## 2 Theoretical Framework  
### Digital Transformation: Technological Solutions  
Digitalization is a concept that includes a bundle of technologies, such as AI, IoT, big data, or blockchain [28], which are also within I4.0 [7], empowering the connections between these concepts [29]. I4.0 has nine technological pillars as drivers: big data and analytics (BDA); autonomous robots; simulation; horizontal and vertical system integration; industrial internet of things (IIoT); cybersecurity; cloud; additive manufacturing (AM); and augmented reality (AR) [30; 31; 32]. Briefly, BDA may be understood as the advanced algorithms used to perceive trends. This technology requires huge amounts of data, a condition easily attained in I4.0, where mass data collection is a reality [33]. Understanding behaviors, inherent to BDA, may be advantageous in many domains such as predictive maintenance, energy consumption, consumer trends, and inventory management [29]. The second pillar, autonomous robots,can be described as the use of robots with the capacity to perform certain tasks without human intervention [34]. At the simulation level, one concept that has been flourishing in the 4.0 paradigm is that of digital twins, which consists of the digital recreation of the physical world, providing benefits such as evaluating what is happening in the real domain, virtually perceiving the impact of changes [35], reducing costs, and improving decision-making processes [36]. This technology differs from traditional simulation by providing a bidirectional data exchange (between the real and virtual worlds) [36]. The integration of horizontal and vertical systems consists of structures that allow integration in both directions. The first integration occurs by mixing the value chains, e.g., in the integration between customers and manufacturing organizations [37]. In vertical terms, integration happens in an organization between different hierarchical levels [38]. IIoT is one of the core I4.0 technologies; it allows connections between sensors, manufacturing lines, and systems, allowing data collection in real-time [39] and providing benefits [40]. Cybersecurity is a concern of this digital paradigm due to the amount of data exchanged, which should be guided by ethical concerns and ensure user data security and privacy [37]. The cloud consists of a form of data storage, which allows other technologies such as BDA or AI to function [41]. AM aggregates a bundle of techniques and provides new opportunities, such as the use of new materials or obtaining customized products [37]. AR consists of the addition of virtual elements to the real environment [37], enabling improvements in assembly processes, reducing resource consumption, and aiding in maintenance processes [42]. Another technology with strong links to I4.0 is AI, which consists of a bundle of algorithms that \"look\" for certain data and provide decisions as a human does with the same information [43]. This technology could be applied to diverse contexts such as in customer relationship management (CRM), which refers to the capacity to understand and retain customers, managing relations [44; 45] to improve the understanding of customer behavior [46]. CRM systems, used to fill CRM objectives [47], could transfer orders automatically to the ERP [45], which shows the potential for data connection, avoiding manual errors and time waste. ERP could be understood as a platform that integrates processes, data, human resources, and materials to optimize resource management, boosting company competitiveness. Between ERP and the factory floor exists MES, which is a fundamental bridge to integrate information [48]. The data collected on the factory floor are facilitated by supervisory control and data acquisition (SCADA) which enables a centralized platform to collect the data from various sources, allowing processes for real-time monitoring [49].