## 4 Conclusions and Future Work  
The usage of DTs is still at an early stage. As a result, standards for implementing them are lacking. With the aim of covering this major gap, a general architecture was pondered jointly with the digital model's implementation, so that future solutions may be based on it.  
The suggested architecture focuses on dividing each component of a CPS. Moreover, none of the used technologies narrows down the range of target-problems, given that widely-compatible programming languages and file formats were opted for. This way, it is believed that a considerable percentage of the oncoming solutions will be safe to rely on an architecture along the lines of the one hereby introduced.  
It is possible to conclude two main ideas from the results of the proposed article. The first one is that the utilisation of simulation-based DTs to predict the behaviour and, more specifically, a distributed manufacturing control system's performance can be useful. Due to the similarity of the values predicted and collected from the real system, the results are promising. A prediction, even not very accurate, can constitute a relevant contribution to applying these distributed control approaches because it is possible to approximately predict the system's performance and the self-organised ecosystem's behaviour, which is impossible to do presently. The second lesson learnt from this research is that the utilisation of simulation to mimic a distributed control logic needs to mimic not only the logic behind each decision and interaction, but also other characteristics like network connections or the amount of cyber-physical modules, and possibly the number of computational devices available and their performance.  
To sum up, the discovered research gaps were not fully covered, because the network's stability and the amount of products being simultaneously processed substantially influenced the model's performance. However, these breakthroughs may be cornerstones of further research on the topic, provided that such drawbacks are taken into consideration on future DTs' implementations.  
As future work, since the communication delays were found to be the most likely cause of inaccuracy, the idea of modelling them arose. Moreover, given that the manufacturing unit can deal with changes in the resource's location halfway through the simulation, it would be valuable to provide the DT with such capability. Finally, it is advisable that the developed model be exported as a stand-alone app and launched from the MAS project. Thereafter, the DT becomes attached to its physical counterpart's implementation.  
Conceptualization, A.D.R. and J.B.; methodology, G.R.R. and A.D.R.; software, G.R.R. and J.T.; validation, G.R.R., A.D.R. and J.T.; formal analysis, G.R.R. and A.D.R.; investigation, G.R.R. and A.D.R.; writing--review and editing, G.R.R. and A.D.R.; supervision, A.D.R. and J.B.; funding acquisition, A.D.R. and J.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the FCT/MCTES project CESME--Collaborative & Evolvable Smart Manufacturing Ecosystem, funding PTDC/EEI-AUT/32410/2017.  
Not applicable.  
The authors declare no conflict of interest.