FAQ
Manufacturing-X is implemented in practice through so-called X-projects. In these projects, industry-specific real-world use cases are developed and tested to demonstrate the added value of data sovereignty and interoperability.
Overview of the X Projects
- Ensuring data sovereignty: Building a sovereign, open, and interoperable data ecosystem for cross-organizational and cross-industry data collaboration
- Protecting data sovereignty: Companies retain control over their data and define access rights themselves.
- Digitizing value-added networks: Supply and production chains are being made more integrated, transparent, and efficient.
- Promoting sustainability: Digital product passports and traceability support the circular economy and help meet regulatory requirements.
- Building resilience: By sharing data, disruptions in supply chains can be identified and mitigated more quickly.
- Enabling innovation: Open standards and interoperability lay the foundation for new data-driven business models.
- Ensuring Competitive Strength: Through digital innovations and new data-driven business models, companies can thrive in the competitive landscape of the future and actively assume a leadership role.
A data room is a digital infrastructure that enables companies to exchange data in a secure, standardized, and controlled manner. Unlike traditional platforms, where data is often collected centrally and controlled by a single provider, a data room operates on the principle of decentralization. This means that the owner decides with whom to share which data and for how long. The data is made accessible via common standards, interfaces, and rules so that others can use it, provided the owner permits it.
Within the Manufacturing-X framework, the industrial data room serves as the technological and organizational foundation for connecting companies across the entire value chain.
Key features:
- Data sovereignty: Each company decides for itself who is allowed to view or use which data.
- Interoperability: Uniform standards ensure that data is compatible across industries, systems, and countries.
- Trust: Access and usage rules are transparent and are defined jointly.
- Decentralized architecture: Instead of centralized data silos, a federated network of many nodes is created.
An industrial data ecosystem is a network-like system comprising companies, institutions, and technologies that aim to exchange and utilize data according to shared rules. It is not merely about the infrastructure itself (as in the case of a data room), but also about the shared processes, business models, and standards that enable data exchange and make it economically viable.
Manufacturing-X defines an industrial data ecosystem as the practical application layer of the data space:
- The data room provides the technical and organizational foundation (e.g., control, interfaces, security).
- The data ecosystem is a networked environment consisting of interoperable data spaces. It describes the collaboration among stakeholders who independently share, link, and utilize their data for specific use cases.
An industrial data ecosystem is characterized by the following features:
- Sovereign data exchange: everyone retains control over their data.
- Interoperability and standards: so that all systems can communicate with one another.
- Trust-based governance: common rules, transparent access rights.
- Scalability: Use cases can be scaled up from pilot projects to entire industries.
Data sovereignty in the data space means that data owners retain full control over their information. Specifically, this means that data sovereignty ensures that every company or individual retains control over their own data and does not relinquish that control to external platforms or providers.
Data owners make decisions through mutual contractual agreements, for example:
- Who has access to which data.
- How long the data will be shared and under what conditions.
- You can change or revoke access at any time.
- Data is not centrally managed or stored by third parties
A digital twin in the data space is a virtual representation of a real-world object, process, or system that reflects all relevant information, such as properties and states. A digital twin accompanies the corresponding physical product, process, or system throughout its entire lifecycle.
In the context of a data room, this means that the digital twin aggregates and securely integrates data from various sources, such as machines, sensors, or enterprise systems. On this basis, analyses, simulations, and optimizations can be performed without the need to intervene on the physical object. This makes it possible to improve processes, run through scenarios, or identify potential causes of errors at an early stage.
In addition, the digital twin lays the foundation for trust-based collaboration within the data ecosystem: Companies can manage joint projects and processes in a data-driven manner while retaining full control over their own data at all times, as the principles of data sovereignty are upheld within the data space. Another advantage is interoperability: Different partners, each using their own IT systems, can work using the same digital model and thus collaborate seamlessly.
In this way, the digital twin in the data room becomes a tool that not only creates transparency but also takes innovation, efficiency, and collaboration to a new level.
An Asset Administration Shell (AAS) is a central concept of Industry 4.0 and serves as a data format for implementing the digital twin. The AAS describes all relevant information about a physical asset—such as a machine, a product, or a component—and provides interfaces and security mechanisms through which the data can be accessed.
