An “Aerospace-X” consortium of 14 companies and research institutions was formed under the leadership of Airbus. The main objectives of this project are to create a digital ecosystem for the aviation and aerospace sector, to ensure digital continuity and to enable new forms of collaborative, data-based value creation.

Due to the manufacturing chains for fuselage, cabins and engines, development is focused on fostering the necessary ability to work together digitally across businesses, especially with a view to enabling technologies, data infrastructure, software services and regulatory aspects. Focus is also placed on the use cases of demand and capacity management, CO2 emissions and carbon footprint regulations, the circular economy and collaborative quality management.

DAVID is a cross-sector project in the Manufacturing-X (MX) space, and works to ensure digital interoperability on the basis of Asset Administration Shells (AASs). The project contributes to the creation of open data ecosystems for industry by making processes and resources available to validate the technological and semantic interoperability of MX projects. This would facilitate use across multiple sectors.

The joint projects from the funding measure also include “KoPa 35c Modul a2 - Digitalisation for vehicle manufacturers and supplier industry”, which are considered to be a part of Project DAVID.

MX projects within Project DAVID receive support with content and methodology, especially as pertains to the use of AASs. The project also coordinates the use of AASs within Manufacturing-X, validates interim project results and ensures these are standardised. Finally, the project is developing an SME “adapter” in order to make the Industrie 4.0 data space accessible to SMEs.

The Decide4ECO plan is concerned with the consideration and assessment of sustainability requirements and the achievement of sustainability goals. As a majority of resource consumption is predetermined in product planning and development, the project is looking deeper into this stage and developing digital solutions to support decision-making in development and manufacturing with a view to sustainability criteria. The assessment is intended to consider the entire value chain and the full product lifecycle.

The data on which the sustainability analyses are based must be collected along the entire manufacturing and supply chain. The data exchange necessary to facilitate this should be implemented in accordance with the collective standards of the Manufacturing-X data ecosystem, whereby the project is developing open interfaces and standards. These entail the inclusion of a standardised Digital Product Passport (DPP) completed with necessary information in the collective data ecosystem. The software solutions that build on this ecosystem should be able to consider all relevant up- and downstream processes in their sustainability assessment, and should also be able to incorporate predictive AI-based impact analyses.

Under the leadership of Siemens and SAP, 47 partners are working together within the Factory-X flagship project. The primary goal is the creation of an open and collaborative data ecosystem for factory equipment suppliers and operators on the basis of Catena-X and concepts from Plattform Industrie 4.0. The project should span eleven data ecosystem use cases of particularly high relevance for the manufacturing industry. The following topics are receiving special consideration:

• Data consistency across manufacturers for engineering, device information and condition monitoring
• Contribution to sustainability via carbon footprint and energy management, as well as digital solutions to support the circular economy
• Provision of digital solutions for “as-a-service” business models (e.g. marketplaces, pay-per-part, remote control/monitoring)
• Traceability of materials, data and products along the entire supply chain
• Updates and change management for devices in the field

Alongside the technical development, the consortium will also play an important role in the overarching coordination of development activities across products and the establishment of an international Manufacturing-X community.

HealthTrack-X aims to create an open and decentralised data ecosystem for the healthcare industry. All involved parties should thus be able to more easily maintain their data sovereignty, forging trust and increasing efficiency. Use of the data ecosystem should foster digital collaboration between stakeholders and thereby improve healthcare provision in Germany and Europe.
The development of architectural components and enabling technologies for the data ecosystem should build on results from Catena-X and be done in close cooperation with other funded Manufacturing-X projects. The solutions developed should revolve around the principles of Gaia-X with regard to the sovereign and secure exchange of data.
The project aims to develop concepts and standards for uniform master data management that would allow physical items and their paths along the supply chain to be digitally recorded and tracked. HealthTrack-X is focused on three main use cases:

1. The simplification of business processes via the automated provision of accompanying digital documentation for goods deliveries.
2. Joint standards for determining carbon footprints are set to enable sustainability assessment of products along their supply chain and to create incentives for sustainable business models.
3. The digitally connected supply chain should also allow for earlier predictions of supply bottlenecks.

After the conclusion of the project, a prototype data ecosystem will be made available to all participants, enabling them to take part in the tested use cases and to implement their own digital business models.

Robot-X wants to digitalise the product lifecycle for robotic systems, network the involved stakeholders and optimise interaction processes. Many small and medium-sized enterprises face high barriers to entry into robotic systems due to their significant investment costs and high-effort development phases. The Robot-X project would like to contribute to lowering these barriers and making it easier for SMEs to access tailored robots and automation lines. This is to be enabled via model-based systems engineering (MBSE) and the use of artificial intelligence (AI).

The core aim of the project is the creation of a networked data space for a harmonised exchange of digital twins for robotic components to facilitate data- and model-based systems engineering with AI. This approach entails automated configuration of robotic systems based on the toolbox principle. To this end, different cross-sector usage scenarios will be implemented across three use cases. These include the drone port, an apparatus for drone take-off and landing with military and agricultural applications. The project is also involved in industrial packaging for non-standard manufacturing parts used in industry and in composites processing, an autonomous precision grinding installation for the aviation sector.

Semiconductor-X is working with several industry heavyweights and research partners to develop digital twins based on Catena-X and Gaia-X architecture for the semiconductor industry. These are intended to digitally depict the segments of the supply and value chain where careful planning is critical, and are based on generic software architecture with parametrical data and reference models. AI-based analysis functions should help companies master sector-specific challenges, such as high throughput times, varying process yields and high product variance. Digital twins will be developed and networked at different application levels:

• Heterogenous integration: secure and efficient data exchange via partners in the manufacturing chain, especially for the integration of semiconductor chiplets in complex integrated circuit (IC) packages
• Resilient network connections: new digital services and cooperative models to optimise operations and increase quality in the manufacturing process
• Sustainability assessment: development of methods to evaluate and optimise carbon footprints, energy and water consumption along the value chain