The industrial metaverse, a real lever for transforming our industries

by David Maurange - Head of Digital Interaction, Sopra Steria Next
| minute read

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Published in Décideurs Magazine on February 13th 2024 

Traditional production optimisation methods have reached their limits. The complete digitalisation of industry means that new approaches can be tested virtually, and disruptive hypotheses can be simulated before they are applied in the real world. In this way, we can implement actions without causing any damage. Our manufacturers can review all their practices at a time when resilience, carbon impact and competition are inevitably driving local re-industrialisation. We need to take advantage of this momentum to harness the full power of new technologies and smart industry, of which the industrial meta-version is a lever. 

The digital twin is the first building block in the industrial metaverse. When it is connected in real time to the production line or to the physical product. When combined with the collaboration of different professions and the power of artificial intelligence and generative AI, this tool speeds up product design, simulates different configurations, anticipates changes in production lines and shortens production cycles. Its photo-realistic visualisation (High Quality Rendering) in augmented or virtual reality now makes virtual simulation tangible. We now have a time machine that can find the root cause of an incident in the past, simulate a change in the future or even take on the role of a control tower in the present. Going beyond the limits of the organisation and cycles to maximise operational efficiency is not simply a matter of setting up a control tower. 

To achieve this, it is essential to adopt a collaborative and interoperable approach, opening it up to all the company's stakeholders - subcontractors, suppliers, partners and even customers - but also to the various internal business lines, even if this means breaking down many silos. 

“The potential gains in this fast-growing segment are considerable” 

This cross-functional collaboration will reduce design, product manufacturing, after-sales and even marketing cycles to accelerate time to market. This complementarity means that a single tool can analyse the various components of the industrial system: 

  • Simulate the behaviour and development of production equipment: corrosion, obsolescence, changes in environmental conditions, etc. ; 
  • Plan major projects to extend the operating life of facilities or work on processes to improve operational efficiency; 
  • But also forecasting and measuring the impact of the first on the second and vice versa. It also means forecasting and measuring the impact of the former on the latter, and vice versa. This means 'agilising' processes to enable real-time collaboration between the various business lines, as has already been done in IT, by creating the necessary tools. The industrial metaverse is now becoming the industry's DevOps platform. 

Traceability, value sharing and control of digital assets 

The ecosystemic vision of industrial metavers makes it possible to share company data securely and traceably to facilitate product design, production and maintenance. For manufacturers, the development of metavers offers a genuine platform for exchange and collaboration to improve operational efficiency. This collaborative space also responds to the challenges of traceability and sharing of value between stakeholders by enhancing, and even monetising, the use of each party's digital assets and content. A subcontractor or supplier can, for example, integrate the 3D models of its spare parts into its customer's repository and be paid for each use. 

Promoting circularity and sustainability by capitalising on what already exists

At a time when 78% of French people are committed to more responsible consumption, optimising re-use and retrofit for the design of new products or catalogue rationalisation is made easier by the use of AI and generative AI fed by the company's assets (3D models of existing products, design studies and prototypes). Aggregating the data collected thanks to the OT/IT connectivity of the production lines with the 3D data of the products and production environments will feed the simulation models boosted by AI. The aim is to reduce energy and materials consumption by :

  • Training robots to limit gestures and movements; 
  • Reconfiguring the factory to make it more efficient; 
  • Providing more sustainable working postures and activities for operators. 

Virtual realism and the use of AI/GenAI to rationalise costs 

The realism of the virtual world makes it possible to engage all those involved. Operators can access their future virtual working environment even before it exists, so they can train themselves and maximise their productivity. This realism also enables AI to be trained in simulated hypothetical situations, generating synthetic data that can be used to improve robotics, autonomous driving (not just for cars), and plant supervision and safety. Mastery of this digital heritage, combined with advances in AI or generative AI models, also makes it possible to envisage the industrialisation of content production chains. It is now possible to generate marketing media directly from 3D design models, adapting their composition according to customer segmentation, brand and location. 

Reducing risk through simulation across the value chain 

The time machine and the use of "What-If scenarios" are used to build a more sustainable and resilient supply chain by integrating the analysis of exogenous weak signals to respond to changes in the geopolitical context. But these scenarios must involve the entire value chain: 

  • Working on a change in product design by replacing a component that has broken down; 
  • Simulate the impact of this change on manufacturing to ensure that we are able to produce while limiting the impact on the production chain; 
  • Study the impact on product sustainability; 
  • Consolidate the production metrics for these different design variants for the benchmarkers; 
  • And finally, storing the variant in the digital twin of the article to ensure traceability and maintenance, if this scenario is adopted and applied in real life. 

These test & learn approaches in the virtual world will contribute to the continuous improvement of the real world. The aim here is to maximise the time spent in the virtual world in order to achieve the objective on the first attempt in the real world. Some manufacturers are already applying these principles by validating and optimising all the processes of a factory in the virtual world before even launching its construction. 

“The aim is to maximise the time spent in the virtual environment in order to achieve the objective on the first attempt in the real world.” 

In conclusion, the opportunities offered by the industrial metaverse are numerous. In a tense economic and geopolitical context, it provides new solutions and complements existing approaches. Each stage of the prioritised pathway must respond to the company's strategic challenges if it is to derive tangible benefits. The implementation of the first technological building blocks needs to be initiated now, so that we can then refine the use cases, fully commit to the industrial transformation and build up the digital assets to feed future AI models, which will themselves generate new opportunities. The first stages of the journey towards the industrial metaverse must begin now, to transform our industry into a smart industry. 


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