The information presented here has been extracted from a technical article, covering topics such as automation in tire production, published in the specialized magazine Tire Technology International in the October 2025 edition.
The use of Artificial Intelligence (AI) in the tire industry is heralded as a significant breakthrough. These systems are demonstrating rapid deployment capabilities resulting in an overall output increase of between 5% and 7%.
One crucial area of progress is quality-related automation, where AI systems provide critical insights by collecting far more data and measurement points compared to traditional passive monitoring. This is where the efforts of Tekna are highlighted. Tekna components utilize machine learning to efficiently detect defects. Furthermore, their machinery possesses the sophisticated ability to generate specific insights, explaining the exact source of the defect. This powerful capability aids in reducing waste by maintaining the consistency of defect detection. Michele De Stasio, Director at Tekna, elaborates on this vision, stating that the company’s objective is to analyze data to identify precisely where the defect originated during the production process. This visibility not only helps in reducing waste through maintaining consistent defect monitoring but also creates new opportunities for effective maintenance.
In the context of future operational efficiency and maintenance support, Michele De Stasio provides extensive perspectives. De Stasio notes that strategic investment in AI allows companies to achieve operational efficiency and maintain machine uptime by utilizing systems that actively support maintenance. Looking ahead, he anticipates serious staffing challenges, predicting that within the next five years, many companies will struggle to find personnel for routine maintenance duties. He suggests that AI solutions could address this by reserving the necessary skills for operators to complete maintenance on complex machinery.
De Stasio also addresses the dynamic applications of AI, such as generating guidance for operators. He explains that the goal is to provide “route guidance” to the operator, directing them along the shortest path in terms of time, even if the physical distance of the recommended route might be longer. This optimization is achieved by anticipating other movements and potential bottlenecks within the factory. Ultimately, De Stasio expresses his hope that tire manufacturers will continue to invest in AI, believing it will integrate seamlessly with existing automation systems. He forecasts that the widespread implementation of AI will contribute significantly to reducing scrap rates, improving overall quality, and making production far more energy efficient. De Stasio concludes by stating his firm belief that the industry is moving past simple automation, transitioning toward using AI for more sophisticated machine learning tasks.
