Industry 4.0 is revolutionizing industrial production by bridging the physical and the virtual worlds and further improving digitalization. Two essential building blocks in industry 4.0 are digital twins (DT) and the internet of things (IoT). While IoT is about connecting resources and collecting data about the physical world, DTs are the virtual representations of resources organizing and managing information and being tightly integrated with artificial intelligence, machine learning and cognitive services to further optimize and automate production. The concepts of DTs and IoT are overlapping when it comes to describing, discovering and accessing resources. Currently, there are multiple DT and IoT standards covering these overlapping aspects created by different organizations with different backgrounds and perspectives. With regard to interoperability, which is presumably the most important aspect of industry 4.0, this barrier needs to be overcome by consolidation of standards. The objective of this paper is to investigate current DT and IoT standards and provide insights to stimulate this consolidation. Overlapping aspects are identified and a classification scheme is created and applied to the standards. The results are compared, aspects with high similarity or divergence are identified and a proposal for stimulating consolidation is presented. Consensus between standards are found regarding the elements a resource should consist of and which serialization format(s) and network protocols to use. Controversial topics include which query language to use for discovery as well as if geo-spatial, temporal and historical data should be explicitly supported.

The concept of digital twins (DT) has already been discussed some decades ago. Digital representations of physical assets are key components in industrial applications as they are the basis for decision making. What is new is the conceptual approach to consider DT as well-defined software entities themselves that follow the whole lifecycle of their physical counterparts from the engineering, operation up to the discharge, and hence, have their own type description, identity, and lifecycle. This paper elaborates on this idea and argues the need for systematic DT engineering and management. After a conceptual description of DT, the paper proposes a DT lifecycle model and presents methodologies and tools for DT management, also in the context of Industrie 4.0 concepts, such as the asset administration shell (AAS), the international data spaces (IDS), and IEC standards (such as OPC UA and AML). As a tool example for the support of DT engineering and management, the Fraunhofer-advanced AAS tools for digital twins (FA³ST) are presented in more detail.

Manufacturing as a Service (MaaS) enables a paradigm shift in the current manufacturing landscape, from integrated production and inflexible, fragile supply chains to open production and flexible, robust supply chains. As part of this evolution, new scaling effects for production capacities and customer segments are possible. This article describes how to accomplish this paradigm shift for the automotive industry by building a digital MaaS ecosystem for the large-scale automotive innovation project Catena-X, which aims at a standardized global data exchange based on European values. A digital MaaS ecosystem can not only achieve scaling effects, but also realize new business models and overcome current and future challenges in the areas of legislation, sustainability, and standardization. This article analyzes the state-of-the-art of MaaS ecosystems and describes the development of a digital MaaS ecosystem based on an updated and advanced version of the reference architecture for smart connected factories, called the Smart Factory Web. Furthermore, this article describes a demonstrator for a federated MaaS marketplace for Catena-X which leverages the full technological potential of this digital ecosystem. In conclusion, the evaluation of the implemented digital ecosystem enables the advancement of the reference architecture Smart Factory Web, which can now be used as a blueprint for open, sustainable, and resilient digital manufacturing ecosystems.

Industry 4.0 supports the vision of networked machines in decentralized production plants across the value chain. Hence, it requires highly connected partners exchanging relevant data about products, processes, and production resources. This paper proposes the usage of data spaces and digital twins to enable this Industry 4.0 vision and investigates the building blocks to realize a data space for Industry 4.0, e.g., the integration of digital twins inside the data space based upon the latest specification of the Industry 4.0 Asset Administration Shell. A prototypical implementation shows the feasibility of storing product carbon footprints inside a digital twin and sharing it over a data space with other partners.

Sensors provide some of the basic input data for risk management of natural andman-made hazards. Here the word ‘sensors’ covers everything from remote sensingsatellites, providing invaluable images of large regions, through instruments installed on theEarth’s surface to instruments situated in deep boreholes and on the sea floor, providinghighly-detailed point-based information from single sites. Data from such sensors is used inall stages of risk management, from hazard, vulnerability and risk assessment in the preeventphase, information to provide on-site help during the crisis phase through to data toaid in recovery following an event. Because data from sensors play such an important part inimproving understanding of the causes of risk and consequently in its mitigation,considerable investment has been made in the construction and maintenance of highlysophisticatedsensor networks. In spite of the ubiquitous need for information from sensornetworks, the use of such data is hampered in many ways. Firstly, information about thepresence and capabilities of sensor networks operating in a region is difficult to obtain dueto a lack of easily available and usable meta-information. Secondly, once sensor networkshave been identified their data it is often difficult to access due to a lack of interoperability between dissemination and acquisition systems. Thirdly, the transfer and processing ofinformation from sensors is limited, again by incompatibilities between systems. Therefore,the current situation leads to a lack of efficiency and limited use of the available data thathas an important role to play in risk mitigation. In view of this situation, the EuropeanCommission (EC) is funding a number of Integrated Projects within the Sixth FrameworkProgramme concerned with improving the accessibility of data and services for riskmanagement. Two of these projects: ‘Open Architecture and Spatial Data Infrastructure forRisk Management’ (ORCHESTRA, http://www.eu-orchestra.org/) and ‘Sensors Anywhere’(SANY, http://sany-ip.eu/) are discussed in this article. These projects have developed anopen distributed information technology architecture and have implemented web servicesfor the accessing and using data emanating, for example, from sensor networks. Thesedevelopments are based on existing data and service standards proposed by internationalorganizations. The projects seek to develop the ideals of the EC directive INSPIRE(http://inspire.jrc.it), which was launched in 2001 and whose implementation began this year(2007), into the risk management domain. Thanks to the open nature of the architecture andservices being developed within these projects, they can be implemented by any interestedparty and can be accessed by all potential users. The architecture is based around a serviceorientedapproach that makes use of Internet-based applications (web services) whose inputsand outputs conform to standards. The benefit of this philosophy is that it is expected tofavor the emergence of an operational market for risk management services in Europe, iteliminates the need to replace or radically alter the hundreds of already operational ITsystems in Europe (drastically lowering costs for users), and it allows users and stakeholdersto achieve interoperability while using the system most adequate to their needs, budgets,culture etc. (i.e. it has flexibility).