From Legacy Systems to Smart Factories: Siemens’ Roadmap for Digital Transformation and the Rise of Intelligent Manufacturing
In today’s rapidly evolving manufacturing landscape, digital transformation has shifted from a strategic option to an existential necessity. Decades-old legacy equipment now operates alongside advanced technologies such as artificial intelligence, digital twins, and industrial IoT systems. The challenge for manufacturers is clear: either learn how to integrate these worlds or risk being left behind.
At Siemens RealizeLive 2025, a flagship event for global manufacturing leaders, Siemens executives shared their perspectives on how to navigate this transition. They explored the key industry trends shaping technology decisions, strategies for connecting brownfield facilities with modern digital tools, and how scalable solutions like digital twins and cloud platforms are enabling companies of all sizes to compete in the next industrial era.
The forces driving today’s manufacturing decisions largely center around product complexity and globalization. Products are increasingly multidisciplinary — combining mechanical, electronic, and software components — from electric vehicles to smart medical devices and next-generation semiconductor equipment. This growing complexity forces manufacturers to rethink efficiency and cost control. At the same time, successful products often need to be rapidly replicated across multiple markets while meeting diverse regulatory requirements. Industries like food and beverage have long operated under this global replication model, but the pace is accelerating in automotive and even semiconductor manufacturing.
The semiconductor sector is a vivid example. Currently, roughly half of the world’s chip production is concentrated in Taiwan. Many companies are now seeking to bring production back to regions like the United States, a move that brings both opportunity and complexity. Building chips requires machines with more than 400,000 parts, and it’s not something AI can simply solve on its own — it still demands skilled professionals, advanced systems, and long-term investments. “AI can help,” one Siemens executive noted, “but it cannot replace expertise.”
The challenge becomes even greater in brownfield factories still dependent on decades-old systems. Some companies rely on software installed in the 1980s or 1990s, stable yet ill-suited for integration with modern digital platforms. One Siemens customer, for example, still operates a 30-year-old quality control system tied to 60 custom-built interfaces. The engineers who originally built those integrations are long gone, making modernization risky because no one knows which interfaces are still mission-critical.
Legacy facilities often face another hurdle: scattered, unstructured data that makes building a digital twin difficult. Siemens is addressing this challenge by developing AI tools that scan entire production floors, identify machine silhouettes, and match them with network-based technical data to generate accurate virtual replicas. This creates a foundation for modernization without disrupting ongoing production.
The digital twin itself is not new, but Siemens emphasizes what it calls the “comprehensive digital twin,” covering the entire product lifecycle rather than modeling isolated elements. A good example is a packaging machine manufacturer redesigning a lettuce packaging unit. Operators previously struggled to clear jams because the access panel was heavy and awkward to open. By using a digital twin to simulate ergonomics and safety scenarios, engineers designed a lightweight, globally deployable access system that improved both usability and safety — without the need for costly, repeated physical prototypes.
This capability extends beyond individual machines. A comprehensive digital twin can simulate entire production environments, including robots, conveyors, and control systems. This enables manufacturers to spot inefficiencies and resolve potential issues before physical changes are made, saving time and resources.
Alongside digital twins, Siemens stresses the importance of the digital thread — a continuous flow of information across design, manufacturing, and service operations. Historically, engineers and production teams often exchanged files and emails, creating delays of up to two weeks for even small design changes. A digital thread eliminates these bottlenecks by automatically flagging tasks, changes, and dependencies for all stakeholders, ensuring no step slips through the cracks. As one Siemens executive put it, “Digital threads are the glue that keeps engineering and manufacturing aligned, reducing risk and accelerating time-to-market.”
When it comes to data and AI, Siemens takes a nuanced view. While many companies believe “more data is better,” Siemens executives argue that success often comes from focusing on key data sets rather than massive repositories. Techniques like retrieval-augmented generation (RAG) allow AI models to perform well with just hundreds of critical data points. Siemens is developing proprietary large language models (LLMs) specifically designed to generate secure 3D representations of machine components for digital twins, protecting intellectual property while accelerating design. “Apply the Pareto principle,” one executive advised. “Twenty percent of your data can deliver 80% of your AI’s value.”
Siemens also recognizes that small and medium-sized enterprises face different challenges from global manufacturers. Its Teamcenter X platform addresses this with tiered cloud offerings — essentials, standard, advanced, and premium — enabling SMEs to start small and scale up as their needs grow. For industries such as machine building, medical devices, battery production, and automotive manufacturing, Siemens offers preconfigured solutions based on decades of best practices with thousands of customers. This approach reduces deployment times and avoids costly custom development.
Beyond technology, Siemens executives stressed the human element of digital transformation. Change management, employee engagement, and process alignment are just as important as software or equipment upgrades. Many brownfield facilities are better served by starting small, targeting key pain points, and expanding gradually. This builds momentum and confidence, reducing the risk of failure.
Ultimately, the journey from legacy systems to smart factories is a process of balancing modernization with existing investments, adopting digital twins and digital threads to streamline operations, and using AI intelligently to enhance productivity while protecting sensitive information. Siemens is not just delivering tools; it is providing a roadmap — one that helps organizations avoid short-term, one-off solutions and instead create future-ready frameworks. Whether it’s a small company taking its first steps into digital manufacturing or a global enterprise redesigning entire production lines, Siemens offers strategies to build more agile, intelligent, and competitive manufacturing systems for the future.