Revealing the value of useful data to accelerate your performance
Data is at the heart of the strategies of the most successful companies. It's just a question of knowing how to activate it effectively.
Our approach reflects our commitment to harnessing every useful piece of data to create measurable value. With a data-centric approach tailored to your specific challenges, we reveal their potential to build sustainable performance.
1. GATHER - to reveal potential
The first step towards a competitive future starts with collecting data from your installations. Every sensor, every system, every building generates valuable information.
Practical examples:
- CentraleSupélec: creation of a digital twin of the building to create a quality data repository, the basis for energy optimisation
- SOA (Sud-Ouest Accouvage): visual data capture for in-vivo sexing of duck eggs, with 150 images per egg analysed in 42 seconds.
- Industrie 4.0: multi-source data retrieval (CMMS, ERP, MES, sensors, laboratory, supervision) to feed predictive models.
- AI use case in production: data collection on mixing, forming and drying phases to predict the appearance of quality defects.
2. VIEW - to reveal current status
We offer you total transparency over your facilities. With real-time visualization of energy consumption and space utilisation, you get a clear and immediate view of your operations.
Practical examples:
- Campus CentraleSupélec: augmented reality goggles and BMS alarms integrated into the digital model for visual data exploitation.
- Smart car parks: systems capable of counting passengers at the entrance, identifying electric vehicles and encouraging carpooling with 98% accuracy.
- Hypervision Equans: web platform interconnected with information systems to visualise performance indicators and adjust operations.
3. UNDERSTAND - to make better decisions
We analyse your data to detect operational obstacles, reveal inefficiencies and identify opportunities.
Practical examples:
- Predictive maintenance at Safran: vibration analysis to anticipate engine failures, with an estimated saving of 30 k€/year.
Quality control at IN Groupe: detection of invisible defects on passport holograms thanks to artificial vision.Data preparation: 80% of data scientists' time is devoted to understanding and cleaning data to guarantee its usability.
- Exploratory Analysis (EDA): standardisation and automation of statistical analysis to qualify data before modeling.
- Machine Learning: behavioral segmentation, detection of weak signals, simulation of scenarios to optimise industrial processes.
4. ACT - for lasting change
We take action by implementing targeted improvement measures, reprogramming systems and optimising operations.
Concrete examples:
- Energy optimisation at CentraleSupélec: 14% reduction in HVAC consumption, 28% on AHUs, and 30% on hot and chilled water production, with an ROI in 1 year.
- Industrial inspection: 15-30% reduction in scrap rates thanks to automated defect detection on production lines.
- Sustainable mobility: prediction of bus autonomy levels to optimise routes.
- AI use cases in production: online prediction of quality defects and explicability of causes to reduce losses
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Measurable results for tangible impact
80%
reduction in time spent looking for a parking space thanks to intelligent parking
ROI < 1 year
for certain AI bricks applied to energy performance or industrial quality