Operation Interoperability

 In her guest article, she shares insights from industrial practice.

The transition to a circular economy presents industry with a fundamental challenge: Sustainability must become measurable, controllable, and operationalizable across complex value chains. Individual optimization measures alone are not going to suffice to achieve this. What is crucial is the ability to systematically collect and share relevant data and place it in a robust context.

However, the industrial practice shows that this is precisely where key hurdles lie. Data is often fragmented, non-interoperable, or only available to a limited extent. At the same time, requirements are increasing, for example due to regulatory mandates or new expectations regarding transparency and circularity. Without a consistent data foundation, neither well-founded sustainability assessments and data-driven optimizations can be implemented, nor can products or components be tracked and evaluated to determine which circular strategy will allow them to remain in the value chain.

Against this backdrop, industrial data ecosystems are gaining importance. Initiatives such as Manufacturing-X aim to establish cross-company and cross-industry data spaces based on common standards and clear governance structures. These enable data to be made available along the entire value chain and translated into a common context. This in turn creates the foundation for consistently calculating sustainability indicators, analyzing processes, and systematically deriving improvements.

A central component here is interoperability. Only when data is described in a standardized manner and can be interpreted unambiguously can it be used efficiently between different actors and systems. At the same time, this structure is a prerequisite for the use of artificial intelligence in industry. Industrial AI can realize its full potential particularly where large, high-quality, and contextualized data sets are available. It enables the analysis of complex relationships, the creation of forecasts, and data-driven support for decision-making processes.

However, translating these approaches into industrial practice requires more than just technological solutions. Small and medium-sized enterprises, in particular, often face challenges when getting started. This can be due to limited resources, a lack of guidance, or uncertainties regarding benefits and implementation. Transfer initiatives such as SCALE-MX address precisely this by specifying use cases, making best practices accessible, and promoting structured exchange between companies. 

A clear example of this is the digital product passport. It aims to make product-related information available throughout the entire life cycle. This creates a foundation for improved transparency, more efficient take-back and recycling processes, and new data-driven business models. At the same time, it once again becomes evident that added value is only created when data is standardized, interoperable, and accessible along the value chain. The sustainable transformation of industry is thus closely linked to the development of high-performance data infrastructures. It requires common standards, reliable governance models, and a willingness to collaborate across corporate boundaries. Only through the interplay of these factors will it be possible not only to demand sustainability but also to systematically implement and continuously improve it. 

earned her doctorate at the Technische Universität Berlin in the field of quality assurance in additive manufacturing using artificial intelligence. Previously, she was a research associate at Fraunhofer IPK and at TU Berlin, where she focused on data-driven production, process analysis, and industrial AI.
 
Today, she heads the »Industrial AI & Data Economy« platform at ZVEI e.V. In this role, she is responsible for the SCALE-MX transfer initiative and the DPP4.0 forum, among other things, and works on the implementation of data eco-systems, interoperability standards, and data-driven business models.