A keynote on artificial intelligence designed for industrial organisations: production, maintenance, quality, supply chain. Romain Rissoan shows how AI — from predictive maintenance to digital twins — improves performance, quality and safety.
Relentless cost pressure, ever-stricter quality requirements, shortages of technical skills, the green transition and reindustrialisation: industry faces a convergence of challenges. Artificial intelligence, combined with IoT and the exploitation of operational data, provides concrete answers to each of them. The key is to distinguish uses that genuinely create value from proof-of-concept demonstrations that go nowhere — and to know where to start when your machine fleet is heterogeneous and your data quality is uneven.
This AI keynote dedicated to industry illustrates proven use cases with real-world examples: predictive maintenance, computer-vision quality control, supply-chain optimisation, digital twins. Romain Rissoan also demystifies generative AI for support functions — engineering, procurement, quality, technical documentation — which are often the first to deliver rapid gains without heavy capital investment.
The talk is accessible to industrial management and site directors as well as production and maintenance teams. The objective is to provide a realistic picture of AI deployment in the factory: what is ready today, what requires prerequisites (sensors, data quality), and how to bring teams along rather than unsettling them.
Use cases illustrated from the shop floor to strategic planning.
By continuously analysing machine signals — vibrations, temperature, energy consumption — AI anticipates failures before they shut down the line. Unplanned downtime decreases, equipment lifespan extends, and maintenance shifts from reactive firefighting to intelligent, planned prevention scheduled at precisely the right moment rather than forced on teams in an emergency.
Computer vision automatically detects defects on production lines at a pace and consistency no manual inspection can match. Rejects are identified earlier, quality becomes more reliable and operators are freed from repetitive inspection tasks to focus on root-cause analysis and continuous improvement — where human expertise adds the most value.
AI refines demand forecasting, optimises inventory management and production planning by integrating a wide range of variables — seasonality, supplier disruptions, capacity constraints. The result: fewer stockouts and overstock situations, more realistic production plans and a supply chain that is far more resilient to unexpected events.
A digital twin allows processes, lines and facilities to be simulated and optimised before any physical investment is made. Teams test scenarios, identify bottlenecks and validate layout or configuration choices without taking production assets offline, reducing risk and accelerating decision-making significantly.
Generative AI accelerates technical documentation, procurement, quality management and engineering design: drafting operating procedures, searching standards, comparing quotes, summarising specifications. These use cases require neither IoT nor sensors, yet deliver rapid, tangible gains within the first few weeks of adoption.
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