and components with direct product contact were considered. Following further
analysis of the sensory and the structural conditions (especially IT-related structures), the two corresponding data models with all relevant data points for the DTMS
were created. These virtual models also comprise the actual DTs and their data. In
terms of the physical model, the DTs are classified in the correct production order
and with a suitable semantic designation. Owing to the progress in the production
within the value chain, the DT is continuously growing in terms of data equipment,
as new, relevant data points are added in each further process step related to the
physical model. Hence, the first DT in the value chain is the data poorest, while the
last DT receives the most and all relevant data points. Subsequently, all the models
were connected via appropriate interfaces and the respective analyzed stakeholders
were involved. The overall structure of all the components finally results in the big
picture, which shows the DTMS, the structure of the DTs, and the stakeholders
involved (Fig. 12). The big picture results in individual structures for each value
chain and must always be created anew. Furthermore, it provides a complete
overview of the architecture of the DTMS and apps for the stakeholders. It is the
crucial step in the implementation of a DTMS and has a considerable impact on its
functionality.
In this case study, DTs aimed to ensure full traceability and trackability along the
value chain. Besides, the connectivity between the companies, plants, production
areas, and process cells was reached. These measures support the use of PI4.0
potentials and the stakeholder participation. The connection of both companies
considered by the full big picture creates one DTMS that enables the complete
reconstruction of the processes.
7.2 Organic Supply Chains: Implementation of a DTMS
for Vegetable and Beef Supply Chains
The approach of the DTM project (Sect. 7.1) is also used and extended in the EIT
Food project “Organic Supply Chains” (OSC, 2019, ongoing) [84], which builds a
MindSphere™ DTMS for vegetable and beef supply chains. The main objective is to
prove the organic status and safety of the produced food. The supply chains extend
from ordering and planning over processing to delivering to retailers. They connect
farmers, food producers, transporters, and retailers. Figure 13 presents an overview
of such a supply chain.
In the DTM project, all involved companies were project partners. Thus, all data
points and systems were accessible by them, and the complete production process
could be covered. In contrast, in the OSC project, only the retailers are currently the
project members, resulting in limited data and source system access. In the OSC
project, there is no concrete physical model of the farms with detailed specifications
of work centers and units for which DTs are requested. Instead, complex business
process models and notation models are used to define all the process steps. The
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R. Werner et al.
analysis of the sensory and the structural conditions (especially IT-related structures), the two corresponding data models with all relevant data points for the DTMS
were created. These virtual models also comprise the actual DTs and their data. In
terms of the physical model, the DTs are classified in the correct production order
and with a suitable semantic designation. Owing to the progress in the production
within the value chain, the DT is continuously growing in terms of data equipment,
as new, relevant data points are added in each further process step related to the
physical model. Hence, the first DT in the value chain is the data poorest, while the
last DT receives the most and all relevant data points. Subsequently, all the models
were connected via appropriate interfaces and the respective analyzed stakeholders
were involved. The overall structure of all the components finally results in the big
picture, which shows the DTMS, the structure of the DTs, and the stakeholders
involved (Fig. 12). The big picture results in individual structures for each value
chain and must always be created anew. Furthermore, it provides a complete
overview of the architecture of the DTMS and apps for the stakeholders. It is the
crucial step in the implementation of a DTMS and has a considerable impact on its
functionality.
In this case study, DTs aimed to ensure full traceability and trackability along the
value chain. Besides, the connectivity between the companies, plants, production
areas, and process cells was reached. These measures support the use of PI4.0
potentials and the stakeholder participation. The connection of both companies
considered by the full big picture creates one DTMS that enables the complete
reconstruction of the processes.
7.2 Organic Supply Chains: Implementation of a DTMS
for Vegetable and Beef Supply Chains
The approach of the DTM project (Sect. 7.1) is also used and extended in the EIT
Food project “Organic Supply Chains” (OSC, 2019, ongoing) [84], which builds a
MindSphere™ DTMS for vegetable and beef supply chains. The main objective is to
prove the organic status and safety of the produced food. The supply chains extend
from ordering and planning over processing to delivering to retailers. They connect
farmers, food producers, transporters, and retailers. Figure 13 presents an overview
of such a supply chain.
In the DTM project, all involved companies were project partners. Thus, all data
points and systems were accessible by them, and the complete production process
could be covered. In contrast, in the OSC project, only the retailers are currently the
project members, resulting in limited data and source system access. In the OSC
project, there is no concrete physical model of the farms with detailed specifications
of work centers and units for which DTs are requested. Instead, complex business
process models and notation models are used to define all the process steps. The
156
R. Werner et al.