A combination of both concepts, the IDS and the AAS, likely ensures interoperability between various stakeholders and respects individual data protection needs.
Both are industry-neutral approaches, also applicable to production systems. Their
combination is a reasonable basis for a shared DT. In particular, interoperability in
this context refers to the likelihood of simple migration to various cloud and edge
providers. The GAIA-X project [75] aims to enable such cross-cloud usage. In
general, GAIA-X forms a networked and provider-neutral data infrastructure that
enables secure storage (data in rest), sovereign exchange, and collaborative use of
data and services.
5 Integration of Models and Data Sources into
a DT-Compatible Platform
Integrating different data sources is a major advantage of implementing DTs. For
example, a product’s DT typically requires information from not only enterprise
resource planning (e.g., batch id and recipe), but also process control (e.g., the used
amount of supply material and produced product and resource consumption for the
product). Data can be integrated by storing information from different sources on a
specific platform, such as a cloud as well as an on-premise platform. An example of a
cloud-based platform is MindSphere™ [76], which has been used in the EIT Food
project “Digital Twin Management” (Sect. 7.1) [77]. The main advantage of using a
cloud-based platform is that there is no need for resources to maintain the
corresponding IT infrastructure and data backups and that the data is available
everywhere. Platforms need to include a user and access management as well as
IT security measures for the exchange of information.
A use case specification for a digitalization project may reveal the lack of data and
interfaces. This is particularly significant for brownfield installations with low-level
automation or connectivity and requires investments in the automation network,
additional sensors, and engineering of additional data points. Connecting various
data sources with the platform can be supported with suitable devices and software
modules. Automation components such as controllers need to connect to the platform, as well as to a REST-API. Software tools require a connection to other sources
such as SAP systems.
Once the data is available on a platform, it can be used for visualization and
analysis. These apps can be created and provided as a service by any provider.
However, data access and analysis are simplified mainly by using a suitable data
model, providing a meaningful semantic description of the data on the platform.
DTs can also be used to increase the transparency of value chains. This is
achieved by sharing parts of DTs with business partners or authorities. Data ownership must be respected by such a solution, for example, preventing data users from
accessing or even manipulating an owner’s data without consent. An excellent
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R. Werner et al.
Both are industry-neutral approaches, also applicable to production systems. Their
combination is a reasonable basis for a shared DT. In particular, interoperability in
this context refers to the likelihood of simple migration to various cloud and edge
providers. The GAIA-X project [75] aims to enable such cross-cloud usage. In
general, GAIA-X forms a networked and provider-neutral data infrastructure that
enables secure storage (data in rest), sovereign exchange, and collaborative use of
data and services.
5 Integration of Models and Data Sources into
a DT-Compatible Platform
Integrating different data sources is a major advantage of implementing DTs. For
example, a product’s DT typically requires information from not only enterprise
resource planning (e.g., batch id and recipe), but also process control (e.g., the used
amount of supply material and produced product and resource consumption for the
product). Data can be integrated by storing information from different sources on a
specific platform, such as a cloud as well as an on-premise platform. An example of a
cloud-based platform is MindSphere™ [76], which has been used in the EIT Food
project “Digital Twin Management” (Sect. 7.1) [77]. The main advantage of using a
cloud-based platform is that there is no need for resources to maintain the
corresponding IT infrastructure and data backups and that the data is available
everywhere. Platforms need to include a user and access management as well as
IT security measures for the exchange of information.
A use case specification for a digitalization project may reveal the lack of data and
interfaces. This is particularly significant for brownfield installations with low-level
automation or connectivity and requires investments in the automation network,
additional sensors, and engineering of additional data points. Connecting various
data sources with the platform can be supported with suitable devices and software
modules. Automation components such as controllers need to connect to the platform, as well as to a REST-API. Software tools require a connection to other sources
such as SAP systems.
Once the data is available on a platform, it can be used for visualization and
analysis. These apps can be created and provided as a service by any provider.
However, data access and analysis are simplified mainly by using a suitable data
model, providing a meaningful semantic description of the data on the platform.
DTs can also be used to increase the transparency of value chains. This is
achieved by sharing parts of DTs with business partners or authorities. Data ownership must be respected by such a solution, for example, preventing data users from
accessing or even manipulating an owner’s data without consent. An excellent
150
R. Werner et al.