## 4 Results
### Best-Practice Framework
In this category, it has comprehension of the organization's digital maturity level as the first best practice, which may be carried out by means of digital maturity assessment models. In this phase, it may be important to understand which solutions are available to create the DT plan and, as a best practice, carrying out benchmarking actions can be highlighted. The utilization of consulting services is also considered a best practice, since companies may not have the proper competencies to define the strategy for digitalization. Another important note that may be considered a best practice relies on the implementation of lean principles, as emphasized in the case studies. This adoption may be justified by the reduction in waste and clarification of processes inherent to this philosophy. This is a relevant point, since technological adoption without a previous implementation of the lean principle may lead to technological employment without advantage. In the second category, operationalization, a set of best practices to consider when inserting technology will be presented. The first best practice present here is the creation of data networks, which allow the comprehension of process indicators in real time. This is particularly relevant because if the process measurements are not correct, the decision-making processes may be threatened. These case studies show the last idea stated, valuing the importance of knowing the state of the company. Linked to data network creation, the second best practice in this category arises, that of data integration across the various systems, since if data are not shared, the potential of the data network structure created can come under threat. Another best practice is connected with data integration, ensuring an understanding of the different types of data variables. To ensure data security, cloud storage is pointed out as another best practice. These data may be used to improve the processes or to ensure the fulfillment of imposed norms, e.g., quality norms. Another best practice which is almost consensual across the different case studies is the creation of dashboards to streamline monitorization of the organization's status. Dematerialization, e.g., using metadata reading, is considered a best practice since it allows a reduction in resources and makes information searches easier. The last best practice highlighted in this second category of framework is compliance with UI/UX application development principles. The last category in the framework arises for organizations where dematerialization, real-time information, or data networks are a reality; it can be said that the good practices in this category aim to add even more value to DT. Thus, as the main best practice, the application of advanced algorithms (e.g., ML, BDA, or AI) to extract advanced conclusions about data can be highlighted, reducing the time it would take the employee to perceive the trend while providing additional tools to improve the decision-making process. The remaining best practice in this category lies in the horizontal integration of the whole value chain, which will allow agile answers to external variations. Two best practices were not included in any one category: ensuring cybersecurity concerns and capacitating the workforce, because they are transversal to the three categories. Concerning ensuring cybersecurity concerns, it is important to have some redundancy of data storage or systems to protect data. Concerning the capacitation of the workforce, it is important to train people to use systems and approaches where technology gives answers (e.g., SPC), as well as training them to react to cybersecurity threats. Below, Table 2 summarizes the best practices identified, with the DT stage reference.