How AI can be integrated into day-to-day production in a practical way with Reifenhäuser NEXT – exclusively for K-Mag Industrial AI in the production environment: digital dashboards such as those used in Reifenhäuser NEXT provide transparency regarding the availability, quality and performance of extrusion lines. Copyright: Reifenhäuser 4 March 2026 Production systems are becoming increasingly complex, whilst the number of skilled workers is dwindling. For many companies in the plastics processing sector, this has long been a reality. This is precisely where Industrial AI comes in: it is becoming increasingly important in industry because it makes knowledge, processes and decisions scalable. Using Reifenhäuser NEXT as an example, we show how Industrial AI can be integrated into day-to-day production in a practical way. What happens when systems become increasingly complex – but there is a shortage of experienced operators? Production systems in the plastics processing industry are becoming more interconnected and data-intensive. At the same time, the shortage of skilled workers is becoming noticeably more acute. Experienced plant operators are hard to find, induction periods are getting longer, and knowledge is being lost due to staff turnover. For many companies, this creates a double pressure: rising demands for productivity, quality and efficiency, coupled with a declining availability of experienced staff. In day-to-day production, this manifests itself, amongst other things, in: high dependence on individual specialists; longer downtimes when problems arise; fluctuating results during shift or staff changes; and increasing costs for training and induction. Whilst traditional automation can stabilise processes, it cannot replace the tacit knowledge of experienced operators. Industrial AI is thus evolving from a future-oriented concept into a concrete solution to a structural problem – not as a replacement for people, but as a tool for scaling knowledge and decision-making in day-to-day production. Analyses, such as the Deloitte Smart Manufacturing Survey 2025, show significant efficiency and production gains through data- and AI-driven technologies (e.g. output and capacity increases of up to 15 per cent) and make it clear that Industrial AI can generate tangible added value in production. Why Industrial AI should not be equated with Consumer AI Industrial AI is frequently compared with well-known AI applications from the consumer sector. However, different requirements and objectives apply to its use in plastics processing: Criterion Industrial AI Consumer AI Context of use Industrial production, machine and process environments Everyday life, office, creative and knowledge-based work Objective Process stability, quality assurance, efficiency, OEE Information retrieval, text, image or idea generation Data basis Machine, process and production data, as well as domain-specific expert knowledge Publicly available, general training data Contextual understanding Knows the plant, material, process parameters and operating states No specific process or machine context Real-time capability Real-time or near-real-time support during ongoing operations No real-time connection to physical processes Nature of results Concrete, actionable recommendations Generic answers or suggestions Requirements High reliability, traceability and process reliability Focus on user-friendliness and creativity Role for employees Decision support and knowledge enhancer Personal assistant or creative tool Typical benefits Fewer downtimes, more consistent quality, more efficient processes; time saved on research and content creation. Marcel Perrevort, Chief Sales Officer of the Reifenhäuser Group, explains how these differences play out in practice in an interview at K 2025: ‘We are integrating new AI features into our plant generations, such as chatbots.’ This enables plant operators to communicate directly with the production line and receive relevant data and information from the process in real time. Marcel Perrevort, Chief Sales Officer at Reifenhäuser Industrial AI in practice: Reifenhäuser NEXT The Reifenhäuser Group demonstrated exactly how Industrial AI can be integrated into day-to-day production at K 2025 with the launch of Reifenhäuser NEXT. The new business division brings together AI-supported assistance systems, digital learning programmes and data-driven applications to form an integrated ecosystem. The aim is to support manufacturers in increasing productivity, quality and OEE, whilst reducing reliance on highly qualified staff. The portfolio is divided into three closely interlinked areas: Next.AI: Assistance systems for day-to-day operations AI-supported assistance systems support operators during day-to-day operations – for example, in the event of malfunctions, quality deviations or process-related queries. Copyright: Reifenhäuser At the heart of Next.AI is an AI chatbot that supports machine operators and service teams directly whilst operations are underway. Instead of spending a long time searching for solutions, staff receive specific recommendations for action in real time – for example, in the event of quality deviations or process-related queries. The AI draws on: Reifenhäuser’s many years of extrusion expertise, technical documentation and, optionally, the user’s live production data. This enables even less experienced staff to achieve consistent results. Downtime is reduced, service call-outs become more efficient, and production processes become more robust. Next.Learning: Securing and scaling knowledge Digital training formats help make specialist knowledge available throughout the company, regardless of experience, language or location. Copyright: Reifenhäuser A shortage of skilled workers also means a loss of knowledge. With Next.Learning, Reifenhäuser specifically addresses this risk. The offering combines on-site training with a digital learning platform that makes content available at any time. Particularly relevant for internationally operating companies: AI-supported training content in over 100 languages reduces language barriers in the production environment and speeds up the onboarding of new staff. Specialist knowledge thus remains within the company – independent of individual employees. Next.Data: Making production data usable Aggregated production data is displayed in clear dashboards and forms the basis for data-driven decisions in plant operations. Copyright: Reifenhäuser The third component, Next.Data, focuses on the systematic use of production data. Relevant data is aggregated via standardised interfaces and visualised in the Extrusion OS software suite. For users, this means: transparent dashboards instead of isolated data sources; real-time analyses of process and quality metrics; and automated OEE calculations as a basis for decision-making. Industrial AI ensures here that data is not merely collected, but becomes actionable. Conclusion: Industrial AI does not replace people – it makes knowledge accessible. Studies already suggest that the targeted use of AI in industrial production offers significant potential. However, seamless integration into existing processes is crucial. The modular approach of Reifenhäuser NEXT enables precisely that. Industrial AI is not a solution to the skills shortage in the sense of staff cuts. It is a tool for democratising knowledge, experience and decision-making – and thus for safeguarding productivity and quality in the long term. Is Industrial AI relevant to your business? This profile was generated by AI. Author: Elena Blume | K-Mag Elena Blume has been writing for various specialist magazines for eight years and has been part of the K-Mag editorial team since 2021. She is particularly interested in topics relating to innovative plastics solutions that contribute to sustainability and address current challenges relevant to industry and society. At a glance: When there is a shortage of skilled workers, knowledge becomes a critical factor in production – particularly with complex systems and intense pressure to improve efficiency. Industrial AI is not a consumer tool, but rather industrial decision support based on process knowledge and production data. Reifenhäuser NEXT demonstrates in a practical way how AI scales knowledge and ensures operational excellence in plastics processing. Key takeaway: Industrial AI does not help the plastics industry to replace people, but rather to scale knowledge, processes and decisions in such a way that productivity and quality remain assured even in the face of a skills shortage. More on digitalisation
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