# A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing
## 3 Results
### Features  
Out of the selected 104 articles, 37% are focused on analytics, 30% on cyber-physical systems (CPS) and 15% on general smart manufacturing concepts, as depicted in Figure 4. In addition, 8% discuss digital twin technology, 5% robotics and less than 4% both human-robot collaboration (HRC) and mixed reality technologies (XR). However, this can be misleading as some articles used AR or VR as applied technologies but were not directly focused on XR. For instance, [PERSON] et al. studied enabling remote human-to-machine applications and used VR in their experiments to study the efficiency of their approach to HRC [39]. Some other examples of studies where XR was used, but that was not reported as the sole theme are the works by [40; 41; 42; 43]. Therefore, it can be said that the total use of XR is better reflected by Figure 7A,B describing the applied technologies in the paper. CPS technologies bridge the gap between the communication of cyber and physical world while studying HRC is significant for human-centric future of smart factories [44; 41]. Next, the key features and obstacles identified are introduced, after which, the studies are elaborated on further. Table 3 details a summary of the total 317 unique features within the articles included in the SLR, which have been grouped into five subcategories (fault detection, predictive maintenance, communication, virtualization, human machine interference (HMI)). Various Industry 4.0 IT solutions are used in fault detection [45; 46; 47], predictive maintenance [48; 49; 50], communication [51; 52; 53], virtualization [54; 55; 42], and human-machine interference (HMI) [56; 57; 58]. In addition to the aforementioned features, there are obstacles that need to be considered during the implementation of various smart manufacturing principles. Figure 5 shows that the main concern areas include cost, security, privacy, and data acquisition. Yet, out of the 104 papers, 25 papers were marked with no obstacles as they did not specify obstacles directly or it was not inferable. Indeed, acquisition and quality of real-time data during manufacturing can be challenging and costly [59]. A more detailed summary of the total of 117 obstacles in smart manufacturing is given in Table 4, sorted by category (data governance, predictive analytics, quality, other). The obstacle and feature (Table 3) subcategories differ because for instance data governance issues were overarching theme across fault detection, predictive maintenance, and HMI [21; 45; 60; 61]. In a similar manner, cost is an obstacle for communication [62], HMI [63] and virtualization [55]. Figure 4: Recurring themes in literature. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Governance** & **Predictive Analytics** & **Quality** & **Other** \\ \hline \multirow{2}{*}{Data integration} & \multirow{2}{*}{Imbalance causes issues} & Solutions only for simple manufacturing systems & Cost \\ \hline Security & Parameter configuration & Architecture & Latency \\ \hline Data acquisition & Data preparation & Lack of standards & Communication \\ \hline Data validity & Automation & Adherence to standards & Ethics \\ \hline Data aggregation across systems & Optical noise & Not generalizable \\ \hline Privacy & User position tracking indoors & Safety \\ \hline Data ownership & Dealing with unexpected scenarios & AR cannot be used for long hours \\ \hline \multicolumn{3}{c}{Real time application} & High computing power required \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Obstacles identified in articles. Figure 5: Most mentioned technological obstacles in papers.