Innovation : R&D


A rise in the frequency and intensity of climate-related events is weighing increasingly heavily on economic growth. In fact, according to the United Nations, 9 out of 10 disasters are now related to the climate and this number will continue to increase over the next 20 years. Companies in the agricultural sector are the most exposed, and the notion of risk is an integral part of all their activities. To cope with this, a new type of insurance called “index-based” insurance has been developed.

Unlike traditional agricultural insurance, which evaluates losses according to the probability of damages on the basis of the yield from the previous year, index-based insurance uses meteorological indices such as humidity, rainfall and temperature. Satellite data is collected to anticipate and manage risks. These are the indices that make it possible to establish the risk of the yield and that determine the thresholds above or below which producers will face financial difficulties due to poor production.

The goal of our project is therefore to build a data model based on statistical algorithms that can aggregate different sources related to the climate index (weather stations, operations, customer, yields, mapping, etc.) to arrive at a scoring and precise and relevant predictions to protect farmers.

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In order to support the digitalisation of companies, HR directors must adapt to this revolution to meet the growing needs of employees.

Digitalising the HR process means gaining agility, visibility, and efficiency. It is with this goal in mind that the PREDIRH+ project aims to offer a set of solutions to help improve workforce management.

By cross-referencing all the data from the DSN (Déclaration Sociale Nominative, French social data reporting system) combined with other heterogeneous data (meteorology, environment, geolocation, smartphones, badge readers, equipment, internal company HR files, etc.) we are establishing an Application Suite dedicated to Predictive Monitoring of HR Risks. This Suite enables better understanding of the behaviour of employees within companies and improvement of the management of human capital, talent searches, and employee onboarding, coaching and offboarding.


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Today, most Big Data architectures suggest the deployment of specific tools with high integration complexity, meeting very important functional requirements. As part of an industrial approach, we are led to question the validation and qualification of these tools in an operational context where reliability, maintainability, availability and safety requirements must be justified in advance. The SYNAPSE environment aims, on the one hand, to offer a generic scheduler for Big Data applications developed in heterogeneous environments and, on the other hand, to standardise a functional test management and scalability process. SYNAPSE can therefore be likened to a software workshop for the development, test management and maturation of Big Data & Datascience computer codes.

Sharing Big Data platforms enables homogenisation of the structure of industrial codes and ensures the porting of reusable generic codes, while developing a robustness check of Data Science applications in a production environment.

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In the age of digitalisation, companies are increasingly investing in the latest generation of digital tools (tablets, smartphones, connected objects, etc.), in addition to traditional work tools (computers, monitors, printers, etc.). Thus, with all these available technologies, IT asset management requires more time and effort.

The DSIPIE project offers the opportunity to create useful value by cognitive ingestion of massive flows of diverse and varied data from heterogeneous outsourcing applications, the Internet of Things, social media, meteorology, etc. By combining this data with Machine Learning, we can facilitate IT asset management, automate financial reporting and asset management dashboard development while reducing the impact of the human factor in processes.

DSIPIE therefore aims to design a real-time management software environment in order to understand the uses of digital technology within companies and improve digital management processes with the goal of maximising the return on investment of your digitalisation strategy.

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Nowadays, the expansion of estate agency websites requires the use of digital marketing to develop the sector. In this context, the Immobot project aims to develop a targeted and personalised choice of housing or property intended for real estate professionals, individuals, and private and public organisations.

It is about designing a conversational agent ingesting a large quantity of heterogeneous and multi-source data (websites, estate agencies, notaries, etc.) capable of making intelligent recommendations. These recommendations are based on the intersection of urban and environmental socio-economic criteria with those inherent in the search for property and those relating to network usage habits.

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Digital transformation is at the heart of projects in the railway industry. In the age of Big Data, companies in this sector are counting on connected objects to improve their service offerings, particularly in terms of quality and safety. In fact, a railway company has announced a major project to equip the railway network with sensors and a whole infrastructure enabling it to collect data on the condition of materials travelling by rail, their use, external conditions, and so on.

Using the data collected, statistical models will make it possible to establish predictive maintenance intended to anticipate malfunctions before a breakdown occurs. This use of prediction algorithms will make it possible to reduce breakdowns and delays while significantly reducing equipment and track maintenance costs.

Synergy between the IoT and Big Data is therefore essential to the successful modernisation of the rail industry and to enable industry players to become more efficient in the face of competition.

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