To the digital twins of its factories, Renault has added real-time data collection and recovery. A platform was then created to manage all this information and create different use cases.
The automaker is working to digitize all of its processes, from employee training to delivering cars to customers and streamlining design and manufacturing processes. After years of focusing on structuring data, the manufacturer was able to expand by internally building a platform based on a single data standard. Let’s return to the digital transformation of the French manufacturer.
Internal solution offered by Atos
Between 2016 and 2018, 120 million euros were invested, among others, in the network at the plants. Then, between 2019 and 2021, €270 million was used to create digital twins of factories and supply chains, as well as partnering with French startup Shippeo to improve supply chain transparency or Google as a cloud platform provider.
During this period of data collection, the automaker explains that it did not find a data management and visualization solution that matched its request, since most of the proposals concerned only certain segments. But Renault was looking for full visibility, hence the in-house development of this platform.
The collected data began to be used when the IDM4.0 platform was created between 2021 and 2022, as well as a “central tower” offering a centralized view of all information. During this period, the manufacturer has invested 390 million euros. The employee explains that Renault then moved “from a static digital twin to a real-time projection and interaction capability.” At the same time, he also asked Atos to monetize his industrial know-how. The solution has been on sale since June 2022 under the name ID@Scale. Atos claims to be in early negotiations with 5 customers and has signed a contract with the first customer in the manufacturing industry.
The IDM4.0 platform integrates four dimensions that are considered important: the connection of teams and machines, the digital twin of facilities and supply chains, the connection with the ecosystem outside the factory walls, the technological dimension that “integrates above data”.
Communication of commands and machines
In figures, Renault says: 8,500 pieces of equipment are connected in factories, 600 pieces of equipment distributed in about twenty factories are equipped with vibration sensors, about sixty production processes are simulated (screwing, machining, welding, stamping, filling, etc.), there are 4,400 digital workstations (DWS) or connected workers. All this allows you to collect a billion data sets per day.
For example, on the Maubeuge website, you can view all the objects of the Kangoo production process: from a screwdriver to an operator on duty. The people present on the production lines are connected operators: at the beginning of their shift, they must confirm that they are actually present, that they are restoring the station to good condition, that all tools are in place, etc. In the event of a problem, information is sent directly to the senior operator, who is alerted and can resolve the issue. In parallel, the team leader receives the same alert, which turns green when the issue is resolved. “Renault has 4,000 tablets and 3,000 networked team leaders,” says one employee.
The entire Maubeuge production line is visible.
Aside from the augmented employees, the robots are covered in sensors and send back a lot of data – more than 70% of industrial equipment is connected – allowing many problems to be detected. For example, you can find out when the electrode of a welding robot sticks. This is due to robot misalignment, which is easy to correct without stopping the production line.
The contribution of digital twins
Renault has created digital twins not only of its factories, but also of its supply chains. Avatars represent each object and different product flows can be analyzed. Based on these digital twins, the manufacturer was also able to develop about fifteen virtual reality training courses, including one for Mégane E-Tech painting. The manufacturer relied on a 3D model developed in-house.
With this training, painters can learn the correct gesture to optimize paint consumption when painting at best. Once the painting module is finished, the software returns to the signal when there is too much or too little paint on the body. Renault guarantees that thanks to virtual reality, people will learn four times faster. “The manufacturer has at least two VR headsets across its 35 locations,” said the VR training manager. “Renault has trained 70% of its painters in France on this device,” she adds.
You can draw in virtual reality.
Digital twins and virtual reality tools are also being used to explore the best ways to organize work areas, test the ergonomics of each workstation, and ensure that all tasks are completed as efficiently as possible.
Connecting to the ecosystem outside the factory
With the help of the IDM4.0 platform, the manufacturer extracts and collects information far beyond its factories. From the supply chain side, the goal is to plan production as best as possible to maximize profits. To do this, it is necessary to automate the recovery of data from suppliers, as well as data on the transportation of goods.
When there is a problem with the supply, it is immediately reported to revise the production plan. Crisis scenarios are offered and the operator can make the final decision. Renault says it wants to continue developing algorithms to improve and deepen its predictive tools and prescriptions on these issues. In this regard, one of the tasks is to optimize the routes of trucks, as well as their loading, which will reduce stocks.
The fourth element that Renault proposed for its platform is the technology dimension, which is placed above data. “AI and machine learning models can further empower the platform,” the employee said. At this level, the manufacturer seeks to take advantage of the cloud and works with Google on specific topics.
Renault says its platform makes it easier to deploy AI models. For example, the artificial intelligence system helps operators to visually inspect tires: “the system prompts, predicts or warns, and the operator decides,” the employee sums up. The software, developed in collaboration with startup Buawei, detects anomalies in tires using images. Whether it’s a tire in bad condition or mounted on the wrong side, a problem with matching screws, or the fact that two different tires have been repaired, the software will alert you to a detected problem.
Once this type of solution is developed on the site, it is passed by the manufacturer to the algorithm library so that it can be used by other factories.
With this IDM4.0 platform, Renault wants to reduce its costs, increase transparency, improve prevention and forecasting, reduce the time between orders and delivery, etc. To date, he says that this platform has saved him 80 million euros, in particular, thanks to 300 vibration alerts preventing it from stopping production, improving spot welding quality, visual inspection of the chassis, or follow-up. cars from prototype to series. But he hopes to save up to 320 million euros by 2025. Meanwhile, Atos is trying to bring this solution to market. Could the success claimed by Renault be replicated by other manufacturers?
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