The Future of Manufacturing: Complexity, Globalization, and the AI Advantage
In a recent Automation World feature, Siemens Zvi Feuer, Senior Vice President of Digital Manufacturing Software and CEO of Siemens Digital Industries Software in Israel, shared his expert perspective on the forces shaping modern manufacturing — and the practical steps manufacturers need to take to move from legacy operations to smart, connected factories.
In the article, Feuer explores the most significant digital trends transforming manufacturing automation today. He explains how the growing complexity of products, the globalization of production, and the accelerating impact of artificial intelligence (AI) are redefining how manufacturers approach their operations — and how they make critical decisions about modernization.
At the same time, globalization is pushing manufacturers to replicate successful production models across multiple regions with minimal disruption. “When a product becomes a big winner in one country, they want to capture the process and duplicate it elsewhere quickly and efficiently,” he says.
“Products today are more multidisciplinary and complex than ever,” Feuer explains. “This complexity requires simplification of the production process. Companies must find ways to do the work more efficiently and at less cost.”
Turning Legacy Systems into Future Assets
The reality for most manufacturers is that they are not starting from scratch. Many operate in brownfield facilities filled with decades-old equipment and custom processes. Feuer points to three major challenges in these environments. First, legacy methodologies are deeply ingrained and resistant to change. Second, critical data is often scattered across homegrown systems. “We have customers who installed Siemens quality control systems nearly 30 years ago. Those systems are still working — but now they have 60 interfaces to locally developed systems, and the people who built those interfaces are no longer there,” he says. This is why transitioning to a modern quality system is so important. By carefully evaluating which interfaces remain relevant, manufacturers can streamline operations, strengthen connectivity, and position their systems for a more agile, future-ready environment.
Digital Twins and AI: Building the Bridge Between Old and New
The third challenge Feuer points out is creating an accurate digital twin of a brownfield factory. This requires capturing vast amounts of unstructured data from the facility. Siemens is addressing this with AI agents that scan the factory, identify the silhouettes of machines, and match them to equipment data to automatically create realistic digital models. “The challenge isn’t just connecting new systems to old — it’s understanding what you have, how it’s connected, and capturing that in a way that creates value,” Feuer explains. AI, he adds, is becoming a powerful enabler in this process — but it must be applied strategically.
Today, we can teach an AI large language model (LLM) to work even better with just 200 pieces of information.” The key is being strategic about the data we choose. By using retrieval-augmented generation (RAG), Feurer explains how we can enrich those inputs and feed them into an AI model in a way that protects intellectual property, while enabling AI to generate accurate 3D models for use in digital twins.
Balancing AI and Expertise: Why People Remain Central to Transformation
Even with advanced automation and AI, human expertise remains essential. Feuer points to semiconductor manufacturing as a prime example: “Chip production is a super-complex domain with machines that have more than 400,000 parts. AI can help, but it’s not going to replace the need for experts here.”
For Feuer, the path forward is clear: transitioning to smart manufacturing isn’t about discarding what exists, it’s about integrating and enhancing it. “You need sophisticated systems, you need investments, and you need good people,” he says. With Siemens’ digital twin technology and digital manufacturing software enriched with cloud and AI technologies, manufacturers can modernize brownfield operations, scale globally, and prepare for the future of production.
