# A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing
## 3 Results
### Smart Manufacturing Principles  
Within the articles selected during the SLR process, it is clear that in Industry 4.0, manufacturing moves towards agility and mass-customization. For example, agent-based manufacturing is proposed, which divides the manufacturing system into multiple departments, such as cloud-controlled suggestion, product, machining and conveying agents as discussed by [PERSON] et al., and [PERSON] et al., [64; 65]. As can be expected, the customization adds a layer of complexity as manufacturing and supply chain face rapid product changes and disturbances as a result. However, the level of customization is controlled in a highly regulated field like medical device manufacturing (so mass customization is not discussed in detail in this literature review).  
One of the significant advantages of smart manufacturing is the level of transparency it introduces to the process. The end-to-end manufacturing can be visualized in real-time by leveraging all the data available. This can be achieved by combining IoT and radio frequency identification (RFID) technologies [62]. Furthermore, the transparency can be applied to spare parts or smart tool-level using blockchain or cloud-based services as discussed by [PERSON] et al. and [PERSON] et al., [40; 66]. Moreover, the smart tool workload and durability can be predicted using DNN [67].  
Another advantage of smart manufacturing is discussed by [PERSON] et al., who highlight that the optimization of equipment effectiveness and throughput time is made possible [68]. This can be achieved through various forms of big data analysis that often include task division between cloud, edge, and fog computing. For data privacy and security issues, blockchain can be used for offloading computationally heavy tasks [53; 69]. Because cloud computing introduces issues such as reliability, security, and scalability, blockchain smart contact solutions are often used [52; 70]. For instance, [PERSON] et al. [70] used Ethereum network to solve these problems [70]. Moreover, blockchain allows for tracking products in bottleneck-free way, which is of interest especially in fields such as medical device manufacturing, where there is heavy governmental regulation of the manufacturing process [52].  
The optimization of end-to-end manufacturing process can be realized through supply bottleneck identification and prediction based on machine downtime, as found in the articles by [PERSON] et al. and [PERSON], et al. [71; 72]. Furthermore, [PERSON] et al. [73] studied smart-connected products that are equipped with a sensor system all through manufacturing, which could support scheduling and quality issues and would allow assessment of excess energy consumption [73]. By equipping the product with various sensors, ANN can be used to detect patterns during manufacturing process and to identify the location of the product during manufacturing process [73].  
The increasing demand and globalization has led to the application of lean manufacturing across supply chain to assure quick delivery while at the same time efficiently reducing waste [71]. AI can be widely used for waste and sustainability related trade-off [74]. This includes efficient energy use as the costs of electricity affect production costs directly and can be controlled through manufacturing scheduling, logistical planning, temperature management, and timely fault detection [75]. Moreover, waste can be reduced by efficient real-time production planning to minimize complexity of smart factories [76; 77]. To achieve this high level overview, supply chains can benefit from digital twin assisted planning [61].