Scalable data models for interoperability in the system development process

inpro has long supported the development and promotion of industrial data standards that are used at various points along the engineering value chain. As part of the DIAMOND research project, a large and influential consortium is working to enable the end-to-end exchange of engineering data from the planning stage to the commissioning of an industrial plant.

The goal: using digital twins in the plant engineering process

One of the biggest practical challenges in the digitalization of industrial processes in general is still the integration of a large number of different planning and engineering disciplines in a working environment or workflow in which the jointly required data can be processed collaboratively and digitally. Due to different software solutions and data formats, many work steps still have to be carried out manually with a great deal of effort and a high susceptibility to errors. The promotion of neutral data formats and efficient tools for dynamic data transformation is therefore of central importance for industrial progress. The DIAMOND project group is dedicated to creating the necessary conditions to enable the digital planning and further development of production facilities in close cooperation with OEMs.

The solution: A common data model for frictionless data serialization

In the first project phase, the DIAMOND project dedicated itself to the challenging task of developing a Common Data Model (CDM) for the entire plant manufacturing process. To this end, the process sequences and data generated were recorded in full and in detail and described in the CDM. A combination of the Asset Administration Shell (AAS) of the Industrial Digital Twin Association (IDTA) and AutomationML was selected for the serialization of these models in DIAMOND in order to develop the full potential for interoperability in heterogeneous system landscapes. Digital assets are represented and integrated by the AAS with specific AutomationML extensions.

Our focus: component manufacturers

The data mapping of AAS and AutomationML operationalizes the use of the asset administration shell and the exchange of engineering data in the plant design process. With the help of the inpro component editor, component manufacturers can easily create digital twins of their products and automatically integrate them into the planning phase. This closes a key gap in the networked value chain.

An outlook: Merging with other data rooms

By focusing the DIAMOND project on the digitalized plant engineering process, massive economies of scale can be achieved across companies. The operationalization of the data model using tools that take into account the use of different engineering systems is a milestone on the way to the widespread use of digital twins. If the process chain is considered further, e.g. in the direction of production and quality management, supply chain management, after sales and service, new data spaces must be developed structurally and at tool level. International initiatives such as Catena-X, which are dedicated to the development and merging of different data chains in the automotive sector, are great hopes for making the vision of end-to-end data exchange in plant planning a reality.

Further information on data standards in plant engineering can also be found at:

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40 years inpro

15th June of 2023 is the 40th anniversary of inpro's founding. Just as we are proud of our history and the successes of our holistic innovation management, it is important for us to focus on the further development of our ecosystem. Our industry is in a state of upheaval.

DIAMOND: Seamless exchange of engineering data

Scalable data models for interoperability in the system development process

The goal: using digital twins in the plant engineering process

The solution: A common data model for frictionless data serialization

Our focus: component manufacturers

An outlook: Merging with other data rooms

Further information on data standards in plant engineering can also be found at:

Discover more

AI-enhanced weldability testing

AI-enhanced weldability testing

40 years inpro

40 years inpro