# A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing
###### Abstract
IoT architecture can contain three components-edge computing, fog computing and cloud computing [15]--where cloud technologies adopt these three layers to share the data between devices and perform big data analytics [16; 17]. The edge layer, notably, contains the data sources such as machine tools, speeding up data analytic processed by moving computational power towards the network edge [15]. Moreover, fog computing involves communication with the cloud and performing computationally heavy tasks close to data source to assure real-time analysis capabilities [15; 18]. Cyber-Physical Systems (CPS) are further merging the cyber and physical world by means of computational technologies to improve the efficiency and flexibility of physical system [19; 20]. CPS, for example, may serve as a basis for digital twins, which is the virtual model of a physical object [21] and is a technology increasingly adopted along-side Industry 4.0 approaches to enhance management, resource supervision and automation. Additionally, it must be noted that the concept of Industry 5.0 has been recently proposed as the next step. Sustainability and mass personalization are predicted to be core additions to the use of IoT technologies [22; 23]. However, before moving on, Industry 4.0 challenges should be suitably addressed, particularly within the medical device manufacturing domain. It is clear that there are existing limitations with the technological advances brought about by Industry 4.0. This has led to the following research questions addressed in this article:  
1. RQ1. In which ways do smart technologies have the potential to revolutionize supply chain for medical device manufacturing? 2. RQ1.1. What are the features and obstacles of digital medical manufacturing? 3. RQ1.2. What are some of the most used digital technologies based on the academic literature? 4. RQ1.3. What are the ways in which end-to-end manufacturing processes are analyzed and optimized through digital technologies? There are various existing SLRs studies relating to lean manufacturing trends and future research methodologies [24; 25; 26; 27; 28; 29; 30; 31]. However, current SLRs either focus on the larger scope of Industry 4.0 or on a specific area in manufacturing. For example, [PERSON] et al. studied the different research approaches used to study Industry 4.0 in the literature and the overall state of research [24]. Moreover, [PERSON] et al. aimed to study the current state of research around smart factory concept and the gaps in research in further detail [25], while [PERSON] et al. studied the energy consumption of smart manufacturing sites and the challenges it brings [26]. Some of the more single-focused manufacturing component research in the field includes the work by [PERSON] et al., which reviewed Industry 4.0 technologies in the field of the state-of-art predictive maintenance using SLR methods, discussing the challenges and limitation which arise from the literature [27]. In a similar manner, [PERSON] et al. studied smart production planning and control and the applicable performance indicators, environmental factor conditions and smart capabilities [28]. They found IoT to be the biggest supporter of production planning and control [28]. [PERSON] et al. studied the relationship between circular economy and Industry 4.0, focusing on the technologies from lifecycle management point-of-view [29]. In a similar way, [PERSON] and [PERSON] studied opportunitiesbrought by Industry 4.0 for supply chain management and Triple Bottom Line sustainability [30]. Lastly, the study done by [PERSON] et al. studied the main contributions in the management literature, which focused on the social aspects (e.g., management differences) of adoption of these emerging technologies [31]. However, to our best knowledge, there is a limited amount of research conducted specifically on the regulated medical device industry.