## 7 Discussion  
The methodology used in this paper shows the impact that production model granularity has on DTC applications. We next discuss the findings in relation to the research questions.  
_RQ1: What impact does production model granularity have on activity recognition?_  
We investigated a specific use case, namely, the Kelly pile production. The hierarchical classification study of activity recognition shows the conflict between the granularity of the training and the test data. Related literature has shown that the model's accuracy decreases with an increase in the labels' granularity. They conclude that they have an insufficient knowledge about the production system itself. In this paper, although the given labels are based on deep expert knowledge, the results are not satisfactory either. They show that the granularity of the activity description must refer to equipment motions, which defines the requirement for a deep understanding of the production system. For example, activities where equipment is not involved cannot be distinguished sufficiently, e.g., _Concrete_ vs. _Waiting for concrete._  
_RQ2: How does production model granularity affect the application of DTC?_  
In our DTC approach, the activity recognition aims to feed the DES with as-built data. A data-driven simulation study shows how this DTC enables construction forecasts. Challenges arise on the assumed granularity of the production system model. We used a simple Petri net without interdependencies throughout the production flow. On the contrary, we integrated the recognized idle times. Thus, we cannot make any statement on the influence that material flow, such as soil disposal or concrete delivery, or operation variabilities, such as tool change or casing screw, have on the production system. However, these influences are essential for its optimization.  
_RQ3: What is needed to adopt production models for DTCs in heavy civil engineering?_  
Based on the previous RQs, we propose that DTCs require a production model that reflects the system's specific components (Figure 15).  
Figure 14: Construction sequence of piles with respect to their length: (**a**) as-built; (**b**) ascending.  
The two components of the DTC, activity recognition and the data-driven DES, serve different purposes: Whereas activity recognition analyzes sensor data, requiring equipment motion steps, the DES analyzes performance requiring value-adding and non-value-adding subprocesses. Although these components have specific requirements, they pursue the same purpose: making the production flow reliable. This reliability is focused on making the production flow, or the sequence of its processes, visible and understandable to models. The next step is then to scale these models toward a catalog of production models (Figure 16). [PERSON] describes this scaling process generically in an evolution, such as power lines. Considering a top-down planning approach, project planning starts by adapting a reference project, deriving the flow-based production model, and modifying it with time. This evolution leads to an enlarged database capturing different variants of the models.  
Figure 15: Activity diagram of the Kelly pile production system and its flows (material supply (green)/removal (orange), information (yellow)) focusing on the process step ’Drill’ (blue).