Digital & data services - Digital transformation Assystem

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Digitalisation in industrial infrastructures has created new needs and new opportunities. The industrial sector is undergoing fast change and the digitalisation breakthrough is dictated by the massive quantity of data now available. It is referred to industrial Big data, BIM, Digital twin, Data science.

Assystem assists you in your industrial digitalisation to meet your challenges:

Adopting “smarter” work methods to increase productivity and relieve operators
Making use of the potential of data by moving to document-to-data
Optimize production by anticipating breakdowns
Resolve traceability and data integrity issues

our offers

.offers

Digital System Engineering

MBSE Services (Model Based Systems Engineering)
BIM Services
PLM Services
Digital test & commissioning
Digital dismantling

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Industrial Data Science

Master Data Management (MDM)
Robotic Process Automation (RPA)
Engineering search engine
Analytics & Artificial intelligence

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Digital Field Services

Predictive maintenance
Augmented operator
Digital twin

Project Control Solutions

Project delivery model
PMIS (Project Management Information Systems)
Complex planning optimisation

Our projects in digital

Nuclear

Industrial control systems

MOC for instrumentation & control systems of nuclear power plants

Transportation

Digital engineering

Making maintenance audits digital for railway stations

Nuclear

Digital field services

Predictive maintenance for hydraulic rakes in nuclear power plant

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expert talks

Nuclear engineering goes digital

"The current issue is to implement a new, interoperable, pooled data repository in order to ensure better project management and imagine new approaches to operation and maintenance. "

Robert PLANA

Chief Technology Officer

Data science supporting industrial performance

"Good understanding and use of data are excellent levers for performance and innovation in industry... And yet, industrial firms are lagging behind in the use of data science, due to a lack of internal expertise and poor structuring of their data."