Siemens' Journey from Traditional Manufacturing Systems to Intelligent Factories: Navigating Manufacturers towards the Future of the Industry
Modernizing Legacy Manufacturing Systems: A Case Study by Siemens
Siemens, a global leader in technology, has successfully demonstrated the integration of a 30-year-old legacy system with 60 custom interfaces in one of its customers' quality control systems. This remarkable feat was achieved by implementing a Siemens quality control system nearly three decades ago, and the legacy system still operates today, connecting via these custom interfaces to local systems developed over time [1].
The complexity and risk of upgrading this system originate from the fact that the original developers of these interfaces are no longer with the company, making it challenging to determine which interfaces remain relevant and which connect to critical active systems. This example underscores common challenges faced in manufacturing when integrating legacy systems with modern digital technologies [1].
These challenges include:
- Legacy methodologies resistant to change
- Data and information scattered across multiple homegrown systems requiring detailed attention
- Difficulties in creating a digital twin of brownfield factories due to unstructured and dispersed data
Siemens addresses these challenges by employing AI to analyze factory data, identify equipment and network connections, and model them to build realistic digital twins, facilitating the transition to smart factories [1].
Beyond this specific case, AI-driven integration solutions are increasingly used to modernize legacy manufacturing systems without complete replacement. AI enables predictive maintenance, real-time data analytics, automation of repetitive tasks, and improved operational efficiency while preserving existing infrastructure [2][3]. Cloud providers like AWS also offer tools to connect industrial assets with newer technologies despite protocol incompatibilities and technical debt in legacy equipment [3].
Another example of successful legacy system integration comes from Quinnox, a service provider that modernized outdated legacy platforms in environmental solutions using MuleSoft interface modernization, governance, and monitoring dashboards, resulting in a 99.9% uptime, cost reduction, and a scalable digital platform [4].
In summary, Siemens provides a concrete real-world instance of a large-scale manufacturing legacy system integration involving multiple custom interfaces spanning decades. Similar strategies combining AI, cloud, and modernization frameworks are increasingly adopted industry-wide to bridge legacy and digital technologies effectively.
In the context of modernizing legacy manufacturing systems, finance plays a crucial role in justifying the investment costs associated with integration projects, given the potential return on investment from increased operational efficiency and reduced maintenance costs aided by AI and predictive maintenance [2]. Additionally, the technology sector offers innovative solutions like cloud services from providers such as AWS, which help connect industrial assets to newer technologies despite legacy equipment issues like protocol incompatibilities and technical debt [3].