It supports digitization, automation, and analysis without the data being scattered across different formats or siloed solutions. The administration shell plays a central role in the data space, as it provides a standardized data format and thus ensures interoperability. Because it is readable by both machines and humans, different systems as well as companies can uniformly access the information via the data space based on the AAS.
A catalog in a data room is an organized overview of available information and services that enables efficient, secure, and transparent access for all participants. The catalog clearly lists available information, documents, or services. The information itself is not publicly visible to everyone; instead, only descriptions such as origin, format, or access rights are displayed. This ensures transparency and traceability while maintaining data and IP security.
OPC UA (“Open Platform Communications Unified Architecture”) is an international industrial standard for the exchange of data and information between machines, plants, systems, and software. It is developed by the OPC Foundation and is particularly relevant in Industry 4.0 and Manufacturing-X.
Key features of OPC UA:
- Manufacturer- and platform-independent – works with machines and systems from various vendors.
- Standardized interfaces – a common “language” for communication in production.
- Built-in security – encryption, authentication, and access control.
- Scalable and expandable – suitable for use from sensors to cloud platforms.
- Information modeling – not just raw data, but also context (e.g., units, structures, states).
Real-world examples:
- Machines communicate seamlessly with one another on a production line.
- ERP, MES, and cloud systems access production data directly.
- Digital twins can be enriched with real-time information.
OPC UA is a key standard for ensuring interoperability and data flow in the connected industrial sector.
A use case describes a specific situation in which a system, product, or technology provides a particular benefit. It answers the question: “Who uses what, for what purpose, and with what goal?”
In the various Manufacturing-X projects, current industry challenges related to cross-sector data exchange are examined. In the process, potential solutions for these specific use cases are discussed and developed.
Typical components of a use case:
- Stakeholders: Who is involved? (e.g., machine operator, IT system, customer)
- Trigger/Goal: What problem or goal is at the heart of this?
- Process: What steps are involved in achieving the goal
- Benefits/Results: What value does the solution provide?
A demonstrator is a hands-on model or prototype that makes a technology, concept, or use case tangible and experiential.
Features of a demonstrator:
- Clarity – complex technologies become tangible.
- Proof of Concept – demonstrates that an idea works from a technical standpoint.
- A foundation for learning and discussion – helps to understand the benefits and limitations.
- Not a finished product—often still in the laboratory or pilot stage.
In summary, it can be said that a use case describes the benefits in words, while a demonstrator makes those benefits visible and tangible. For this reason, various demonstrators are being developed within the individual X projects to visually illustrate and bring to life the proposed solutions for a given use case.
A digital business model uses digital technologies and data to create new products, services, or revenue streams. For example, companies can:
- Offer maintenance or diagnostic services (e.g., servicing machines before they break down),
- use digital twins to optimize products or processes,
- or work with partners to develop new solutions for sustainability or supply chains.
In a data room, companies can securely and confidently share and use data with others. This opens up new opportunities for collaboration and innovation, such as through greater transparency, automated processes, or joint data-driven offerings.
In short: Participating in a data room opens the door to more data, trust, and cooperation—and thus to new markets and digital business models that were previously impossible.
Collaborative engineering refers to the joint and coordinated development of products or technical systems by multiple stakeholders, often across organizational boundaries, supported by digital technologies. The goal is to enhance collaboration, efficiency, and innovation by enabling experts from different fields to work simultaneously on designs, simulations, or production processes.
A data room provides a secure and standardized environment where companies can share data throughout the entire product lifecycle—from development to production. This allows different partners to work on a product simultaneously and efficiently without losing control over their data. This means:
- Design data, simulations, and bills of materials can be exchanged easily and securely.
- Changes are immediately visible to everyone involved, which reduces errors and duplicate work.
- Suppliers, manufacturers, and customers can better coordinate their efforts and respond more quickly.
A data room enables collaborative engineering by fostering trust, transparency, and secure collaboration across organizational boundaries, thereby facilitating joint, data-driven product development.
