The dark factory is increasingly being discussed as the target vision for fully autonomous production. In this future scenario, machines, equipment and software systems make many decisions independently and control production processes largely autonomously.
“However, this is less a short-term end state than a long-term maturity journey. In highly standardized manufacturing environments, autonomous processes are increasingly becoming reality. In many industrial companies, however, variant diversity, small batch sizes and short-notice changes continue to shape day-to-day production. In these settings, the focus is not on factories without people, but on intelligent collaboration between humans, production systems and AI,” says Martin Heinz, CEO of iTAC Software AG.
As a result, people are increasingly taking on controlling, monitoring and strategic tasks, while AI supports them with analysis, root cause investigation and decision-making.
AI needs production context
For AI to reliably analyze production processes, identify correlations and derive sound recommendations for action, it needs far more than large volumes of data. What matters is production context: Which machine processed which product? Which materials were used? Which process parameters applied? Which quality specifications must be met? Only this context enables robust analyses and well-founded decisions.
This is precisely where the Manufacturing Execution System plays a central role today and will continue to do so in the future. It controls and documents production workflows, connects machines, materials, employees and processes, and thereby creates the production context that analytics and AI require for reliable evaluations and informed decisions.
The future of production therefore does not lie in replacing MES with AI, but in the intelligent interplay between both worlds. While MES controls and safeguards production processes, analytics solutions analyze production and machine data in near real time and identify optimization potential. AI uses these insights to provide root cause analyses, recommendations for action and assistance functions. Together, MES, analytics and AI create the conditions for increasingly autonomous production.
Start with concrete use cases
Many companies still approach their AI initiatives from a technology-first perspective. According to Martin Heinz, a different approach is more successful: “The starting point should not be the question ‘Where can we use AI?’ but rather ‘Which decision do we want to improve?’ Typical use cases include root cause analysis for quality issues, the optimization of setup times or supporting employees in quickly accessing production knowledge.”
Companies that already use a modern MOM or MES solution can bring such applications into productive use much faster, because the required process and production data is already available.
With solutions such as the iTAC.MOM.Suite manufacturing management system and the iTAC.CATi AI platform, iTAC follows exactly this approach. The aim is to bring production knowledge, real-time data and artificial intelligence together in a shared context. This gives rise to intelligent assistance systems that support employees while paving the way toward increasingly autonomous production. The dark factory can thus become the logical next step in the evolution of digitally connected manufacturing.
