A manifesto for meaningful data for a competitive future
Today, competitiveness depends on the ability to intelligently exploit the data generated by your assets. This manifesto embodies our commitment to transforming this data into sustainable performance drivers.
1. GATHERing 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.
Concrete 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): capture of visual data for in-vivo sexing of duck eggs, with 150 images per egg analysed in 42 seconds.
- Industry 4.0: recovery of multi-source data (CMMS, ERP, MES, sensors, laboratory, supervision) to feed predictive models.
- AI use case in production: collection of data on mixing, forming and drying phases to predict the appearance of quality defects.
2. SEEing is understanding
We provide total transparency for your facilities. With real-time visualisation of energy consumption and space utilisation, you get a clear and immediate view of your operations.
Concrete examples:
- Campus CentraleSupélec: augmented reality glasses and BMS alarms integrated into the digital model for visual data processing.
- Intelligent parking lots: systems capable of counting passengers at the entrance, identifying electric vehicles and encouraging car-pooling with 98% accuracy.
- Equans Hypervision: a web-based platform interconnected with information systems to visualise performance indicators and adjust operations.
3. UNDERSTANDing for better decision-making
We analyse your data to detect operational obstacles, reveal inefficiencies and identify opportunities.
Concrete examples:
- Predictive maintenance at Safran: vibration analysis to anticipate engine failures, with an estimated gain 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 cleansing 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. ACTing for lasting change
We take action by implementing targeted improvement measures, reprogramming systems and optimising operations.
Concrete examples:
- Energy optimisation at CentraleSupélec: reduction of 14% in HVAC consumption, 28% in AHUs, and 30% in hot and chilled water production, with an ROI in 1 year.
- Industrial inspection: 15-30% reduction in scrap rates thanks to automated detection of defects on production lines.
- Sustainable mobility: prediction of bus autonomy to optimise routes.
- AI use case in production: online prediction of quality defects and explanability of causes to reduce waste
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