Process optimisation with AI: Which method for which task?
With the help of specific approaches, it is now possible for companies to design complex systems more intelligently: From sequential decision-making for robotic systems and the improvement of process parameters in production to process evaluation for quality control and error detection. While traditional methods reach their limits in the face of hundreds of interdependent variables, AI techniques such as reinforcement learning and Bayesian optimisation offer flexible, data-driven solutions.
The new SATW article in the SME magazine is aimed at decision-makers and experts who want to understand how AI can improve industrial processes in concrete terms and which prerequisites are crucial for success. The article was written by Johan Poccard and Dr Iason Kastanis from the Centre Suisse d'Électronique et de Microtechnique CSEM.