Context and challenges
Digitalisation of engineering: converging business processes and data models
In industry, understanding and effective use of data are powerful drivers of performance and innovation. This serves to decompartmentalise organisational data, improve data traceability and governance, foster collaboration and increase project performance. This leads to an improved quality of delivery, optimised execution times, anticipated failures, and reduced stoppage times.
By being aware of these issues, we support our customers’ digitalisation processes by combining engineering, data management, and artificial intelligence. What sets us apart is the correlation between our historical knowledge of our sectors of operation and our proficiency in digital technologies.
Structuring, centralising, valorising, and controlling data
Combining engineering and the power of data, our approach is underpinned by digital engineering and industrial data science solutions to effectively address operational performance issues: increasing productivity, harnessing data's potential, optimising production, resolving traceability and data integrity issues, improving quality, reducing execution times, and modelling empirical knowledge.
This approach extends to the entire data value chain for unified, optimised, and controlled asset management. We cover the following activities :
- Data acquisition and structuring
- Data modelling, simulation and optimisation
- Data visualisation
- Data maintenance in operational conditions
For our projects, we use various structured and unstructured database technologies and database connection APIs. These databases, which interact automatically, can be injected into visualisation tools.
Cloud-based technologies provide additional means of data storage and manipulation.
Managing and valorising industrial data
We help our customers acquire and structure their data so that they can use it to best effect. Beyond merely formalising a body of knowledge, our method serves to improve data traceability by moving on from a document-centric approach to a data-centric approach.
We have also developed tools to meet the operational needs of our customers in terms of extracting knowledge:
- DeepFinder: search and retrieve information from all types of documents, structured or otherwise.
More information about DeepFinder
- DeepREXT: automatically extract, classify, and rationalise requirements from a document base.
More information about DeepREXT
- Emoby: create skills maps using artificial intelligence for sourcing purposes (HR).
Industrial data science:
To improve the efficiency of complex projects, we leverage digital technology and draw on our extensive ecosystem of tools to implement methodologies that structure engineering data through modelling. By using models and visualised interactions, this approach improves stakeholders’ understanding and support. Through simulation, these models of real systems create multiple scenarios to aid understanding of the system's operating dynamics and inform decision making.
- BIM: sharing structured information throughout the life cycle of a facility via a digitalised, collaborative repository.
- Optimizio: optimising and improving the reliability of complex schedules to efficiently manage resources and constraints, and support decision making.
Performance measurement (metrics, KPIs, BI)
We support our customers’ use and implementation of Business Intelligence tools to control information in real time, for managing projects, monitoring resources, or pushing forward technical issues: Key Performance Indicators (KPIs), dashboards and project reviews.
Operational maintenance of data
Increasing amounts of data increase its diversity, resulting in fragmented digital assets with high management complexity. Meanwhile, the increasing number of business tools, the specificity of information systems and the co-existence of different layers of data lead to gaps in digital maturity, disparate project dynamics and further digital asset fragmentation. This makes it difficult to ensure that the data is still effectively "usable" and "operable", regardless of the project life cycle phase.
Assystem has developed a service offer which ensures the operational maintenance of industrial data using a method that identifies various data sources and platforms involved in the scope of a project.
Benefits for the customer
Speed up the execution of infrastructure engineering projects in optimal conditions
Develop tailored solutions that consider all regulatory specificities for an improved return on investment
Simulate very complex project execution scenarios before their implementation (such as dynamic scheduling or sequencing of complex tasks)
Collate data to reduce the risk of vast, unusable data lakes
Ensure improved data governance thanks to high-quality data and optimised processes
The digital transition is all about data abundance and diversity. While you have to be able to structure and interpret your data in order to use it for improved industrial performance, you must also be able to listen to the business line and understand the strategic and technical issues in order to propose high-impact digital solutions with considerable added value.