 Mio. € additional revenue
3,7 Mio. € savings
> 80 % provider's market share in the segment

Technology

Methods used

Stack

  • IoT
  • Machine Learning
  • Telematics
  • ESG/CO2

The CO2 measurement helps customers meet ESG requirements and verifiably reduce their footprint.

Led by Beyonetix founders and senior engineers. Figures per the respective project report.

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