## 5 Discussion  
Available research clearly agrees with the need for digitisation for small- and medium-sized enterprises. The authors consider SMEs as key players in the market, but also as the most endangered by the need to digitise and thus improve their competitiveness compared to large companies. At the same time, surveys conducted in various countries point to the unpreparedness or even reluctance of more than half of SMEs to prepare a strategy to digitise them and upgrade them to the Smart Factory level, which could jeopardise the market soon as they do not run out of production capacity as a result of the growing demands for large companies using digitisation tools. For these reasons, it is necessary for small- and medium-sized enterprises to create and implement strategies and plans for digitisation of their production environment and transformation to a level close to the Smart Factory. Digitisation is a major challenge for SMEs, whether financial, technological, or educational, and the three challenges are closely interlinked. Probably the most important of these three challenges is the financial challenge, associated with the lack of capital needed to move the business to Industry 4.0. Small- and medium-sized enterprises cannot compete with large companies in terms capital, which is why digitalisation is a major problem for them. It could be solved by implementing only the attributes of Industry 4.0 needed to transform the company to a level close to the Smart Factory. If companies incorporate the necessary attributes, they will be able to profit from them and speed up and improve their production, which would increase their competitiveness in the market and thus increase profits. As an example, we illustrate predictive maintenance. If the company were able to effectively predict machine failures, it would be able to avoid them and could effectively use the time required for repairs for production, which would create less downtime. The second big challenge is the technological challenge. Enterprises often have older machines from which it is difficult to obtain data [82]. These machines need to be replaced \begin{table}
\begin{tabular}{c c} \hline \hline
**Key Industrial Features** & **Jetson AGX Industrial** \\ \hline Operational Temperature & \(-40\)\({}^{\circ}\)C to 85 \({}^{\circ}\)C at TTP \\ \hline Vibration Operational &
\begin{tabular}{c} 10-500 Hz, 5G RMS (random/sinusoidal) \\ Non-operational: 10–1000 Hz, 3G RMS, \\ 3-axis, FCT \\ \end{tabular} \\ \hline Temperature & Operational: \(-40\)\({}^{\circ}\)C to 85 \({}^{\circ}\)C at TTP \\ \hline Humidity & Non-operational: 95\% RH, \(-10\)\({}^{\circ}\)C to 65 \({}^{\circ}\)C \\ \hline Operating Lifetime & 10 Years \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Jetson Xavier AGX Industrial technical specifications. with newer IoT devices, or the old devices transformed via new devices/sensors to the IoT level needed for efficient data collection. Without the right technical equipment, transformation of companies to a level close to the Smart Factory will be difficult. Horizontal and Vertical System Integration is also part of this challenge if the company has several non-communicating systems. It is necessary to unify the systems either into one large system or to ensure the transfer of critical data between existing systems. In the case of critical data transmission, it will be necessary to analyse such data to start the integration process. Collecting data from all sensors/facilities in the company and evaluating them can be difficult for small- and medium-sized enterprises. Therefore, it is also possible to determine the criticality and non-criticality of the collected data, as mentioned above, and to evaluate just such data. Data analysis needs sufficient computing power. Businesses have two options for dealing with this problem. The first solution is to use computers with sufficient performance and perform analysis in house, which can be costly in terms of investment and energy cost.