## 2 Background and Research Gaps
Despite the wide-ranging debate on digital transformation and Industry 4.0, there is still a lack of standards and operational guidelines that can support companies in defining clear strategies for automation [78]. This deficiency is a real challenge, hindering the effective integration of technologies into production processes [79]. In the absence of a shared framework, it is difficult to identify the best practices and ensure a sustainable transition to advanced production models [80]. The problem is particularly critical for small and medium-sized enterprises (SMEs), which often operate with limited resources. Without accurate guidance, they risk making \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Field of Application** & **Main Benefits** & **Main Challenges** & **Main References** \\ \hline
14. & DES is employed for modeling drug & Regulatory & \\ Pharmaceuticals and & manufacturing processes, clinical trial designs, & & \\ Drug Development & and supply chain management. & & \\  & & & \\ \hline
15. & NASA and other space agencies use DES for & & \\ Space Exploration & mission planning, spacecraft operations, and & & \\ exploration simulations. & & & \\  & & & \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 1: Time distribution of articles published on the topic from 2000 to the present [Source: Scopus]. erroneous investments, such as purchasing unsuitable technologies or poorly integrating them into existing processes [9; 81], with negative consequences for competitiveness [82]. In response to these difficulties, the literature proposes useful decision-making tools to guide technology choices. The study by [83], for example, shows how discrete event simulation (DES) helps reduce decision uncertainty by identifying KPIs through simulated scenarios. Similarly, ref. [84] employs DES to optimize buffer allocation in logistics flows, while [85] uses it to validate the improvement of an assembly line for PCBA. Also making a relevant contribution is [86], which integrates DES and Throughput Accounting to support medium-term decisions in manufacturing system development. Applied to a roof window manufacturing company, the study demonstrates how simulation provides strategic information that is difficult to access with traditional tools. Despite the growing number of contributions, the literature review still shows relevant methodological and conceptual gaps. This research aims to fill in these gaps, as summarized in Table 2, contributing to advancing knowledge and improving practices in the field of industrial digitization. However, despite the large amount of research available in the literature on this topic, a thorough analysis of published articles reveals the existence of several significant gaps. These methodological and conceptual shortcomings represent critical areas that this research aims to address. Table 2 summarizes the main gaps identified and what this work aims to do to advance knowledge and improve practices in the field of digital transformation and Industry 4.0. In summary, the literature highlights the value of discrete event simulation (DES) as a Key Enabling Technology for digital transformation in manufacturing. However, significant gaps remain, particularly in terms of integrated methodologies, practical guidelines, and