Industrial AI in Plastics Processing – When Skilled Workers Are in Short Supply
How industrial AI can be integrated into everyday production with Reifenhäuser NEXT
Exclusively for K-Mag
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 shortage of skilled workers.
4 March 2026
Production facilities 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 the example of the Reifenhäuser ‘ ’ NEXT, we demonstrate how industrial AI can be integrated into day-to-day production in a practical way.
What happens when machinery becomes increasingly complex – but there is a shortage of experienced operators?
Production systems in plastics processing are becoming increasingly networked 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, whilst the availability of experienced staff is declining.
In day-to-day production, this manifests itself, amongst other things, in:
- a high degree of reliance on individual specialists
- longer downtimes when problems arise
- fluctuating results during shift changes or staff rotations
- increasing costs for training and induction
Whilst traditional automation can stabilise processes, it does not 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 to scale 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 deliver tangible value in production.
Why Industrial AI should not be equated with Consumer AI
Industrial AI is often 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, the office, creative and knowledge-based work |
| Objectives | Process stability, quality assurance, efficiency, OEE | Information retrieval, text, image or idea generation |
| Data base | 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 |
| Outcome-oriented | Concrete, actionable recommendations | Generic answers or suggestions |
| Requirements | High reliability, traceability and process reliability | Focus on user-friendliness and creativity |
| Role for staff | 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.
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 combines AI-supported assistance systems, digital learning programmes and data-driven applications into an integrated ecosystem.
The aim is to support manufacturers in
- increase productivity, quality and OEE
- reduce reliance on highly qualified staff
The portfolio is divided into three closely interlinked areas:
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 concrete 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
- as well as, 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.
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, which makes content available at any time.
Particularly relevant for internationally operating companies:
- AI-supported training content in over 100 languages
- reduced language barriers in the production environment
- Faster onboarding of new staff
Expertise thus remains within the company – independent of individual employees.
The third component, Next.Data, focuses on the systematic use of production data. Relevant data is aggregated via standardised interfaces and visualised in the software suite Extrusion OS.
For users, this means:
- transparent dashboards instead of isolated data sources
- real-time analyses of process and quality metrics
- automated OEE calculations as a basis for decision-making
Industrial AI ensures 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. Reifenhäuser NEXT’s modular approach 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 safeguarding productivity and quality in the long term.
Is Industrial AI relevant to your business?
Author: Elena Blume | K-Mag
Elena Blume has been writing for various trade 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 facing industry and society.
