The beginning of a new era in process control is underway, where cable systems evolve from static predefined logic to smart adaptive layers that overlay physical equipment.
We have entered a new era of industrial performance. For decades, process control has relied on deterministic models, predefined control loops, and human-driven tuning. While effective, this paradigm struggles with the growing complexity of modern production lines.
AI changes the role of data from passive historical data to active intelligence. Instead of manually searching for cause-effect relationships, engineers can now rely on AI to explore vast process datasets and uncover complex, nonlinear, and time-dependent interactions that are difficult for humans to detect. Modern cable manufacturing lines generate enormous amounts of information, including:
When analyzed holistically, these data streams form a digital representation of the production process. AI systems can process this high-dimensional space systematically, identifying patterns, quantifying sensitivities, and detecting structures that define process behavior. This capability lays the foundation for a new generation of intelligent control systems.
The true impact of AI emerges when insight becomes operational. Beyond identifying correlations, AI helps define the structure of process models themselves. Rather than engineers manually prescribing a process control topology, AI can propose architectures that best capture the relationships revealed by the data. These models can then be formalized, validated, and transformed into executable online modules integrated directly into the production environment.
This marks a critical shift: AI is not only analyzing processes—it is helping design the digital components that control them. Process control thus evolves from reactive correction to predictive and adaptive optimization.
A major accelerator of this transformation is the rise of AI agents powered by large language models (LLMs). When connected to coding tools, data analysis environments, and web-search capabilities, these agents can execute complex engineering tasks autonomously.
Once deployed, such AI modules can:
Consider a module combining Topo (Topography Scanner) measurements, MRC (Maillefer Roundness Control) data, and comprehensive line signals. Instead of reacting to threshold violations, it understands multivariate relationships across the entire system. It detects emerging instability patterns, anticipates quality drifts, and provides actionable intelligence—or autonomously intervenes within defined safety limits. This does not replace engineers. Instead, it amplifies their capabilities, allowing them to focus on defining objectives, constraints, and strategic direction while AI handles large-scale exploration and implementation.
AI requires infrastructure. A modern data warehouse is more than storage—it is the execution platform for AI-style software modules. Residing on top of the traditional SCADA process control layer, the data warehouse system enables scalable resources and AI native solutions. By centralizing structured production data and enabling scalable computation on-premises, it provides the foundation for secure and deterministic AI deployment.
The Data warehouse enables:
Running these modules on-premises ensures data confidentiality, industrial cybersecurity, and seamless integration with existing production systems.
When AI modules continuously learn from incoming data, a virtuous cycle emerges: data generates insight, insight improves control, improved control generates better data, and the system continuously refines itself.
This marks the beginning of a new era in process control. Production systems evolve from static, predefined logic to adaptive, software-defined intelligence layers built on top of physical equipment. The factory becomes more than a machine. It becomes a cognitive system - capable of understanding itself, optimizing itself, and continuously expanding the boundaries of performance through AI-aided process control.
Jussi Hanhirova
“AI” Development, Wire & Cable Business Unit
jussi.hanhirova@maillefer.net
Topics: Maillefer, process control, AI