## 7 Limitations and Future Work  
As limitations, the number of case studies may be pointed out, as well as the lack of various manufacturing sectors; for example, the automotive sector is considered a pioneer/example in various domains such as lean, which was identified as a close relative of DT, and may provide a more complete/futuristic vision about it. Furthermore, as explained in the methodology and discussion, the convenience sampling method requires certain precautions when generalizing the results. However, due to the specificity of the topic, successful companies in DT would be the main contributors to the study, and the results were obtained randomly. Given this previous study, the convenience sampling method already seems to be the most suitable, reducing the danger of not being able to generalize the results. Concerning future work, in addition to exploiting additional industries to enrich the framework, it could be interesting to explore the impact of this framework in an industry context, which may be carried out by implementing these best practices in companies that aim to implement DT to make conclusions and perceive how the framework helped the DT process. In addition, the selected companies are in a continuous learning process, and so conducting the same study in the future may increase the value of the results, allowing the framework to be enriched. It is also important to note that DT has inherent legal acts that should be followed, e.g., the General Data Protection Regulation, Artificial Intelligence Act, or NIS 2 Directive, constituting a valuable addition to these study results and the direction of future work. Another important future research direction in this area is related to the creation of a DT roadmap based on the best-practice framework presented in this study.  
Conceptualization, T.B., L.T. and C.G.; methodology, L.T. and C.G.; validation, T.B., L.T. and C.G.; formal analysis, T.B., L.T. and C.G.; investigation, T.B., L.T. and C.G.; resources, L.T. and C.G.; data curation, T.B., L.T. and C.G.; writing--original draft preparation, T.B., L.T. and C.G.; writing--review and editing, T.B., L.T. and C.G.; visualization, T.B., L.T. and C.G.; supervision, L.T. and C.G.; project administration, L.T. and C.G. All authors have read and agreed to the published version of the manuscript.  
This research is part of the SHIFT2 FUTURE project funded by FEDER (POCI-02-0853-FEDER-046960), through the COMPETE2020--Programa Operacional Competitividade e Internacionalizacao--and the Programa Operacional Regional do Centro (Centro 2020), and carried out by a consortium that included the Instituto de Soldadura e Qualidade (ISQ), Agencia para a Competitividade e Invacao (IAPMEI), Associacao Universidade-Empresa para o Desenvolvimento (TECMINHO), Universidade de Aveiro (UA), and Centro Tecnologico da Ceramica e do Vidro (CTCV). The participation of the author [PERSON] in this work was financed by the FCT--Fundacao para a Ciencia a Tecnologia--through financial support via funds from the national budget and community budget through the FSE. The author [PERSON] was supported by the Institute of Electronics and Informatics Engineering of Aveiro (IEETA), supported by Por-tuguese funds through the FCT--Fundacao para a Ciencia e a Tecnologia--in the context of the project UIDB/00127/2020.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors would like to acknowledge the participation of Sinmetro in this study.  
The authors declare no conflicts of interest.