## 4 Results  
As a result of the literature analysis, cross-industry principles for system modeling to form a digital representation of complex technical systems were identified. For ease of understanding, all principles are summarized in a single list (Table 1). To compare the distribution and determine the most used principles across all industries, a summary chart was created (Figure 4). The chart shows the industries mentioned in the studies, with the horizontal axis representing the principles mentioned in research papers. Fragments related to different industries are highlighted in color. The chart is in a stacked bar chart format, where the total height of the columns corresponds to the ratio of the number of articles that use the principle to the total number of articles considered (100%), which reflects the degree of use of each principle. Each column is divided into shares of using the direction of the principle. For clarity, the color of each share corresponds to the ratio of the number of articles where that principle was revealed to the total number of articles where that principle was revealed (with 100% being the full height of the column). The following charts (Figure 5) show the percentage of usage of the principles in each direction, allowing for identification of the most and least used principles in industry. \begin{table}
\begin{tabular}{c c} \hline \hline  & Metamodeling \\  & Ontology \\  & Semantics \\  & Stability \\  & Hierarchy \\ System of systems level principles & Unified standards \\  & Interoperability \\  & Transformability \\  & Iterative principle \\  & Systematic verification \\  & Lifecycle \\ \hline  & Subjectivity principle \\  & Reverse functional \\  & Independence \\  & Networking principle \\  & Externalization \\  & Minimization \\  & Systematic validation \\  & Reusing \\  & Visualization \\  & 3D model usage \\  & \"Black box\" \\  & Generativity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cross-industry principles for the formation of a digital representation of complex technical systems. Figure 4: Identified principles and areas of application. Figure 5: Distribution of principles within each area of application 1—Meta-modeling, 2—Semantics, 3—Stability, 4—Hierarchy, 5—Independence, 6—Interoperability, 7—Transformability, 8—Iterative principle, 9—Systematic verification, 10—Ontology, 11—Subjectivity, 12—Reverse functional, 13—Unified standards, 14—Networking, 15—3D Model, 16—Externalization, 17—Lifecycle, 18—Minimization, 19—Visualization, 20—Systematic validation, 21—Reusing, 22—Black box, 23—Generativity. Legend: Using principles in the industry —more than 50%, —  
Based on the research results, the following conclusions can be drawn for various industries regarding digital transformation. Aerospace prioritizes hierarchy, interoperability, unified standards, and semantics. Civil Engineering focuses on hierarchy, lifecycle, and meta-modeling. Management emphasizes interoperability, systematic verification, ontology, and subjectivity. In the IT sector, there is a strong focus on hierarchy, meta-modeling, lifecycle, and systematic verification. The military prioritizes interoperability, lifecycle, and visualization. Mechanical engineering highly prioritizes hierarchy, interoperability, systematic verification, ontology, and lifecycle. Manufacturing emphasizes hierarchy, interoperability, ontology, and subjectivity. The energy industry prioritizes meta-modeling, hierarchy, and subjectivity. In summary, the industries analyzed generally prioritize hierarchy, interoperability, and lifecycle for their digital transformation, with variations in other principles based on the industry's specific needs. Differences across industries lie in the degree to which other principles such as semantics, stability, and systematic verification are applied. This information can help guide the development of digital transformation strategies tailored to each industry's needs and priorities.